Initial commit: Prompt Performance Analytics Dashboard

- Agent 1: Prompt Analyzer (FastAPI + Anthropic Claude) - Scores prompts on 5 dimensions (clarity, token efficiency, goal alignment, structure, vagueness) - Generates optimized rewrites with token savings - Per-project Context Store for pattern learning - Agent 2: Analytics Reporter - SQLite storage with async writes - Human vs Agent mode tracking - Rewrite adoption metrics - MCP Server - analyze_prompt and get_analysis_history tools - Claude Desktop integration via claude_desktop_config.json - Dashboard UI - Live KPIs, quality trend chart, common mistakes donut chart - Interaction feed with per-project filtering - CLI test script (test_agent.py) for direct API testing
2026-02-22 16:56:52 +05:30 · 2026-02-22 16:56:52 +05:30 · 704cccf729
commit 704cccf729
24 changed files with 4160 additions and 0 deletions
--- a/.env.example
+++ b/.env.example
@ -0,0 +1,4 @@
+ANTHROPIC_API_KEY=sk-ant-your-key-here
+ANTHROPIC_MODEL=claude-sonnet-4-20250514
+LLM_MAX_TOKENS=4096
+LLM_TEMPERATURE=0.3
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,11 @@
+__pycache__/
+*.pyc
+.env
+context_store/
+*.db
+.venv/
+venv/
+node_modules/
+.DS_Store
+*.pptx
+make_presentation.py
--- a/PROJECT_BRIEF.docx
+++ b/PROJECT_BRIEF.docx
--- a/README.md
+++ b/README.md
@ -0,0 +1,70 @@
+# Prompt Performance Analytics
+
+A two-agent system for analyzing, scoring, and improving AI prompts — for both humans and enterprise multi-agent systems.
+
+## Quick Start
+
+```bash
+# 1. Install dependencies
+pip install -r requirements.txt
+
+# 2. Set up Anthropic API key
+cp .env.example .env
+# Edit .env with your Anthropic API key
+
+# 3. Run the server
+uvicorn backend.main:app --reload --port 8000
+
+# 4. Open in browser
+# Analyzer:  http://localhost:8000/
+# Dashboard: http://localhost:8000/dashboard-ui
+```
+
+## Architecture
+
+```
+Agent 1: Prompt Analyzer ──→ Agent 2: Analytics Reporter ──→ Dashboard
+    ↑                              ↑
+    ├── REST API (humans)          └── SQLite DB
+    └── MCP Server (agents)
+```
+
+### Agent 1: Prompt Analyzer
+Scores prompts on 5 dimensions (clarity, token efficiency, goal alignment, structure, vagueness), identifies mistakes, and generates optimized rewrites. Uses Anthropic Claude via the official SDK.
+
+### Agent 2: Analytics Reporter
+Aggregates all analyses into trends, mistake frequencies, and agent rankings, then serves data to the dashboard.
+
+### Context Store
+Per-project isolated memory. Each project's history, patterns, and agent profiles are stored separately — no cross-contamination.
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/analyze` | Analyze a prompt |
+| POST | `/rewrite-choice` | Record rewrite acceptance |
+| GET | `/dashboard/overview` | KPI overview |
+| GET | `/dashboard/interactions` | Paginated interaction feed |
+| GET | `/dashboard/trends?days=N&hours=N` | Quality score trends |
+| GET | `/dashboard/mistakes` | Common mistake types |
+| GET | `/dashboard/agents` | Agent leaderboard |
+
+## MCP Server (for agent-to-agent)
+
+```bash
+python -m mcp_server.server
+```
+
+Tools exposed:
+- `analyze_prompt` — analyze a prompt with optional project context
+- `get_analysis_history` — retrieve past analyses
+
+## Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `ANTHROPIC_API_KEY` | Anthropic API key | — |
+| `ANTHROPIC_MODEL` | Claude model to use | claude-sonnet-4-20250514 |
+| `LLM_MAX_TOKENS` | Max output tokens | 4096 |
+| `LLM_TEMPERATURE` | Generation temperature | 0.3 |
--- a/analytics_reporter/init.py
+++ b/analytics_reporter/init.py
@ -0,0 +1,4 @@
+"""Analytics Reporter package."""
+from analytics_reporter.reporter import AnalyticsReporter
+
+__all__ = ["AnalyticsReporter"]
--- a/analytics_reporter/db.py
+++ b/analytics_reporter/db.py
@ -0,0 +1,288 @@
+"""SQLite database for storing analysis results and aggregations."""
+
+import aiosqlite
+import json
+import logging
+from typing import Optional
+
+from prompt_analyzer.config import ANALYTICS_DB_PATH
+
+logger = logging.getLogger(__name__)
+
+DB_PATH = ANALYTICS_DB_PATH
+
+CREATE_TABLE_SQL = """
+CREATE TABLE IF NOT EXISTS analyses (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    timestamp TEXT NOT NULL,
+    mode TEXT NOT NULL DEFAULT 'human',
+    source_agent TEXT,
+    target_agent TEXT,
+    project_id TEXT,
+    original_prompt TEXT NOT NULL,
+    rewritten_prompt TEXT,
+    overall_score INTEGER NOT NULL DEFAULT 0,
+    clarity INTEGER NOT NULL DEFAULT 0,
+    token_efficiency INTEGER NOT NULL DEFAULT 0,
+    goal_alignment INTEGER NOT NULL DEFAULT 0,
+    structure INTEGER NOT NULL DEFAULT 0,
+    vagueness_index INTEGER NOT NULL DEFAULT 0,
+    mistake_count INTEGER NOT NULL DEFAULT 0,
+    mistakes_json TEXT,
+    original_tokens INTEGER NOT NULL DEFAULT 0,
+    rewritten_tokens INTEGER NOT NULL DEFAULT 0,
+    token_savings_percent REAL NOT NULL DEFAULT 0.0,
+    rewrite_used INTEGER,
+    full_result_json TEXT
+);
+"""
+
+CREATE_INDEX_SQL = [
+    "CREATE INDEX IF NOT EXISTS idx_timestamp ON analyses(timestamp);",
+    "CREATE INDEX IF NOT EXISTS idx_project ON analyses(project_id);",
+    "CREATE INDEX IF NOT EXISTS idx_source ON analyses(source_agent);",
+]
+
+
+async def init_db():
+    """Initialize the database and create tables."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        await db.execute(CREATE_TABLE_SQL)
+        for idx_sql in CREATE_INDEX_SQL:
+            await db.execute(idx_sql)
+        await db.commit()
+    logger.info("Database initialized at %s", DB_PATH)
+
+
+async def store_analysis(result_dict: dict) -> int:
+    """Store an analysis result and return its ID."""
+    scores = result_dict.get("scores", {})
+    meta = result_dict.get("metadata", {})
+    tc = result_dict.get("token_comparison", {})
+
+    def _get_score(dim: str) -> int:
+        val = scores.get(dim, {})
+        if isinstance(val, dict):
+            return val.get("score", 0)
+        return 0
+
+    async with aiosqlite.connect(DB_PATH) as db:
+        cursor = await db.execute(
+            """INSERT INTO analyses (
+                timestamp, mode, source_agent, target_agent, project_id,
+                original_prompt, rewritten_prompt,
+                overall_score, clarity, token_efficiency, goal_alignment,
+                structure, vagueness_index,
+                mistake_count, mistakes_json,
+                original_tokens, rewritten_tokens, token_savings_percent,
+                full_result_json
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+            (
+                meta.get("timestamp", ""),
+                meta.get("mode", "human"),
+                meta.get("source_agent"),
+                meta.get("target_agent"),
+                meta.get("project_id"),
+                result_dict.get("original_prompt", ""),
+                result_dict.get("rewritten_prompt", ""),
+                result_dict.get("overall_score", 0),
+                _get_score("clarity"),
+                _get_score("token_efficiency"),
+                _get_score("goal_alignment"),
+                _get_score("structure"),
+                _get_score("vagueness_index"),
+                len(result_dict.get("mistakes", [])),
+                json.dumps(result_dict.get("mistakes", []), default=str),
+                tc.get("original_tokens", 0),
+                tc.get("rewritten_tokens", 0),
+                tc.get("savings_percent", 0.0),
+                json.dumps(result_dict, default=str),
+            ),
+        )
+        await db.commit()
+        row_id = cursor.lastrowid
+        logger.info("Stored analysis id=%d", row_id)
+        return row_id
+
+
+async def get_interactions(
+    limit: int = 50,
+    offset: int = 0,
+    project_id: Optional[str] = None,
+) -> list[dict]:
+    """Get paginated interaction rows."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        db.row_factory = aiosqlite.Row
+        if project_id:
+            cursor = await db.execute(
+                "SELECT * FROM analyses WHERE project_id = ? ORDER BY id DESC LIMIT ? OFFSET ?",
+                (project_id, limit, offset),
+            )
+        else:
+            cursor = await db.execute(
+                "SELECT * FROM analyses ORDER BY id DESC LIMIT ? OFFSET ?",
+                (limit, offset),
+            )
+        rows = await cursor.fetchall()
+        return [dict(row) for row in rows]
+
+
+async def get_total_count(project_id: Optional[str] = None) -> int:
+    """Get total interaction count."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        if project_id:
+            cursor = await db.execute(
+                "SELECT COUNT(*) FROM analyses WHERE project_id = ?",
+                (project_id,),
+            )
+        else:
+            cursor = await db.execute("SELECT COUNT(*) FROM analyses")
+        row = await cursor.fetchone()
+        return row[0] if row else 0
+
+
+async def get_overview_stats() -> dict:
+    """Get aggregate stats for the dashboard overview."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        cursor = await db.execute("""
+            SELECT
+                COUNT(*) as total,
+                SUM(CASE WHEN mode = 'human' THEN 1 ELSE 0 END) as human_count,
+                SUM(CASE WHEN mode = 'agent' THEN 1 ELSE 0 END) as agent_count,
+                AVG(overall_score) as avg_score,
+                AVG(token_savings_percent) as avg_savings,
+                SUM(mistake_count) as total_mistakes,
+                SUM(CASE WHEN rewrite_used = 1 THEN 1 ELSE 0 END) as rewrites_used,
+                SUM(CASE WHEN rewrite_used IS NOT NULL THEN 1 ELSE 0 END) as rewrites_decided,
+                SUM(original_tokens) as total_tokens,
+                AVG(original_tokens) as avg_tokens
+            FROM analyses
+        """)
+        row = await cursor.fetchone()
+        if not row or row[0] == 0:
+            return {
+                "total_interactions": 0,
+                "human_count": 0,
+                "agent_count": 0,
+                "avg_overall_score": 0,
+                "avg_token_savings": 0,
+                "rewrite_acceptance_rate": 0,
+                "total_mistakes_found": 0,
+                "total_tokens": 0,
+                "avg_tokens_per_prompt": 0,
+            }
+        return {
+            "total_interactions": row[0],
+            "human_count": row[1] or 0,
+            "agent_count": row[2] or 0,
+            "avg_overall_score": round(row[3] or 0, 1),
+            "avg_token_savings": round(row[4] or 0, 1),
+            "rewrite_acceptance_rate": round(
+                (row[5] / row[6] * 100) if row[6] and row[6] > 0 else 0, 1
+            ),
+            "total_mistakes_found": row[7] or 0,
+            "total_tokens": row[8] or 0,
+            "avg_tokens_per_prompt": round(row[9] or 0, 1),
+        }
+
+
+async def get_trends(hours: int = None, days: int = 30) -> list[dict]:
+    """Get score trends over time. If hours is set, group by hour; otherwise by day."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        if hours is not None:
+            # Group by hour for short time ranges
+            cursor = await db.execute(
+                """
+                SELECT
+                    strftime('%Y-%m-%d %H:00', timestamp) as period,
+                    AVG(overall_score) as avg_score,
+                    COUNT(*) as count
+                FROM analyses
+                WHERE timestamp >= datetime('now', ?)
+                GROUP BY strftime('%Y-%m-%d %H:00', timestamp)
+                ORDER BY period ASC
+                """,
+                (f"-{hours} hours",),
+            )
+        else:
+            # Group by day for longer ranges
+            cursor = await db.execute(
+                """
+                SELECT
+                    DATE(timestamp) as period,
+                    AVG(overall_score) as avg_score,
+                    COUNT(*) as count
+                FROM analyses
+                WHERE timestamp >= datetime('now', ?)
+                GROUP BY DATE(timestamp)
+                ORDER BY period ASC
+                """,
+                (f"-{days} days",),
+            )
+        rows = await cursor.fetchall()
+        return [
+            {"date": row[0], "avg_score": round(row[1], 1), "count": row[2]}
+            for row in rows
+        ]
+
+
+async def get_mistake_frequencies(limit: int = 10) -> list[dict]:
+    """Get the most common mistake types."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        cursor = await db.execute("SELECT mistakes_json FROM analyses WHERE mistakes_json IS NOT NULL")
+        rows = await cursor.fetchall()
+
+    counts: dict[str, int] = {}
+    for row in rows:
+        try:
+            mistakes = json.loads(row[0])
+            for m in mistakes:
+                mt = m.get("type", "unknown")
+                counts[mt] = counts.get(mt, 0) + 1
+        except (json.JSONDecodeError, TypeError):
+            continue
+
+    total = sum(counts.values()) or 1
+    sorted_counts = sorted(counts.items(), key=lambda x: -x[1])[:limit]
+    return [
+        {"type": k, "count": v, "percentage": round(v / total * 100, 1)}
+        for k, v in sorted_counts
+    ]
+
+
+async def get_agent_leaderboard() -> list[dict]:
+    """Get per-agent statistics."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        cursor = await db.execute("""
+            SELECT
+                source_agent,
+                COUNT(*) as total_prompts,
+                AVG(overall_score) as avg_score
+            FROM analyses
+            WHERE source_agent IS NOT NULL
+            GROUP BY source_agent
+            ORDER BY avg_score DESC
+        """)
+        rows = await cursor.fetchall()
+
+    results = []
+    for row in rows:
+        results.append({
+            "agent_id": row[0],
+            "total_prompts": row[1],
+            "avg_score": round(row[2], 1),
+            "weakest_dimension": None,
+            "most_common_mistake": None,
+            "improvement_trend": "—",
+        })
+    return results
+
+
+async def mark_rewrite_used(analysis_id: int, used: bool) -> None:
+    """Mark whether the user chose the rewritten prompt."""
+    async with aiosqlite.connect(DB_PATH) as db:
+        await db.execute(
+            "UPDATE analyses SET rewrite_used = ? WHERE id = ?",
+            (1 if used else 0, analysis_id),
+        )
+        await db.commit()
--- a/analytics_reporter/models.py
+++ b/analytics_reporter/models.py
@ -0,0 +1,58 @@
+"""Pydantic models for dashboard data."""
+
+from __future__ import annotations
+from pydantic import BaseModel, Field
+
+
+class DashboardOverview(BaseModel):
+    """Top-level KPI data for the dashboard."""
+    total_interactions: int = 0
+    human_count: int = 0
+    agent_count: int = 0
+    avg_overall_score: float = 0.0
+    avg_token_savings: float = 0.0
+    rewrite_acceptance_rate: float = 0.0
+    total_mistakes_found: int = 0
+
+
+class TrendPoint(BaseModel):
+    """A single point in a time-series trend."""
+    date: str
+    avg_score: float
+    count: int
+
+
+class MistakeFrequency(BaseModel):
+    """How often a mistake type appears."""
+    type: str
+    count: int
+    percentage: float
+
+
+class AgentStats(BaseModel):
+    """Stats for a single agent across all its interactions."""
+    agent_id: str
+    total_prompts: int = 0
+    avg_score: float = 0.0
+    weakest_dimension: str | None = None
+    most_common_mistake: str | None = None
+    improvement_trend: str = "—"  # ↑ ↓ —
+
+
+class InteractionRow(BaseModel):
+    """A single row in the interaction feed."""
+    id: int
+    timestamp: str
+    source: str  # "human" or agent name
+    target: str | None = None
+    project_id: str | None = None
+    prompt_preview: str
+    overall_score: int
+    clarity: int
+    token_efficiency: int
+    goal_alignment: int
+    structure: int
+    vagueness_index: int
+    mistake_count: int
+    token_savings: float
+    rewrite_used: bool | None = None
--- a/analytics_reporter/reporter.py
+++ b/analytics_reporter/reporter.py
@ -0,0 +1,78 @@
+"""
+Agent 2: Analytics Reporter
+
+Receives analysis results from Agent 1 (Prompt Analyzer),
+aggregates them, and stores them for the dashboard.
+"""
+
+import logging
+from prompt_analyzer.models import AnalysisResult
+from analytics_reporter import db
+
+logger = logging.getLogger(__name__)
+
+
+class AnalyticsReporter:
+    """
+    Collects analysis results, stores them in the database,
+    and provides aggregated data for the dashboard.
+    """
+
+    async def initialize(self) -> None:
+        """Initialize the database."""
+        await db.init_db()
+        logger.info("AnalyticsReporter initialized")
+
+    async def report(self, result: AnalysisResult) -> int:
+        """
+        Process and store an analysis result.
+
+        Args:
+            result: The AnalysisResult from the Prompt Analyzer
+
+        Returns:
+            The database ID of the stored analysis
+        """
+        result_dict = result.model_dump(mode="json")
+        analysis_id = await db.store_analysis(result_dict)
+        logger.info(
+            "Reported analysis id=%d score=%d project=%s",
+            analysis_id,
+            result.overall_score,
+            result.metadata.project_id,
+        )
+        return analysis_id
+
+    async def get_overview(self) -> dict:
+        """Get dashboard overview KPIs."""
+        return await db.get_overview_stats()
+
+    async def get_interactions(
+        self, limit: int = 50, offset: int = 0, project_id: str = None
+    ) -> dict:
+        """Get paginated interaction feed."""
+        rows = await db.get_interactions(limit, offset, project_id)
+        total = await db.get_total_count(project_id)
+        return {
+            "interactions": rows,
+            "total": total,
+            "limit": limit,
+            "offset": offset,
+        }
+
+    async def get_trends(self, days: int = 30, hours: int = None) -> list[dict]:
+        """Get score trends over time."""
+        return await db.get_trends(hours=hours, days=days)
+
+    async def get_mistake_frequencies(self, limit: int = 10) -> list[dict]:
+        """Get most common mistake types."""
+        return await db.get_mistake_frequencies(limit)
+
+    async def get_agent_leaderboard(self) -> list[dict]:
+        """Get per-agent statistics."""
+        return await db.get_agent_leaderboard()
+
+    async def mark_rewrite_choice(self, analysis_id: int, used: bool) -> None:
+        """Record whether the user chose the rewritten prompt."""
+        await db.mark_rewrite_used(analysis_id, used)
+        logger.info("Rewrite choice recorded: id=%d used=%s", analysis_id, used)
--- a/backend/main.py
+++ b/backend/main.py
@ -0,0 +1,169 @@
+"""
+FastAPI backend — REST API for the Prompt Analyzer and Dashboard.
+
+Serves:
+- POST /analyze        → Human prompt analysis
+- POST /rewrite-choice → Record rewrite acceptance
+- GET  /dashboard/*    → Dashboard data endpoints
+- GET  /health         → Health check
+"""
+
+import logging
+from contextlib import asynccontextmanager
+from typing import Optional
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+
+from prompt_analyzer import PromptAnalyzer
+from prompt_analyzer.models import AnalyzeRequest
+from analytics_reporter.reporter import AnalyticsReporter
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s")
+logger = logging.getLogger(__name__)
+
+# Shared instances
+analyzer = PromptAnalyzer()
+reporter = AnalyticsReporter()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Initialize services on startup."""
+    await reporter.initialize()
+    logger.info("Backend ready")
+    yield
+    logger.info("Backend shutting down")
+
+
+app = FastAPI(
+    title="Prompt Performance Analytics",
+    version="0.1.0",
+    lifespan=lifespan,
+)
+
+# CORS — allow frontend to call from any origin in dev
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ── Analysis Endpoints ─────────────────────────────────────────
+
+
+@app.post("/analyze")
+async def analyze_prompt(request: AnalyzeRequest):
+    """
+    Analyze a prompt and return quality scores, mistakes, and rewrite.
+    This is the main endpoint used by both the Web UI and external clients.
+    """
+    try:
+        result = await analyzer.analyze(
+            prompt=request.prompt,
+            context=request.context,
+            project_id=request.project_id,
+            source_agent=request.source_agent,
+            target_agent=request.target_agent,
+        )
+
+        # Agent 2: store the result for dashboard
+        analysis_id = await reporter.report(result)
+
+        # Return result with the DB id so frontend can track rewrite choice
+        response = result.model_dump(mode="json")
+        response["analysis_id"] = analysis_id
+        return response
+
+    except Exception as e:
+        logger.error("Analysis failed: %s", str(e), exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+
+class RewriteChoiceRequest(BaseModel):
+    analysis_id: int
+    used_rewrite: bool
+
+
+@app.post("/rewrite-choice")
+async def record_rewrite_choice(request: RewriteChoiceRequest):
+    """Record whether the user chose the rewritten prompt."""
+    try:
+        await reporter.mark_rewrite_choice(request.analysis_id, request.used_rewrite)
+        return {"status": "ok"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ── Dashboard Endpoints ────────────────────────────────────────
+
+
+@app.get("/dashboard/overview")
+async def dashboard_overview():
+    """Get dashboard KPI overview."""
+    return await reporter.get_overview()
+
+
+@app.get("/dashboard/interactions")
+async def dashboard_interactions(
+    limit: int = Query(default=50, ge=1, le=200),
+    offset: int = Query(default=0, ge=0),
+    project_id: Optional[str] = Query(default=None),
+):
+    """Get paginated interaction feed."""
+    return await reporter.get_interactions(limit, offset, project_id)
+
+
+@app.get("/dashboard/trends")
+async def dashboard_trends(
+    days: int = Query(default=30, ge=1, le=365),
+    hours: Optional[int] = Query(default=None, ge=1, le=720),
+):
+    """Get quality score trends over time. Use hours for short ranges."""
+    return await reporter.get_trends(days=days, hours=hours)
+
+
+@app.get("/dashboard/mistakes")
+async def dashboard_mistakes(limit: int = Query(default=10, ge=1, le=50)):
+    """Get most common mistake types."""
+    return await reporter.get_mistake_frequencies(limit)
+
+
+@app.get("/dashboard/agents")
+async def dashboard_agents():
+    """Get agent leaderboard."""
+    return await reporter.get_agent_leaderboard()
+
+
+# ── Static files & Health ──────────────────────────────────────
+
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "version": "0.1.0"}
+
+
+# Serve frontend static files
+import os
+frontend_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "frontend")
+dashboard_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "dashboard")
+
+if os.path.isdir(frontend_dir):
+    @app.get("/", response_class=FileResponse)
+    async def serve_frontend():
+        return FileResponse(os.path.join(frontend_dir, "index.html"))
+
+    app.mount("/static", StaticFiles(directory=frontend_dir), name="frontend")
+
+if os.path.isdir(dashboard_dir):
+    @app.get("/dashboard-ui", response_class=FileResponse)
+    async def serve_dashboard():
+        return FileResponse(os.path.join(dashboard_dir, "index.html"))
+
+    app.mount("/dashboard-static", StaticFiles(directory=dashboard_dir), name="dashboard")
--- a/dashboard/app.js
+++ b/dashboard/app.js
@ -0,0 +1,438 @@
+/**
+ * Dashboard — Frontend Logic
+ * Fetches data from /dashboard/* API endpoints and renders charts + tables.
+ */
+
+const API = window.location.origin;
+
+let currentPage = 0;
+const PAGE_SIZE = 20;
+let allInteractions = [];
+let trendChart = null;
+let mistakesChart = null;
+
+// ── Initialize ────────────────────────────────────────────────
+
+document.addEventListener('DOMContentLoaded', () => {
+    refreshAll();
+});
+
+async function refreshAll() {
+    await Promise.all([
+        loadOverview(),
+        loadTrends(),
+        loadMistakes(),
+        loadInteractions(),
+        loadAgents(),
+    ]);
+}
+
+// ── Overview KPIs ─────────────────────────────────────────────
+
+async function loadOverview() {
+    try {
+        const res = await fetch(`${API}/dashboard/overview`);
+        const data = await res.json();
+
+        document.getElementById('kpi-total').textContent = data.total_interactions.toLocaleString();
+        document.getElementById('kpi-avg-score').textContent = `${data.avg_overall_score}%`;
+        document.getElementById('kpi-savings').textContent = `${data.avg_token_savings}%`;
+        document.getElementById('kpi-rewrite').textContent = `${data.rewrite_acceptance_rate}%`;
+        document.getElementById('kpi-split').textContent = `${data.human_count}H / ${data.agent_count}A`;
+
+        document.getElementById('kpi-total-tokens').textContent = data.total_tokens.toLocaleString();
+        document.getElementById('kpi-avg-tokens').textContent = Math.round(data.avg_tokens_per_prompt).toLocaleString();
+    } catch (e) {
+        console.error('Failed to load overview:', e);
+    }
+}
+
+// ── Trends Chart ──────────────────────────────────────────────
+
+async function loadTrends(params = {}) {
+    try {
+        const url = new URL(`${API}/dashboard/trends`);
+        if (params.hours) {
+            url.searchParams.set('hours', params.hours);
+        } else {
+            url.searchParams.set('days', params.days || 30);
+        }
+        const res = await fetch(url);
+        const data = await res.json();
+
+        if (!data || data.length === 0) {
+            document.getElementById('trend-chart').style.display = 'none';
+            document.getElementById('trend-empty').classList.remove('hidden');
+            return;
+        }
+
+        document.getElementById('trend-chart').style.display = 'block';
+        document.getElementById('trend-empty').classList.add('hidden');
+
+        const ctx = document.getElementById('trend-chart').getContext('2d');
+
+        if (trendChart) trendChart.destroy();
+
+        // Format labels based on whether data is hourly or daily
+        const labels = data.map(d => {
+            if (d.date && d.date.includes(':')) {
+                // Hourly format: show time
+                const parts = d.date.split(' ');
+                return parts.length > 1 ? parts[1] : d.date;
+            }
+            return d.date;
+        });
+
+        // Calculate max interactions for axis scaling
+        const maxCount = Math.max(...data.map(d => d.count), 1);
+
+        trendChart = new Chart(ctx, {
+            type: 'line',
+            data: {
+                labels: labels,
+                datasets: [{
+                    label: 'Avg Quality Score',
+                    data: data.map(d => d.avg_score),
+                    borderColor: '#3b82f6',
+                    backgroundColor: 'rgba(59, 130, 246, 0.1)',
+                    fill: true,
+                    tension: 0.4,
+                    pointRadius: 4,
+                    pointBackgroundColor: '#3b82f6',
+                }, {
+                    label: 'Interactions',
+                    data: data.map(d => d.count),
+                    borderColor: '#8b5cf6',
+                    backgroundColor: 'rgba(139, 92, 246, 0.1)',
+                    fill: false,
+                    tension: 0.4,
+                    pointRadius: 3,
+                    pointBackgroundColor: '#8b5cf6',
+                    yAxisID: 'y1',
+                }],
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                interaction: { mode: 'index', intersect: false },
+                plugins: {
+                    legend: {
+                        labels: { color: '#8899b4', font: { family: 'Inter', size: 12 } }
+                    },
+                },
+                scales: {
+                    x: {
+                        ticks: { color: '#5a6a85', font: { size: 11 }, maxRotation: 45 },
+                        grid: { color: 'rgba(42, 52, 82, 0.5)' },
+                    },
+                    y: {
+                        min: 0, max: 100,
+                        ticks: { color: '#5a6a85', font: { size: 11 } },
+                        grid: { color: 'rgba(42, 52, 82, 0.5)' },
+                    },
+                    y1: {
+                        position: 'right',
+                        min: 0,
+                        suggestedMax: maxCount + 1,
+                        ticks: {
+                            color: '#5a6a85',
+                            font: { size: 11 },
+                            stepSize: 1,
+                            precision: 0,
+                        },
+                        grid: { display: false },
+                    },
+                },
+            },
+        });
+    } catch (e) {
+        console.error('Failed to load trends:', e);
+    }
+}
+
+function setTrendFilter(btn) {
+    // Update active state
+    document.querySelectorAll('.filter-btn').forEach(b => b.classList.remove('active'));
+    btn.classList.add('active');
+
+    // Build params
+    const params = {};
+    if (btn.dataset.hours) {
+        params.hours = parseInt(btn.dataset.hours);
+    } else if (btn.dataset.days) {
+        params.days = parseInt(btn.dataset.days);
+    }
+
+    loadTrends(params);
+}
+
+// ── Mistakes Chart ────────────────────────────────────────────
+
+async function loadMistakes() {
+    try {
+        const res = await fetch(`${API}/dashboard/mistakes?limit=6`);
+        const data = await res.json();
+
+        if (!data || data.length === 0) {
+            document.getElementById('mistakes-chart').style.display = 'none';
+            document.getElementById('mistakes-empty').classList.remove('hidden');
+            return;
+        }
+
+        document.getElementById('mistakes-chart').style.display = 'block';
+        document.getElementById('mistakes-empty').classList.add('hidden');
+
+        const ctx = document.getElementById('mistakes-chart').getContext('2d');
+
+        if (mistakesChart) mistakesChart.destroy();
+
+        const colors = ['#ef4444', '#f59e0b', '#3b82f6', '#8b5cf6', '#06b6d4', '#10b981'];
+
+        mistakesChart = new Chart(ctx, {
+            type: 'doughnut',
+            data: {
+                labels: data.map(d => formatMistakeType(d.type)),
+                datasets: [{
+                    data: data.map(d => d.count),
+                    backgroundColor: colors.slice(0, data.length),
+                    borderColor: '#1a2235',
+                    borderWidth: 3,
+                }],
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    legend: {
+                        position: 'right',
+                        labels: { color: '#8899b4', font: { family: 'Inter', size: 11 }, padding: 12 },
+                    },
+                },
+            },
+        });
+    } catch (e) {
+        console.error('Failed to load mistakes:', e);
+    }
+}
+
+// ── Interactions Feed ─────────────────────────────────────────
+
+async function loadInteractions() {
+    try {
+        const projectFilter = document.getElementById('feed-filter').value.trim() || null;
+        const url = new URL(`${API}/dashboard/interactions`);
+        url.searchParams.set('limit', PAGE_SIZE);
+        url.searchParams.set('offset', currentPage * PAGE_SIZE);
+        if (projectFilter) url.searchParams.set('project_id', projectFilter);
+
+        const res = await fetch(url);
+        const data = await res.json();
+
+        allInteractions = data.interactions;
+        const total = data.total;
+
+        renderFeed(allInteractions);
+
+        // Pagination
+        const totalPages = Math.ceil(total / PAGE_SIZE) || 1;
+        document.getElementById('page-info').textContent = `Page ${currentPage + 1} of ${totalPages}`;
+        document.getElementById('prev-btn').disabled = currentPage === 0;
+        document.getElementById('next-btn').disabled = (currentPage + 1) * PAGE_SIZE >= total;
+
+    } catch (e) {
+        console.error('Failed to load interactions:', e);
+    }
+}
+
+function renderFeed(rows) {
+    const tbody = document.getElementById('feed-body');
+
+    if (!rows || rows.length === 0) {
+        tbody.innerHTML = '<tr><td colspan="9" class="feed-empty">No interactions yet — go analyze some prompts!</td></tr>';
+        return;
+    }
+
+    tbody.innerHTML = rows.map(row => {
+        const score = row.overall_score;
+        const scoreClass = score >= 85 ? 'excellent' : score >= 65 ? 'good' : score >= 40 ? 'fair' : 'poor';
+        const source = row.source_agent
+            ? `<span class="source-badge">🤖 ${escapeHtml(row.source_agent)}</span>`
+            : '<span class="source-badge">👤 Human</span>';
+        const preview = escapeHtml((row.original_prompt || '').substring(0, 60)) + (row.original_prompt && row.original_prompt.length > 60 ? '...' : '');
+        const rewrite = row.rewrite_used === 1 ? '✅' : row.rewrite_used === 0 ? '❌' : '—';
+        const time = formatTime(row.timestamp);
+
+        return `
+            <tr>
+                <td>${time}</td>
+                <td>${source}</td>
+                <td>${escapeHtml(row.project_id || '—')}</td>
+                <td style="max-width:200px;overflow:hidden;text-overflow:ellipsis;font-family:var(--font-mono);font-size:12px;">${preview}</td>
+                <td><span class="score-badge ${scoreClass}">${score}</span></td>
+                <td>${row.mistake_count}</td>
+                <td>${row.token_savings_percent}%</td>
+                <td>${rewrite}</td>
+                <td><button class="view-btn" onclick='viewDetail(${row.id})'>View</button></td>
+            </tr>
+        `;
+    }).join('');
+}
+
+function filterFeed() {
+    currentPage = 0;
+    loadInteractions();
+}
+
+function prevPage() {
+    if (currentPage > 0) { currentPage--; loadInteractions(); }
+}
+
+function nextPage() {
+    currentPage++;
+    loadInteractions();
+}
+
+// ── Agent Leaderboard ─────────────────────────────────────────
+
+async function loadAgents() {
+    try {
+        const res = await fetch(`${API}/dashboard/agents`);
+        const data = await res.json();
+
+        const tbody = document.getElementById('agent-body');
+
+        if (!data || data.length === 0) {
+            tbody.innerHTML = '<tr><td colspan="5" class="feed-empty">No agent data yet</td></tr>';
+            return;
+        }
+
+        tbody.innerHTML = data.map((agent, i) => {
+            const scoreClass = agent.avg_score >= 85 ? 'excellent' : agent.avg_score >= 65 ? 'good' : agent.avg_score >= 40 ? 'fair' : 'poor';
+            return `
+                <tr>
+                    <td style="font-weight:700;">#${i + 1}</td>
+                    <td>🤖 ${escapeHtml(agent.agent_id)}</td>
+                    <td>${agent.total_prompts}</td>
+                    <td><span class="score-badge ${scoreClass}">${agent.avg_score}</span></td>
+                    <td>${agent.improvement_trend}</td>
+                </tr>
+            `;
+        }).join('');
+    } catch (e) {
+        console.error('Failed to load agents:', e);
+    }
+}
+
+// ── Detail Modal ──────────────────────────────────────────────
+
+async function viewDetail(id) {
+    // Find the row in current data
+    const row = allInteractions.find(r => r.id === id);
+    if (!row) return;
+
+    const fullResult = row.full_result_json ? JSON.parse(row.full_result_json) : null;
+
+    const modal = document.getElementById('detail-modal');
+    const body = document.getElementById('modal-body');
+
+    let scoresHtml = '';
+    if (fullResult && fullResult.scores) {
+        const dims = ['clarity', 'token_efficiency', 'goal_alignment', 'structure', 'vagueness_index'];
+        scoresHtml = `<div class="modal-scores">${dims.map(d => {
+            const s = fullResult.scores[d];
+            const color = getScoreColor(s.score);
+            return `<div class="modal-score-item">
+                <span class="score-val" style="color:${color}">${s.score}</span>
+                <span class="score-name">${formatDimension(d)}</span>
+            </div>`;
+        }).join('')}</div>`;
+    }
+
+    let mistakesHtml = '';
+    if (fullResult && fullResult.mistakes && fullResult.mistakes.length > 0) {
+        mistakesHtml = `
+            <div class="modal-prompt-section">
+                <div class="modal-prompt-label">Mistakes (${fullResult.mistakes.length})</div>
+                ${fullResult.mistakes.map(m => `
+                    <div style="padding:8px 12px;background:var(--bg-input);border-radius:var(--radius-sm);margin-bottom:8px;border-left:3px solid var(--accent-red);">
+                        <div style="font-size:11px;font-weight:600;color:var(--accent-red);text-transform:uppercase;">${formatMistakeType(m.type)}</div>
+                        ${m.text ? `<div style="font-family:var(--font-mono);font-size:12px;margin:4px 0;">"${escapeHtml(m.text)}"</div>` : ''}
+                        <div style="font-size:12px;color:var(--accent-green);">💡 ${escapeHtml(m.suggestion)}</div>
+                    </div>
+                `).join('')}
+            </div>
+        `;
+    }
+
+    body.innerHTML = `
+        <div style="margin-bottom:16px;">
+            <span class="score-badge ${row.overall_score >= 85 ? 'excellent' : row.overall_score >= 65 ? 'good' : row.overall_score >= 40 ? 'fair' : 'poor'}" style="font-size:16px;padding:6px 16px;">
+                Overall: ${row.overall_score}
+            </span>
+            <span style="margin-left:12px;color:var(--text-muted);font-size:13px;">
+                ${formatTime(row.timestamp)} · ${row.source_agent ? '🤖 ' + row.source_agent : '👤 Human'}
+                ${row.project_id ? ' · 📁 ' + row.project_id : ''}
+            </span>
+        </div>
+
+        ${scoresHtml}
+
+        <div class="modal-prompt-section">
+            <div class="modal-prompt-label">Original Prompt (${row.original_tokens} tokens)</div>
+            <div class="modal-prompt-text">${escapeHtml(row.original_prompt)}</div>
+        </div>
+
+        ${row.rewritten_prompt ? `
+        <div class="modal-prompt-section">
+            <div class="modal-prompt-label">Optimized Rewrite (${row.rewritten_tokens} tokens · ${row.token_savings_percent}% saved)</div>
+            <div class="modal-prompt-text" style="border-color:var(--accent-green);">${escapeHtml(row.rewritten_prompt)}</div>
+        </div>` : ''}
+
+        ${mistakesHtml}
+    `;
+
+    modal.classList.remove('hidden');
+}
+
+function closeModal(event) {
+    if (event.target === event.currentTarget) {
+        document.getElementById('detail-modal').classList.add('hidden');
+    }
+}
+
+function closeDetail() {
+    document.getElementById('detail-modal').classList.add('hidden');
+}
+
+// ── Helpers ───────────────────────────────────────────────────
+
+function formatTime(ts) {
+    if (!ts) return '—';
+    try {
+        const d = new Date(ts);
+        return d.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }) +
+            ' ' + d.toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit' });
+    } catch { return ts; }
+}
+
+function formatMistakeType(type) {
+    return (type || 'unknown').replace(/_/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
+}
+
+function formatDimension(dim) {
+    return dim.replace(/_/g, ' ');
+}
+
+function getScoreColor(score) {
+    if (score >= 85) return '#10b981';
+    if (score >= 65) return '#3b82f6';
+    if (score >= 40) return '#f59e0b';
+    return '#ef4444';
+}
+
+function escapeHtml(text) {
+    if (!text) return '';
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+}
--- a/dashboard/index.html
+++ b/dashboard/index.html
@ -0,0 +1,210 @@
+<!DOCTYPE html>
+<html lang="en" class="dark">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Dashboard — Prompt Performance Analytics</title>
+    <meta name="description" content="View analytics on prompt quality across all interactions, agents, and projects.">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:wght,FILL@100..700,0..1&display=swap"
+        rel="stylesheet">
+    <link rel="stylesheet" href="/dashboard-static/styles.css?v=2">
+</head>
+
+<body>
+    <!-- Header -->
+    <header class="header">
+        <div class="header-brand">
+            <div class="brand-icon">
+                <span class="material-symbols-outlined">monitoring</span>
+            </div>
+            <div>
+                <h1 class="brand-title">Analytics Dashboard</h1>
+                <span class="brand-version">Prompt Performance</span>
+            </div>
+        </div>
+        <div class="header-actions">
+            <a href="/" class="btn btn-ghost">
+                <span class="material-symbols-outlined">psychology</span>
+                Analyzer
+            </a>
+            <button class="btn btn-ghost" onclick="refreshAll()">
+                <span class="material-symbols-outlined">refresh</span>
+                Refresh
+            </button>
+        </div>
+    </header>
+
+    <!-- Main Content -->
+    <main class="dash-content">
+        <!-- KPI Cards -->
+        <section class="kpi-grid" id="kpi-section">
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-blue"><span class="material-symbols-outlined">analytics</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-total">—</div>
+                    <div class="kpi-label">Total Interactions</div>
+                </div>
+            </div>
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-green"><span class="material-symbols-outlined">speed</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-avg-score">—</div>
+                    <div class="kpi-label">Avg Quality Score</div>
+                </div>
+            </div>
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-cyan"><span class="material-symbols-outlined">savings</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-savings">—</div>
+                    <div class="kpi-label">Avg Token Savings</div>
+                </div>
+            </div>
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-purple"><span class="material-symbols-outlined">check_circle</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-rewrite">—</div>
+                    <div class="kpi-label">Rewrite Acceptance</div>
+                </div>
+            </div>
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-amber"><span class="material-symbols-outlined">group</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-split">—</div>
+                    <div class="kpi-label">Human vs Agent</div>
+                </div>
+            </div>
+
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-teal"><span class="material-symbols-outlined">token</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-total-tokens">—</div>
+                    <div class="kpi-label">Total Tokens</div>
+                </div>
+            </div>
+            <div class="kpi-card">
+                <div class="kpi-icon kpi-orange"><span class="material-symbols-outlined">avg_pace</span></div>
+                <div class="kpi-data">
+                    <div class="kpi-value" id="kpi-avg-tokens">—</div>
+                    <div class="kpi-label">Avg Tokens / Prompt</div>
+                </div>
+            </div>
+        </section>
+
+        <!-- Charts Row -->
+        <section class="charts-row">
+            <div class="chart-card">
+                <div class="chart-header">
+                    <h3><span class="material-symbols-outlined">show_chart</span> Quality Trend</h3>
+                    <div class="trend-filters" id="trend-filters">
+                        <button class="filter-btn" data-hours="1" onclick="setTrendFilter(this)">1h</button>
+                        <button class="filter-btn" data-hours="5" onclick="setTrendFilter(this)">5h</button>
+                        <button class="filter-btn" data-hours="12" onclick="setTrendFilter(this)">12h</button>
+                        <button class="filter-btn" data-days="1" onclick="setTrendFilter(this)">1d</button>
+                        <button class="filter-btn" data-days="10" onclick="setTrendFilter(this)">10d</button>
+                        <button class="filter-btn active" data-days="30" onclick="setTrendFilter(this)">1mo</button>
+                    </div>
+                </div>
+                <div class="chart-body">
+                    <canvas id="trend-chart" height="220"></canvas>
+                    <div id="trend-empty" class="chart-empty hidden">No trend data yet — analyze some prompts first!
+                    </div>
+                </div>
+            </div>
+            <div class="chart-card">
+                <div class="chart-header">
+                    <h3><span class="material-symbols-outlined">pie_chart</span> Common Mistakes</h3>
+                </div>
+                <div class="chart-body">
+                    <canvas id="mistakes-chart" height="220"></canvas>
+                    <div id="mistakes-empty" class="chart-empty hidden">No mistakes recorded yet!</div>
+                </div>
+            </div>
+        </section>
+
+        <!-- Interaction Feed -->
+        <section class="feed-section">
+            <div class="feed-header">
+                <h2><span class="material-symbols-outlined">list_alt</span> Interaction Feed</h2>
+                <div class="feed-controls">
+                    <input type="text" id="feed-filter" placeholder="Filter by project..." class="feed-search"
+                        oninput="filterFeed()">
+                </div>
+            </div>
+            <div class="feed-table-wrap">
+                <table class="feed-table" id="feed-table">
+                    <thead>
+                        <tr>
+                            <th>Time</th>
+                            <th>Source</th>
+                            <th>Project</th>
+                            <th>Prompt</th>
+                            <th>Score</th>
+                            <th>Mistakes</th>
+                            <th>Savings</th>
+                            <th>Rewrite</th>
+                            <th></th>
+                        </tr>
+                    </thead>
+                    <tbody id="feed-body">
+                        <tr>
+                            <td colspan="9" class="feed-empty">No interactions yet — go analyze some prompts!</td>
+                        </tr>
+                    </tbody>
+                </table>
+            </div>
+            <div class="feed-pagination">
+                <button class="btn btn-outline btn-sm" id="prev-btn" onclick="prevPage()" disabled>← Previous</button>
+                <span id="page-info" class="page-info">Page 1</span>
+                <button class="btn btn-outline btn-sm" id="next-btn" onclick="nextPage()" disabled>Next →</button>
+            </div>
+        </section>
+
+        <!-- Agent Leaderboard -->
+        <section class="leaderboard-section">
+            <div class="feed-header">
+                <h2><span class="material-symbols-outlined">leaderboard</span> Agent Leaderboard</h2>
+            </div>
+            <div class="feed-table-wrap">
+                <table class="feed-table" id="agent-table">
+                    <thead>
+                        <tr>
+                            <th>Rank</th>
+                            <th>Agent</th>
+                            <th>Prompts</th>
+                            <th>Avg Score</th>
+                            <th>Trend</th>
+                        </tr>
+                    </thead>
+                    <tbody id="agent-body">
+                        <tr>
+                            <td colspan="5" class="feed-empty">No agent data yet</td>
+                        </tr>
+                    </tbody>
+                </table>
+            </div>
+        </section>
+
+        <!-- Detail Modal -->
+        <div class="modal-overlay hidden" id="detail-modal" onclick="closeModal(event)">
+            <div class="modal-content" onclick="event.stopPropagation()">
+                <div class="modal-header">
+                    <h3>Analysis Detail</h3>
+                    <button class="btn btn-ghost" onclick="closeDetail()">
+                        <span class="material-symbols-outlined">close</span>
+                    </button>
+                </div>
+                <div class="modal-body" id="modal-body"></div>
+            </div>
+        </div>
+    </main>
+
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4/dist/chart.umd.min.js"></script>
+    <script src="/dashboard-static/app.js?v=2"></script>
+</body>
+
+</html>
--- a/dashboard/styles.css
+++ b/dashboard/styles.css
@ -0,0 +1,634 @@
+/* ── Dashboard Design System ───────────────────────────────── */
+
+:root {
+    --bg-primary: #0a0e1a;
+    --bg-secondary: #111827;
+    --bg-card: #1a2235;
+    --bg-card-hover: #1e2840;
+    --bg-input: #0f1629;
+
+    --border: #2a3452;
+    --border-hover: #3b4a70;
+
+    --text-primary: #f1f5f9;
+    --text-secondary: #8899b4;
+    --text-muted: #5a6a85;
+
+    --accent-blue: #3b82f6;
+    --accent-purple: #8b5cf6;
+    --accent-green: #10b981;
+    --accent-red: #ef4444;
+    --accent-amber: #f59e0b;
+    --accent-cyan: #06b6d4;
+
+    --font-sans: 'Inter', -apple-system, sans-serif;
+    --font-mono: 'JetBrains Mono', monospace;
+
+    --radius-sm: 6px;
+    --radius-md: 10px;
+    --radius-lg: 16px;
+
+    --shadow-card: 0 4px 24px rgba(0, 0, 0, 0.3);
+}
+
+*,
+*::before,
+*::after {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    font-family: var(--font-sans);
+    background: var(--bg-primary);
+    color: var(--text-primary);
+    min-height: 100vh;
+    -webkit-font-smoothing: antialiased;
+    line-height: 1.6;
+}
+
+::-webkit-scrollbar {
+    width: 8px;
+    height: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: var(--bg-primary);
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--border);
+    border-radius: 4px;
+}
+
+/* ── Header ────────────────────────────────────────────────── */
+
+.header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 16px 32px;
+    border-bottom: 1px solid var(--border);
+    background: var(--bg-secondary);
+    position: sticky;
+    top: 0;
+    z-index: 50;
+}
+
+.header-brand {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.brand-icon {
+    width: 40px;
+    height: 40px;
+    border-radius: var(--radius-md);
+    background: linear-gradient(135deg, var(--accent-cyan), var(--accent-blue));
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: white;
+}
+
+.brand-title {
+    font-size: 18px;
+    font-weight: 700;
+}
+
+.brand-version {
+    font-size: 11px;
+    color: var(--text-muted);
+}
+
+.header-actions {
+    display: flex;
+    gap: 8px;
+}
+
+/* ── Buttons ───────────────────────────────────────────────── */
+
+.btn {
+    display: inline-flex;
+    align-items: center;
+    gap: 8px;
+    padding: 10px 20px;
+    font-size: 14px;
+    font-weight: 600;
+    font-family: var(--font-sans);
+    border-radius: var(--radius-md);
+    border: none;
+    cursor: pointer;
+    transition: all 0.2s;
+    text-decoration: none;
+}
+
+.btn .material-symbols-outlined {
+    font-size: 18px;
+}
+
+.btn-ghost {
+    background: transparent;
+    color: var(--text-secondary);
+    padding: 8px 16px;
+}
+
+.btn-ghost:hover {
+    background: var(--bg-card);
+    color: var(--text-primary);
+}
+
+.btn-outline {
+    background: transparent;
+    color: var(--text-secondary);
+    border: 1px solid var(--border);
+}
+
+.btn-outline:hover {
+    border-color: var(--text-secondary);
+    color: var(--text-primary);
+}
+
+.btn-outline:disabled {
+    opacity: 0.4;
+    cursor: not-allowed;
+}
+
+.btn-sm {
+    padding: 6px 14px;
+    font-size: 13px;
+}
+
+/* ── Main ──────────────────────────────────────────────────── */
+
+.dash-content {
+    max-width: 1300px;
+    margin: 0 auto;
+    padding: 28px 24px 64px;
+}
+
+/* ── KPI Cards ─────────────────────────────────────────────── */
+
+.kpi-grid {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: 16px;
+}
+
+.kpi-card {
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    padding: 20px;
+    display: flex;
+    align-items: center;
+    gap: 14px;
+    transition: border-color 0.2s, transform 0.2s;
+    box-shadow: var(--shadow-card);
+}
+
+.kpi-card:hover {
+    border-color: var(--accent-blue);
+    transform: translateY(-2px);
+}
+
+.kpi-icon {
+    width: 44px;
+    height: 44px;
+    border-radius: var(--radius-md);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    flex-shrink: 0;
+}
+
+.kpi-icon .material-symbols-outlined {
+    font-size: 22px;
+    color: white;
+}
+
+.kpi-blue {
+    background: linear-gradient(135deg, #3b82f6, #2563eb);
+}
+
+.kpi-green {
+    background: linear-gradient(135deg, #10b981, #059669);
+}
+
+.kpi-cyan {
+    background: linear-gradient(135deg, #06b6d4, #0891b2);
+}
+
+.kpi-purple {
+    background: linear-gradient(135deg, #8b5cf6, #7c3aed);
+}
+
+.kpi-amber {
+    background: linear-gradient(135deg, #f59e0b, #d97706);
+}
+
+.kpi-red {
+    background: linear-gradient(135deg, #ef4444, #dc2626);
+}
+
+.kpi-teal {
+    background: linear-gradient(135deg, #14b8a6, #0d9488);
+}
+
+.kpi-orange {
+    background: linear-gradient(135deg, #f97316, #ea580c);
+}
+
+.kpi-value {
+    font-size: 22px;
+    font-weight: 800;
+    line-height: 1.2;
+}
+
+.kpi-label {
+    font-size: 12px;
+    color: var(--text-secondary);
+    font-weight: 500;
+}
+
+.kpi-hint {
+    font-size: 10px;
+    color: var(--text-muted);
+    margin-top: 2px;
+    line-height: 1.3;
+}
+
+/* ── Charts ────────────────────────────────────────────────── */
+
+.charts-row {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 16px;
+    margin-top: 24px;
+}
+
+.chart-card {
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    box-shadow: var(--shadow-card);
+    overflow: hidden;
+}
+
+.chart-header {
+    padding: 18px 22px 0;
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+}
+
+.chart-header h3 {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-size: 15px;
+    font-weight: 600;
+}
+
+.chart-header h3 .material-symbols-outlined {
+    font-size: 20px;
+    color: var(--accent-blue);
+}
+
+.trend-filters {
+    display: flex;
+    gap: 4px;
+}
+
+.filter-btn {
+    background: var(--bg-input);
+    border: 1px solid var(--border);
+    color: var(--text-muted);
+    font-family: var(--font-sans);
+    font-size: 11px;
+    font-weight: 600;
+    padding: 4px 10px;
+    border-radius: var(--radius-sm);
+    cursor: pointer;
+    transition: all 0.2s;
+}
+
+.filter-btn:hover {
+    border-color: var(--accent-blue);
+    color: var(--text-primary);
+}
+
+.filter-btn.active {
+    background: var(--accent-blue);
+    border-color: var(--accent-blue);
+    color: white;
+}
+
+.chart-body {
+    padding: 16px 22px 22px;
+}
+
+.chart-empty {
+    text-align: center;
+    color: var(--text-muted);
+    padding: 40px 20px;
+    font-size: 14px;
+}
+
+/* ── Feed / Table ──────────────────────────────────────────── */
+
+.feed-section,
+.leaderboard-section {
+    margin-top: 24px;
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    box-shadow: var(--shadow-card);
+    overflow: hidden;
+}
+
+.feed-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 18px 22px;
+    border-bottom: 1px solid var(--border);
+}
+
+.feed-header h2 {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    font-size: 16px;
+    font-weight: 600;
+}
+
+.feed-header h2 .material-symbols-outlined {
+    font-size: 20px;
+    color: var(--accent-blue);
+}
+
+.feed-search {
+    background: var(--bg-input);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-md);
+    padding: 8px 14px;
+    font-family: var(--font-sans);
+    font-size: 13px;
+    color: var(--text-primary);
+    width: 220px;
+    outline: none;
+    transition: border-color 0.2s;
+}
+
+.feed-search:focus {
+    border-color: var(--accent-blue);
+}
+
+.feed-table-wrap {
+    overflow-x: auto;
+}
+
+.feed-table {
+    width: 100%;
+    border-collapse: collapse;
+    font-size: 13px;
+}
+
+.feed-table th {
+    text-align: left;
+    padding: 12px 16px;
+    font-weight: 600;
+    font-size: 11px;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--text-muted);
+    background: var(--bg-input);
+    border-bottom: 1px solid var(--border);
+    white-space: nowrap;
+}
+
+.feed-table td {
+    padding: 12px 16px;
+    border-bottom: 1px solid rgba(42, 52, 82, 0.5);
+    color: var(--text-secondary);
+    white-space: nowrap;
+}
+
+.feed-table tbody tr {
+    transition: background 0.15s;
+}
+
+.feed-table tbody tr:hover {
+    background: var(--bg-card-hover);
+}
+
+.feed-empty {
+    text-align: center;
+    padding: 40px !important;
+    color: var(--text-muted) !important;
+    white-space: normal !important;
+}
+
+/* Score badge in table */
+.score-badge {
+    display: inline-block;
+    padding: 3px 10px;
+    border-radius: 999px;
+    font-weight: 700;
+    font-size: 12px;
+}
+
+.score-badge.excellent {
+    background: rgba(16, 185, 129, .15);
+    color: var(--accent-green);
+}
+
+.score-badge.good {
+    background: rgba(59, 130, 246, .15);
+    color: var(--accent-blue);
+}
+
+.score-badge.fair {
+    background: rgba(245, 158, 11, .15);
+    color: var(--accent-amber);
+}
+
+.score-badge.poor {
+    background: rgba(239, 68, 68, .15);
+    color: var(--accent-red);
+}
+
+.source-badge {
+    display: inline-flex;
+    align-items: center;
+    gap: 4px;
+    font-size: 12px;
+}
+
+.view-btn {
+    background: transparent;
+    border: 1px solid var(--border);
+    color: var(--accent-blue);
+    font-size: 12px;
+    padding: 4px 10px;
+    border-radius: var(--radius-sm);
+    cursor: pointer;
+    font-family: var(--font-sans);
+    transition: all 0.2s;
+}
+
+.view-btn:hover {
+    background: var(--accent-blue);
+    color: white;
+}
+
+.feed-pagination {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 16px;
+    padding: 16px;
+    border-top: 1px solid var(--border);
+}
+
+.page-info {
+    font-size: 13px;
+    color: var(--text-muted);
+}
+
+/* ── Modal ─────────────────────────────────────────────────── */
+
+.modal-overlay {
+    position: fixed;
+    inset: 0;
+    background: rgba(0, 0, 0, 0.7);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 100;
+    backdrop-filter: blur(4px);
+}
+
+.modal-content {
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    width: 90%;
+    max-width: 800px;
+    max-height: 80vh;
+    overflow-y: auto;
+    box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5);
+}
+
+.modal-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 18px 24px;
+    border-bottom: 1px solid var(--border);
+}
+
+.modal-header h3 {
+    font-size: 16px;
+}
+
+.modal-body {
+    padding: 24px;
+}
+
+.modal-scores {
+    display: grid;
+    grid-template-columns: repeat(5, 1fr);
+    gap: 12px;
+    margin-bottom: 20px;
+}
+
+.modal-score-item {
+    text-align: center;
+    padding: 12px;
+    background: var(--bg-input);
+    border-radius: var(--radius-md);
+}
+
+.modal-score-item .score-val {
+    font-size: 24px;
+    font-weight: 800;
+    display: block;
+}
+
+.modal-score-item .score-name {
+    font-size: 11px;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.modal-prompt-section {
+    margin-top: 16px;
+}
+
+.modal-prompt-label {
+    font-size: 12px;
+    font-weight: 600;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    margin-bottom: 6px;
+}
+
+.modal-prompt-text {
+    background: var(--bg-input);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-md);
+    padding: 14px;
+    font-family: var(--font-mono);
+    font-size: 13px;
+    line-height: 1.7;
+    white-space: pre-wrap;
+    word-break: break-word;
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+/* ── Utilities ─────────────────────────────────────────────── */
+
+.hidden {
+    display: none !important;
+}
+
+/* ── Responsive ────────────────────────────────────────────── */
+
+@media (max-width: 1200px) {
+    .kpi-grid {
+        grid-template-columns: repeat(3, 1fr);
+    }
+
+    .trend-filters {
+        flex-wrap: wrap;
+    }
+}
+
+@media (max-width: 768px) {
+    .header {
+        padding: 12px 16px;
+    }
+
+    .dash-content {
+        padding: 16px 12px 48px;
+    }
+
+    .kpi-grid {
+        grid-template-columns: repeat(2, 1fr);
+    }
+
+    .charts-row {
+        grid-template-columns: 1fr;
+    }
+
+    .modal-scores {
+        grid-template-columns: repeat(3, 1fr);
+    }
+}
--- a/frontend/app.js
+++ b/frontend/app.js
@ -0,0 +1,304 @@
+/**
+ * Prompt Analyzer — Frontend Logic
+ */
+
+const API_BASE = window.location.origin;
+
+let currentAnalysisId = null;
+let currentResult = null;
+
+// ── Character count ───────────────────────────────────────────
+
+const promptInput = document.getElementById('prompt-input');
+const charCount = document.getElementById('char-count');
+
+promptInput.addEventListener('input', () => {
+    const len = promptInput.value.length;
+    charCount.textContent = `${len.toLocaleString()} characters`;
+});
+
+// ── Analyze ───────────────────────────────────────────────────
+
+async function analyzePrompt() {
+    const prompt = promptInput.value.trim();
+    if (!prompt) {
+        showToast('Please enter a prompt to analyze', 'warning');
+        return;
+    }
+
+    const context = document.getElementById('context-input').value.trim() || null;
+    const projectId = document.getElementById('project-input').value.trim() || null;
+
+    const btn = document.getElementById('analyze-btn');
+    btn.disabled = true;
+
+    hideResults();
+    hideError();
+    showLoading();
+
+    try {
+        const response = await fetch(`${API_BASE}/analyze`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({
+                prompt,
+                context,
+                project_id: projectId,
+            }),
+        });
+
+        if (!response.ok) {
+            const err = await response.json().catch(() => ({}));
+            throw new Error(err.detail || `HTTP ${response.status}`);
+        }
+
+        const result = await response.json();
+        currentResult = result;
+        currentAnalysisId = result.analysis_id;
+
+        hideLoading();
+        renderResults(result);
+
+    } catch (error) {
+        hideLoading();
+        showError(error.message);
+    } finally {
+        btn.disabled = false;
+    }
+}
+
+// ── Render Results ────────────────────────────────────────────
+
+function renderResults(result) {
+    const section = document.getElementById('results-section');
+    section.classList.remove('hidden');
+
+    // Overall score
+    const score = result.overall_score;
+    const scoreCard = document.getElementById('overall-score-card');
+    const scoreRing = document.getElementById('score-ring');
+    const scoreValue = document.getElementById('overall-score');
+    const scoreLabel = document.getElementById('overall-label');
+
+    // Remove old class
+    scoreCard.className = 'overall-score-card';
+    if (score >= 85) {
+        scoreCard.classList.add('score-excellent');
+        scoreLabel.textContent = 'Excellent — This prompt is well-crafted';
+    } else if (score >= 65) {
+        scoreCard.classList.add('score-good');
+        scoreLabel.textContent = 'Good — Minor improvements possible';
+    } else if (score >= 40) {
+        scoreCard.classList.add('score-fair');
+        scoreLabel.textContent = 'Fair — Several issues to address';
+    } else {
+        scoreCard.classList.add('score-poor');
+        scoreLabel.textContent = 'Poor — Major improvements needed';
+    }
+
+    // Animate ring
+    const circumference = 2 * Math.PI * 52; // r=52
+    const offset = circumference - (score / 100) * circumference;
+    scoreRing.style.strokeDashoffset = offset;
+
+    // Animate number
+    animateNumber(scoreValue, score);
+
+    // Dimension scores
+    const dimensions = ['clarity', 'token_efficiency', 'goal_alignment', 'structure', 'vagueness_index'];
+    dimensions.forEach(dim => {
+        const data = result.scores[dim];
+        const numEl = document.querySelector(`[data-dimension="${dim}"]`);
+        const reasoningEl = document.getElementById(`reasoning-${dim}`);
+
+        animateNumber(numEl, data.score);
+        reasoningEl.textContent = data.reasoning;
+
+        // Color the score
+        const card = document.getElementById(`score-${dim}`);
+        card.style.borderColor = getScoreColor(data.score);
+        numEl.style.color = getScoreColor(data.score);
+    });
+
+    // Mistakes
+    const mistakesList = document.getElementById('mistakes-list');
+    const mistakeCount = document.getElementById('mistake-count');
+    const mistakes = result.mistakes || [];
+
+    mistakeCount.textContent = `${mistakes.length} issue${mistakes.length !== 1 ? 's' : ''}`;
+    mistakeCount.className = mistakes.length === 0 ? 'badge badge-success' : 'badge badge-error';
+
+    if (mistakes.length === 0) {
+        mistakesList.innerHTML = `
+            <div style="text-align:center; padding:20px; color:var(--accent-green);">
+                <span class="material-symbols-outlined" style="font-size:32px;">check_circle</span>
+                <p style="margin-top:8px;">No issues found — great prompt!</p>
+            </div>
+        `;
+    } else {
+        mistakesList.innerHTML = mistakes.map(m => `
+            <div class="mistake-item">
+                <div class="mistake-icon">
+                    <span class="material-symbols-outlined">${getMistakeIcon(m.type)}</span>
+                </div>
+                <div class="mistake-content">
+                    <div class="mistake-type">${formatMistakeType(m.type)}</div>
+                    ${m.text ? `<div class="mistake-text">"${escapeHtml(m.text)}"</div>` : ''}
+                    <div class="mistake-suggestion">${escapeHtml(m.suggestion)}</div>
+                </div>
+            </div>
+        `).join('');
+    }
+
+    // Rewrite comparison
+    document.getElementById('original-text').textContent = result.original_prompt;
+    document.getElementById('rewritten-text').textContent = result.rewritten_prompt;
+
+    const tc = result.token_comparison;
+    document.getElementById('original-tokens').textContent = `${tc.original_tokens} tokens`;
+    document.getElementById('rewritten-tokens').textContent = `${tc.rewritten_tokens} tokens`;
+    document.getElementById('savings-text').textContent = `${tc.savings_percent}% saved`;
+
+    // Scroll to results
+    section.scrollIntoView({ behavior: 'smooth', block: 'start' });
+}
+
+// ── Rewrite Choice ────────────────────────────────────────────
+
+async function useRewrite() {
+    if (!currentResult) return;
+
+    // Paste rewritten prompt into the input textarea
+    promptInput.value = currentResult.rewritten_prompt;
+    charCount.textContent = `${promptInput.value.length.toLocaleString()} characters`;
+
+    // Also copy to clipboard
+    try {
+        await navigator.clipboard.writeText(currentResult.rewritten_prompt);
+        showToast('Rewritten prompt pasted into input & copied to clipboard!', 'success');
+    } catch {
+        showToast('Rewritten prompt pasted into input!', 'success');
+    }
+
+    // Scroll back to prompt input
+    promptInput.scrollIntoView({ behavior: 'smooth', block: 'center' });
+    promptInput.focus();
+
+    if (currentAnalysisId) {
+        fetch(`${API_BASE}/rewrite-choice`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ analysis_id: currentAnalysisId, used_rewrite: true }),
+        }).catch(() => { });
+    }
+}
+
+async function keepOriginal() {
+    if (!currentResult) return;
+
+    try {
+        await navigator.clipboard.writeText(currentResult.original_prompt);
+        showToast('Original prompt copied to clipboard!', 'success');
+    } catch {
+        showToast('Could not copy to clipboard', 'warning');
+    }
+
+    if (currentAnalysisId) {
+        fetch(`${API_BASE}/rewrite-choice`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ analysis_id: currentAnalysisId, used_rewrite: false }),
+        }).catch(() => { });
+    }
+}
+
+// ── Helpers ───────────────────────────────────────────────────
+
+function animateNumber(el, target) {
+    let current = 0;
+    const duration = 1200;
+    const stepTime = 16;
+    const steps = duration / stepTime;
+    const increment = target / steps;
+
+    const timer = setInterval(() => {
+        current += increment;
+        if (current >= target) {
+            current = target;
+            clearInterval(timer);
+        }
+        el.textContent = Math.round(current);
+    }, stepTime);
+}
+
+function getScoreColor(score) {
+    if (score >= 85) return 'var(--accent-green)';
+    if (score >= 65) return 'var(--accent-blue)';
+    if (score >= 40) return 'var(--accent-amber)';
+    return 'var(--accent-red)';
+}
+
+function getMistakeIcon(type) {
+    const icons = {
+        vague_instruction: 'blur_on',
+        missing_context: 'help_outline',
+        redundancy: 'content_copy',
+        contradiction: 'sync_problem',
+        poor_formatting: 'format_align_left',
+        missing_output_format: 'output',
+        unclear_scope: 'unfold_more',
+        overly_complex: 'device_hub',
+    };
+    return icons[type] || 'warning';
+}
+
+function formatMistakeType(type) {
+    return type.replace(/_/g, ' ');
+}
+
+function escapeHtml(text) {
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+}
+
+function showLoading() {
+    document.getElementById('loading-section').classList.remove('hidden');
+}
+
+function hideLoading() {
+    document.getElementById('loading-section').classList.add('hidden');
+}
+
+function hideResults() {
+    document.getElementById('results-section').classList.add('hidden');
+}
+
+function showError(message) {
+    document.getElementById('error-message').textContent = message;
+    document.getElementById('error-section').classList.remove('hidden');
+}
+
+function hideError() {
+    document.getElementById('error-section').classList.add('hidden');
+}
+
+function showToast(message, type = 'success') {
+    const toast = document.getElementById('toast');
+    const icon = document.getElementById('toast-icon');
+    const msg = document.getElementById('toast-message');
+
+    icon.textContent = type === 'success' ? 'check_circle' : 'warning';
+    icon.style.color = type === 'success' ? 'var(--accent-green)' : 'var(--accent-amber)';
+    msg.textContent = message;
+
+    toast.classList.remove('hidden');
+    setTimeout(() => toast.classList.add('hidden'), 3000);
+}
+
+// Allow Ctrl+Enter to submit
+promptInput.addEventListener('keydown', (e) => {
+    if ((e.ctrlKey || e.metaKey) && e.key === 'Enter') {
+        analyzePrompt();
+    }
+});
--- a/frontend/index.html
+++ b/frontend/index.html
@ -0,0 +1,230 @@
+<!DOCTYPE html>
+<html lang="en" class="dark">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prompt Analyzer — AI-Powered Prompt Quality Analysis</title>
+    <meta name="description" content="Analyze your prompts for clarity, efficiency, and structure. Get AI-powered rewrites and quality scores.">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:wght,FILL@100..700,0..1&display=swap" rel="stylesheet">
+    <link rel="stylesheet" href="/static/styles.css">
+</head>
+<body>
+    <!-- Header -->
+    <header class="header">
+        <div class="header-brand">
+            <div class="brand-icon">
+                <span class="material-symbols-outlined">psychology</span>
+            </div>
+            <div>
+                <h1 class="brand-title">PromptAnalyzer</h1>
+                <span class="brand-version">v0.1.0</span>
+            </div>
+        </div>
+        <div class="header-actions">
+            <a href="/dashboard-ui" class="btn btn-ghost">
+                <span class="material-symbols-outlined">dashboard</span>
+                Dashboard
+            </a>
+        </div>
+    </header>
+
+    <!-- Main Content -->
+    <main class="main-content">
+        <!-- Input Section -->
+        <section class="input-section">
+            <div class="input-card">
+                <div class="card-header">
+                    <h2>
+                        <span class="material-symbols-outlined">edit_note</span>
+                        Enter Your Prompt
+                    </h2>
+                    <span class="badge badge-info">AI Analysis</span>
+                </div>
+                <div class="input-fields">
+                    <div class="field-group">
+                        <label for="prompt-input">Prompt</label>
+                        <textarea id="prompt-input" rows="8" placeholder="Paste or type your prompt here..."></textarea>
+                        <div class="field-footer">
+                            <span id="char-count" class="char-count">0 characters</span>
+                        </div>
+                    </div>
+                    <div class="field-row">
+                        <div class="field-group flex-1">
+                            <label for="context-input">Context / Goal <span class="optional">(optional)</span></label>
+                            <input type="text" id="context-input" placeholder="e.g., Customer support chatbot, Code review...">
+                        </div>
+                        <div class="field-group flex-1">
+                            <label for="project-input">Project ID <span class="optional">(optional)</span></label>
+                            <input type="text" id="project-input" placeholder="e.g., customer_support_bot">
+                        </div>
+                    </div>
+                    <button id="analyze-btn" class="btn btn-primary btn-lg" onclick="analyzePrompt()">
+                        <span class="material-symbols-outlined">auto_awesome</span>
+                        Analyze Prompt
+                    </button>
+                </div>
+            </div>
+        </section>
+
+        <!-- Loading -->
+        <section id="loading-section" class="loading-section hidden">
+            <div class="loading-card">
+                <div class="spinner"></div>
+                <p>Analyzing your prompt with Claude Opus...</p>
+                <p class="loading-sub">Scoring clarity, efficiency, structure, and more</p>
+            </div>
+        </section>
+
+        <!-- Results Section -->
+        <section id="results-section" class="results-section hidden">
+            <!-- Overall Score -->
+            <div class="overall-score-card" id="overall-score-card">
+                <div class="overall-score-ring">
+                    <svg viewBox="0 0 120 120">
+                        <circle cx="60" cy="60" r="52" class="score-ring-bg"/>
+                        <circle cx="60" cy="60" r="52" class="score-ring-fill" id="score-ring"/>
+                    </svg>
+                    <span class="overall-score-value" id="overall-score">0</span>
+                </div>
+                <div class="overall-score-info">
+                    <h2>Overall Quality Score</h2>
+                    <p id="overall-label" class="score-label">—</p>
+                </div>
+            </div>
+
+            <!-- Score Cards -->
+            <div class="scores-grid">
+                <div class="score-card" id="score-clarity">
+                    <div class="score-card-header">
+                        <span class="material-symbols-outlined">visibility</span>
+                        <h3>Clarity</h3>
+                    </div>
+                    <div class="score-value-wrap">
+                        <span class="score-num" data-dimension="clarity">0</span>
+                        <span class="score-max">/100</span>
+                    </div>
+                    <p class="score-reasoning" id="reasoning-clarity"></p>
+                </div>
+                <div class="score-card" id="score-token_efficiency">
+                    <div class="score-card-header">
+                        <span class="material-symbols-outlined">token</span>
+                        <h3>Token Efficiency</h3>
+                    </div>
+                    <div class="score-value-wrap">
+                        <span class="score-num" data-dimension="token_efficiency">0</span>
+                        <span class="score-max">/100</span>
+                    </div>
+                    <p class="score-reasoning" id="reasoning-token_efficiency"></p>
+                </div>
+                <div class="score-card" id="score-goal_alignment">
+                    <div class="score-card-header">
+                        <span class="material-symbols-outlined">target</span>
+                        <h3>Goal Alignment</h3>
+                    </div>
+                    <div class="score-value-wrap">
+                        <span class="score-num" data-dimension="goal_alignment">0</span>
+                        <span class="score-max">/100</span>
+                    </div>
+                    <p class="score-reasoning" id="reasoning-goal_alignment"></p>
+                </div>
+                <div class="score-card" id="score-structure">
+                    <div class="score-card-header">
+                        <span class="material-symbols-outlined">account_tree</span>
+                        <h3>Structure</h3>
+                    </div>
+                    <div class="score-value-wrap">
+                        <span class="score-num" data-dimension="structure">0</span>
+                        <span class="score-max">/100</span>
+                    </div>
+                    <p class="score-reasoning" id="reasoning-structure"></p>
+                </div>
+                <div class="score-card" id="score-vagueness_index">
+                    <div class="score-card-header">
+                        <span class="material-symbols-outlined">blur_on</span>
+                        <h3>Vagueness</h3>
+                    </div>
+                    <div class="score-value-wrap">
+                        <span class="score-num" data-dimension="vagueness_index">0</span>
+                        <span class="score-max">/100</span>
+                    </div>
+                    <p class="score-reasoning" id="reasoning-vagueness_index"></p>
+                </div>
+            </div>
+
+            <!-- Mistakes -->
+            <div class="mistakes-card" id="mistakes-card">
+                <div class="card-header">
+                    <h2>
+                        <span class="material-symbols-outlined">bug_report</span>
+                        Identified Mistakes
+                    </h2>
+                    <span class="badge badge-error" id="mistake-count">0 issues</span>
+                </div>
+                <div id="mistakes-list" class="mistakes-list"></div>
+            </div>
+
+            <!-- Rewrite Comparison -->
+            <div class="rewrite-card" id="rewrite-card">
+                <div class="card-header">
+                    <h2>
+                        <span class="material-symbols-outlined">compare_arrows</span>
+                        Prompt Comparison
+                    </h2>
+                    <div class="token-savings" id="token-savings">
+                        <span class="material-symbols-outlined">savings</span>
+                        <span id="savings-text">0% saved</span>
+                    </div>
+                </div>
+                <div class="comparison-grid">
+                    <div class="comparison-col">
+                        <div class="comparison-label">
+                            <span class="dot dot-red"></span>Original
+                            <span class="token-badge" id="original-tokens">0 tokens</span>
+                        </div>
+                        <div class="comparison-text" id="original-text"></div>
+                    </div>
+                    <div class="comparison-col">
+                        <div class="comparison-label">
+                            <span class="dot dot-green"></span>Optimized Rewrite
+                            <span class="token-badge" id="rewritten-tokens">0 tokens</span>
+                        </div>
+                        <div class="comparison-text" id="rewritten-text"></div>
+                    </div>
+                </div>
+                <div class="rewrite-actions">
+                    <button class="btn btn-success" onclick="useRewrite()">
+                        <span class="material-symbols-outlined">check_circle</span>
+                        Use Rewritten Prompt
+                    </button>
+                    <button class="btn btn-outline" onclick="keepOriginal()">
+                        <span class="material-symbols-outlined">undo</span>
+                        Keep Original
+                    </button>
+                </div>
+            </div>
+        </section>
+
+        <!-- Error -->
+        <section id="error-section" class="error-section hidden">
+            <div class="error-card">
+                <span class="material-symbols-outlined">error</span>
+                <h3>Analysis Failed</h3>
+                <p id="error-message">Something went wrong.</p>
+                <button class="btn btn-outline" onclick="hideError()">Try Again</button>
+            </div>
+        </section>
+    </main>
+
+    <!-- Toast -->
+    <div id="toast" class="toast hidden">
+        <span class="material-symbols-outlined" id="toast-icon">check_circle</span>
+        <span id="toast-message">Copied to clipboard!</span>
+    </div>
+
+    <script src="/static/app.js"></script>
+</body>
+</html>
--- a/frontend/styles.css
+++ b/frontend/styles.css
@ -0,0 +1,694 @@
+/* ── Design System ──────────────────────────────────────────── */
+
+:root {
+    --bg-primary:    #0a0e1a;
+    --bg-secondary:  #111827;
+    --bg-card:       #1a2235;
+    --bg-card-hover: #1e2840;
+    --bg-input:      #0f1629;
+
+    --border:        #2a3452;
+    --border-hover:  #3b4a70;
+
+    --text-primary:  #f1f5f9;
+    --text-secondary:#8899b4;
+    --text-muted:    #5a6a85;
+
+    --accent-blue:   #3b82f6;
+    --accent-purple: #8b5cf6;
+    --accent-green:  #10b981;
+    --accent-red:    #ef4444;
+    --accent-amber:  #f59e0b;
+    --accent-cyan:   #06b6d4;
+
+    --font-sans:     'Inter', -apple-system, sans-serif;
+    --font-mono:     'JetBrains Mono', monospace;
+
+    --radius-sm:     6px;
+    --radius-md:     10px;
+    --radius-lg:     16px;
+    --radius-xl:     20px;
+
+    --shadow-card:   0 4px 24px rgba(0, 0, 0, 0.3);
+    --shadow-glow:   0 0 20px rgba(59, 130, 246, 0.15);
+}
+
+/* ── Reset & Base ──────────────────────────────────────────── */
+
+*, *::before, *::after {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    font-family: var(--font-sans);
+    background: var(--bg-primary);
+    color: var(--text-primary);
+    min-height: 100vh;
+    -webkit-font-smoothing: antialiased;
+    line-height: 1.6;
+}
+
+/* Custom scrollbar */
+::-webkit-scrollbar { width: 8px; height: 8px; }
+::-webkit-scrollbar-track { background: var(--bg-primary); }
+::-webkit-scrollbar-thumb { background: var(--border); border-radius: 4px; }
+::-webkit-scrollbar-thumb:hover { background: var(--border-hover); }
+
+/* ── Header ────────────────────────────────────────────────── */
+
+.header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 16px 32px;
+    border-bottom: 1px solid var(--border);
+    background: var(--bg-secondary);
+    position: sticky;
+    top: 0;
+    z-index: 50;
+    backdrop-filter: blur(12px);
+}
+
+.header-brand {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.brand-icon {
+    width: 40px;
+    height: 40px;
+    border-radius: var(--radius-md);
+    background: linear-gradient(135deg, var(--accent-blue), var(--accent-purple));
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: white;
+}
+
+.brand-title {
+    font-size: 18px;
+    font-weight: 700;
+    letter-spacing: 0.5px;
+}
+
+.brand-version {
+    font-size: 11px;
+    color: var(--text-muted);
+    font-weight: 500;
+}
+
+.header-actions {
+    display: flex;
+    gap: 8px;
+}
+
+/* ── Main Content ──────────────────────────────────────────── */
+
+.main-content {
+    max-width: 1100px;
+    margin: 0 auto;
+    padding: 32px 24px 64px;
+}
+
+/* ── Cards ─────────────────────────────────────────────────── */
+
+.input-card, .mistakes-card, .rewrite-card, .loading-card, .error-card {
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    padding: 28px;
+    box-shadow: var(--shadow-card);
+}
+
+.card-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    margin-bottom: 20px;
+}
+
+.card-header h2 {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    font-size: 18px;
+    font-weight: 600;
+}
+
+.card-header h2 .material-symbols-outlined {
+    font-size: 22px;
+    color: var(--accent-blue);
+}
+
+/* ── Input Section ─────────────────────────────────────────── */
+
+.input-fields {
+    display: flex;
+    flex-direction: column;
+    gap: 16px;
+}
+
+.field-group {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+}
+
+.field-group label {
+    font-size: 13px;
+    font-weight: 600;
+    color: var(--text-secondary);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.optional {
+    font-weight: 400;
+    text-transform: none;
+    color: var(--text-muted);
+}
+
+.field-row {
+    display: flex;
+    gap: 16px;
+}
+
+.flex-1 { flex: 1; }
+
+textarea, input[type="text"] {
+    background: var(--bg-input);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-md);
+    padding: 14px 16px;
+    font-family: var(--font-mono);
+    font-size: 14px;
+    color: var(--text-primary);
+    resize: vertical;
+    transition: border-color 0.2s, box-shadow 0.2s;
+    outline: none;
+    width: 100%;
+}
+
+textarea:focus, input[type="text"]:focus {
+    border-color: var(--accent-blue);
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.15);
+}
+
+textarea::placeholder, input::placeholder {
+    color: var(--text-muted);
+}
+
+.field-footer {
+    display: flex;
+    justify-content: flex-end;
+}
+
+.char-count {
+    font-size: 12px;
+    color: var(--text-muted);
+    font-family: var(--font-mono);
+}
+
+/* ── Buttons ───────────────────────────────────────────────── */
+
+.btn {
+    display: inline-flex;
+    align-items: center;
+    gap: 8px;
+    padding: 10px 20px;
+    font-size: 14px;
+    font-weight: 600;
+    font-family: var(--font-sans);
+    border-radius: var(--radius-md);
+    border: none;
+    cursor: pointer;
+    transition: all 0.2s;
+    text-decoration: none;
+}
+
+.btn .material-symbols-outlined { font-size: 18px; }
+
+.btn-primary {
+    background: linear-gradient(135deg, var(--accent-blue), var(--accent-purple));
+    color: white;
+    box-shadow: 0 4px 14px rgba(59, 130, 246, 0.3);
+}
+.btn-primary:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 6px 20px rgba(59, 130, 246, 0.4);
+}
+.btn-primary:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    transform: none;
+}
+
+.btn-lg {
+    padding: 14px 28px;
+    font-size: 15px;
+}
+
+.btn-success {
+    background: var(--accent-green);
+    color: white;
+}
+.btn-success:hover {
+    background: #0d9668;
+    transform: translateY(-1px);
+}
+
+.btn-outline {
+    background: transparent;
+    color: var(--text-secondary);
+    border: 1px solid var(--border);
+}
+.btn-outline:hover {
+    border-color: var(--text-secondary);
+    color: var(--text-primary);
+}
+
+.btn-ghost {
+    background: transparent;
+    color: var(--text-secondary);
+    padding: 8px 16px;
+}
+.btn-ghost:hover {
+    background: var(--bg-card);
+    color: var(--text-primary);
+}
+
+/* ── Badges ────────────────────────────────────────────────── */
+
+.badge {
+    font-size: 12px;
+    font-weight: 600;
+    padding: 4px 10px;
+    border-radius: 999px;
+}
+
+.badge-info {
+    background: rgba(59, 130, 246, 0.15);
+    color: var(--accent-blue);
+}
+
+.badge-error {
+    background: rgba(239, 68, 68, 0.15);
+    color: var(--accent-red);
+}
+
+.badge-success {
+    background: rgba(16, 185, 129, 0.15);
+    color: var(--accent-green);
+}
+
+/* ── Loading ───────────────────────────────────────────────── */
+
+.loading-section { margin-top: 32px; }
+
+.loading-card {
+    text-align: center;
+    padding: 48px;
+}
+
+.spinner {
+    width: 48px;
+    height: 48px;
+    border: 3px solid var(--border);
+    border-top-color: var(--accent-blue);
+    border-radius: 50%;
+    margin: 0 auto 20px;
+    animation: spin 0.8s linear infinite;
+}
+
+@keyframes spin { to { transform: rotate(360deg); } }
+
+.loading-sub {
+    font-size: 13px;
+    color: var(--text-muted);
+    margin-top: 6px;
+}
+
+/* ── Overall Score ─────────────────────────────────────────── */
+
+.overall-score-card {
+    display: flex;
+    align-items: center;
+    gap: 24px;
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    padding: 28px 32px;
+    margin-top: 32px;
+    box-shadow: var(--shadow-card);
+}
+
+.overall-score-ring {
+    position: relative;
+    width: 100px;
+    height: 100px;
+    flex-shrink: 0;
+}
+
+.overall-score-ring svg {
+    width: 100%;
+    height: 100%;
+    transform: rotate(-90deg);
+}
+
+.score-ring-bg {
+    fill: none;
+    stroke: var(--border);
+    stroke-width: 8;
+}
+
+.score-ring-fill {
+    fill: none;
+    stroke: var(--accent-blue);
+    stroke-width: 8;
+    stroke-linecap: round;
+    stroke-dasharray: 327;
+    stroke-dashoffset: 327;
+    transition: stroke-dashoffset 1.5s ease-out, stroke 0.5s;
+}
+
+.overall-score-value {
+    position: absolute;
+    inset: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 28px;
+    font-weight: 800;
+}
+
+.overall-score-info h2 {
+    font-size: 20px;
+    font-weight: 700;
+}
+
+.score-label {
+    font-size: 14px;
+    color: var(--text-secondary);
+    margin-top: 4px;
+}
+
+/* Score colors */
+.score-excellent .score-ring-fill { stroke: var(--accent-green); }
+.score-good .score-ring-fill { stroke: var(--accent-blue); }
+.score-fair .score-ring-fill { stroke: var(--accent-amber); }
+.score-poor .score-ring-fill { stroke: var(--accent-red); }
+
+.score-excellent .overall-score-value { color: var(--accent-green); }
+.score-good .overall-score-value { color: var(--accent-blue); }
+.score-fair .overall-score-value { color: var(--accent-amber); }
+.score-poor .overall-score-value { color: var(--accent-red); }
+
+/* ── Score Cards Grid ──────────────────────────────────────── */
+
+.scores-grid {
+    display: grid;
+    grid-template-columns: repeat(5, 1fr);
+    gap: 16px;
+    margin-top: 24px;
+}
+
+.score-card {
+    background: var(--bg-card);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-lg);
+    padding: 20px;
+    transition: border-color 0.2s, transform 0.2s;
+    box-shadow: var(--shadow-card);
+}
+
+.score-card:hover {
+    border-color: var(--accent-blue);
+    transform: translateY(-2px);
+}
+
+.score-card-header {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-bottom: 12px;
+}
+
+.score-card-header .material-symbols-outlined {
+    font-size: 20px;
+    color: var(--accent-blue);
+}
+
+.score-card-header h3 {
+    font-size: 13px;
+    font-weight: 600;
+    color: var(--text-secondary);
+}
+
+.score-value-wrap {
+    display: flex;
+    align-items: baseline;
+    gap: 2px;
+    margin-bottom: 10px;
+}
+
+.score-num {
+    font-size: 32px;
+    font-weight: 800;
+    line-height: 1;
+}
+
+.score-max {
+    font-size: 14px;
+    color: var(--text-muted);
+    font-weight: 500;
+}
+
+.score-reasoning {
+    font-size: 12px;
+    color: var(--text-secondary);
+    line-height: 1.5;
+}
+
+/* ── Mistakes ──────────────────────────────────────────────── */
+
+.mistakes-card { margin-top: 24px; }
+
+.mistakes-list {
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+}
+
+.mistake-item {
+    display: flex;
+    gap: 14px;
+    padding: 16px;
+    background: var(--bg-input);
+    border-radius: var(--radius-md);
+    border-left: 3px solid var(--accent-red);
+}
+
+.mistake-icon {
+    flex-shrink: 0;
+    width: 32px;
+    height: 32px;
+    border-radius: var(--radius-sm);
+    background: rgba(239, 68, 68, 0.1);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.mistake-icon .material-symbols-outlined {
+    font-size: 18px;
+    color: var(--accent-red);
+}
+
+.mistake-content { flex: 1; }
+
+.mistake-type {
+    font-size: 12px;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--accent-red);
+    margin-bottom: 4px;
+}
+
+.mistake-text {
+    font-size: 13px;
+    color: var(--text-primary);
+    font-family: var(--font-mono);
+    background: rgba(239, 68, 68, 0.08);
+    padding: 4px 8px;
+    border-radius: 4px;
+    display: inline-block;
+    margin-bottom: 6px;
+}
+
+.mistake-suggestion {
+    font-size: 13px;
+    color: var(--accent-green);
+}
+
+.mistake-suggestion::before {
+    content: "💡 ";
+}
+
+/* ── Rewrite Comparison ────────────────────────────────────── */
+
+.rewrite-card { margin-top: 24px; }
+
+.token-savings {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    font-size: 14px;
+    font-weight: 600;
+    color: var(--accent-green);
+}
+
+.token-savings .material-symbols-outlined { font-size: 18px; }
+
+.comparison-grid {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 16px;
+    margin-bottom: 20px;
+}
+
+.comparison-label {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-size: 13px;
+    font-weight: 600;
+    color: var(--text-secondary);
+    margin-bottom: 10px;
+}
+
+.dot {
+    width: 8px;
+    height: 8px;
+    border-radius: 50%;
+}
+
+.dot-red { background: var(--accent-red); }
+.dot-green { background: var(--accent-green); }
+
+.token-badge {
+    font-size: 11px;
+    font-weight: 500;
+    background: var(--bg-input);
+    padding: 2px 8px;
+    border-radius: 999px;
+    color: var(--text-muted);
+    margin-left: auto;
+}
+
+.comparison-text {
+    background: var(--bg-input);
+    border: 1px solid var(--border);
+    border-radius: var(--radius-md);
+    padding: 16px;
+    font-family: var(--font-mono);
+    font-size: 13px;
+    line-height: 1.7;
+    white-space: pre-wrap;
+    word-break: break-word;
+    max-height: 300px;
+    overflow-y: auto;
+    color: var(--text-primary);
+}
+
+.rewrite-actions {
+    display: flex;
+    gap: 12px;
+}
+
+/* ── Error ─────────────────────────────────────────────────── */
+
+.error-section { margin-top: 32px; }
+
+.error-card {
+    text-align: center;
+    padding: 40px;
+}
+
+.error-card .material-symbols-outlined {
+    font-size: 48px;
+    color: var(--accent-red);
+    margin-bottom: 12px;
+}
+
+.error-card h3 {
+    font-size: 18px;
+    margin-bottom: 8px;
+}
+
+.error-card p {
+    color: var(--text-secondary);
+    font-size: 14px;
+    margin-bottom: 20px;
+}
+
+/* ── Toast ─────────────────────────────────────────────────── */
+
+.toast {
+    position: fixed;
+    bottom: 32px;
+    right: 32px;
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 14px 20px;
+    background: var(--bg-card);
+    border: 1px solid var(--accent-green);
+    border-radius: var(--radius-md);
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
+    font-size: 14px;
+    font-weight: 500;
+    z-index: 100;
+    animation: toastIn 0.3s ease-out;
+}
+
+.toast .material-symbols-outlined {
+    color: var(--accent-green);
+}
+
+@keyframes toastIn {
+    from { transform: translateY(20px); opacity: 0; }
+    to { transform: translateY(0); opacity: 1; }
+}
+
+/* ── Utilities ─────────────────────────────────────────────── */
+
+.hidden { display: none !important; }
+
+.results-section { animation: fadeIn 0.4s ease-out; }
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(12px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+/* ── Responsive ────────────────────────────────────────────── */
+
+@media (max-width: 900px) {
+    .scores-grid {
+        grid-template-columns: repeat(3, 1fr);
+    }
+}
+
+@media (max-width: 680px) {
+    .header { padding: 12px 16px; }
+    .main-content { padding: 16px 12px 48px; }
+    .scores-grid { grid-template-columns: repeat(2, 1fr); }
+    .comparison-grid { grid-template-columns: 1fr; }
+    .field-row { flex-direction: column; }
+    .overall-score-card { flex-direction: column; text-align: center; }
+    .rewrite-actions { flex-direction: column; }
+}
--- a/mcp_server/mcp_config.json
+++ b/mcp_server/mcp_config.json
@ -0,0 +1,14 @@
+{
+    "mcpServers": {
+        "prompt-analyzer": {
+            "command": "/Users/ananya/Downloads/stitch_prompt_performance_analytics_dashboard/.venv/bin/python",
+            "args": [
+                "-m",
+                "mcp_server.server"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/ananya/Downloads/stitch_prompt_performance_analytics_dashboard"
+            }
+        }
+    }
+}
--- a/mcp_server/server.py
+++ b/mcp_server/server.py
@ -0,0 +1,165 @@
+"""
+MCP Server — Exposes the Prompt Analyzer as discoverable tools
+for enterprise multi-agent systems.
+
+Tools:
+- analyze_prompt: Analyze a prompt for quality
+- get_analysis_history: Retrieve past analyses
+"""
+
+import asyncio
+import json
+import logging
+
+from mcp.server import Server
+from mcp.server.stdio import stdio_server
+from mcp.types import Tool, TextContent
+
+from prompt_analyzer import PromptAnalyzer
+from analytics_reporter.reporter import AnalyticsReporter
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Create MCP server
+server = Server("prompt-analyzer")
+
+# Shared instances
+analyzer = PromptAnalyzer()
+reporter = AnalyticsReporter()
+
+
+@server.list_tools()
+async def list_tools() -> list[Tool]:
+    """Advertise available tools to MCP clients."""
+    return [
+        Tool(
+            name="analyze_prompt",
+            description=(
+                "Analyze a prompt for quality across 5 dimensions: "
+                "clarity, token efficiency, goal alignment, structure, "
+                "and vagueness. Returns scores (0-100), identified mistakes "
+                "with suggestions, an optimized rewrite, and token savings. "
+                "Supports project-aware analysis for context-specific recommendations."
+            ),
+            inputSchema={
+                "type": "object",
+                "properties": {
+                    "prompt": {
+                        "type": "string",
+                        "description": "The prompt to analyze",
+                    },
+                    "context": {
+                        "type": "string",
+                        "description": "Optional goal or context for the prompt",
+                    },
+                    "project_id": {
+                        "type": "string",
+                        "description": "Project ID for context-aware analysis (isolated per project)",
+                    },
+                    "source_agent": {
+                        "type": "string",
+                        "description": "Name of the agent that authored this prompt",
+                    },
+                    "target_agent": {
+                        "type": "string",
+                        "description": "Name of the agent this prompt is directed to",
+                    },
+                },
+                "required": ["prompt"],
+            },
+        ),
+        Tool(
+            name="get_analysis_history",
+            description=(
+                "Retrieve past prompt analyses. Can filter by project. "
+                "Useful for understanding prompt quality trends."
+            ),
+            inputSchema={
+                "type": "object",
+                "properties": {
+                    "limit": {
+                        "type": "integer",
+                        "description": "Max results to return (default 10)",
+                        "default": 10,
+                    },
+                    "project_id": {
+                        "type": "string",
+                        "description": "Filter by project ID",
+                    },
+                },
+            },
+        ),
+    ]
+
+
+@server.call_tool()
+async def call_tool(name: str, arguments: dict) -> list[TextContent]:
+    """Handle tool calls from MCP clients."""
+
+    if name == "analyze_prompt":
+        prompt = arguments.get("prompt", "")
+        if not prompt:
+            return [TextContent(type="text", text="Error: prompt is required")]
+
+        try:
+            # All MCP calls are agent interactions — default source_agent if not provided
+            source_agent = arguments.get("source_agent") or "mcp-client"
+
+            result = await analyzer.analyze(
+                prompt=prompt,
+                context=arguments.get("context"),
+                project_id=arguments.get("project_id"),
+                source_agent=source_agent,
+                target_agent=arguments.get("target_agent"),
+            )
+
+            # Store via Agent 2
+            await reporter.initialize()
+            analysis_id = await reporter.report(result)
+
+            response = result.model_dump(mode="json")
+            response["analysis_id"] = analysis_id
+
+            return [
+                TextContent(
+                    type="text",
+                    text=json.dumps(response, indent=2, default=str),
+                )
+            ]
+
+        except Exception as e:
+            logger.error("analyze_prompt failed: %s", e, exc_info=True)
+            return [TextContent(type="text", text=f"Error: {str(e)}")]
+
+    elif name == "get_analysis_history":
+        try:
+            await reporter.initialize()
+            data = await reporter.get_interactions(
+                limit=arguments.get("limit", 10),
+                project_id=arguments.get("project_id"),
+            )
+            return [
+                TextContent(
+                    type="text",
+                    text=json.dumps(data, indent=2, default=str),
+                )
+            ]
+
+        except Exception as e:
+            logger.error("get_analysis_history failed: %s", e, exc_info=True)
+            return [TextContent(type="text", text=f"Error: {str(e)}")]
+
+    return [TextContent(type="text", text=f"Unknown tool: {name}")]
+
+
+async def main():
+    """Run the MCP server over stdio."""
+    await reporter.initialize()
+    logger.info("MCP Server starting (stdio mode)")
+    async with stdio_server() as (read_stream, write_stream):
+        await server.run(read_stream, write_stream, server.create_initialization_options())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
--- a/prompt_analyzer/init.py
+++ b/prompt_analyzer/init.py
@ -0,0 +1,32 @@
+"""
+Prompt Analyzer — AI-powered prompt quality analysis.
+
+Usage:
+    from prompt_analyzer import PromptAnalyzer
+    analyzer = PromptAnalyzer()
+    result = await analyzer.analyze("Your prompt here")
+"""
+
+from prompt_analyzer.analyzer import PromptAnalyzer
+from prompt_analyzer.models import (
+    AnalysisResult,
+    AnalyzeRequest,
+    Scores,
+    Score,
+    Mistake,
+    TokenComparison,
+    AnalysisMetadata,
+)
+
+__version__ = "0.1.0"
+
+__all__ = [
+    "PromptAnalyzer",
+    "AnalysisResult",
+    "AnalyzeRequest",
+    "Scores",
+    "Score",
+    "Mistake",
+    "TokenComparison",
+    "AnalysisMetadata",
+]
--- a/prompt_analyzer/analyzer.py
+++ b/prompt_analyzer/analyzer.py
@ -0,0 +1,321 @@
+"""
+Core Prompt Analyzer agent.
+
+Takes a prompt, calls Claude via the Anthropic API, and returns
+structured analysis: scores, mistakes, rewritten prompt, and token comparison.
+"""
+
+import json
+import logging
+from typing import Optional
+
+import tiktoken
+
+from prompt_analyzer.anthropic_client import AnthropicClient
+from prompt_analyzer.context_store import ContextStore
+from prompt_analyzer.models import (
+    AnalysisResult,
+    AnalysisMetadata,
+    Scores,
+    Score,
+    Mistake,
+    TokenComparison,
+)
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_PROMPT = """You are an expert Prompt Quality Analyzer. Your job is to analyze a given prompt and return a structured JSON assessment.
+
+ANALYZE THE PROMPT ON THESE 5 DIMENSIONS (score each 0-100):
+
+1. **Clarity** (0-100): How unambiguous is the prompt? Can it be misinterpreted? Are instructions precise?
+2. **Token Efficiency** (0-100): How concise is the prompt? Are there redundant words, repeated instructions, or unnecessary filler? Higher = more efficient.
+3. **Goal Alignment** (0-100): Does the prompt clearly state what output is expected? Is the desired result format, length, and style specified?
+4. **Structure** (0-100): Is the prompt well-organized? Does it have logical flow, proper sections, and clear instruction ordering?
+5. **Vagueness Index** (0-100): How many vague/ambiguous phrases exist? ("make it good", "do something nice", "be creative"). Score 0 = extremely vague, 100 = no vagueness at all.
+
+ALSO:
+- **Identify specific mistakes** in the prompt. For each mistake, provide:
+  - `type`: one of: vague_instruction, missing_context, redundancy, contradiction, poor_formatting, missing_output_format, unclear_scope, overly_complex
+  - `text`: the exact problematic text from the prompt (null if the mistake is about something missing)
+  - `suggestion`: a concrete fix
+
+- **Rewrite the prompt** to be optimal — maximum clarity, minimum tokens, best structure. The rewrite should accomplish the exact same goal as the original.
+
+{project_context}
+
+RESPOND WITH ONLY VALID JSON in this exact format (no markdown, no code fences, just the JSON):
+{{
+  "overall_score": <number 0-100, weighted average: clarity 25%, token_efficiency 20%, goal_alignment 25%, structure 15%, vagueness_index 15%>,
+  "scores": {{
+    "clarity": {{ "score": <0-100>, "reasoning": "<1-2 sentences>" }},
+    "token_efficiency": {{ "score": <0-100>, "reasoning": "<1-2 sentences>" }},
+    "goal_alignment": {{ "score": <0-100>, "reasoning": "<1-2 sentences>" }},
+    "structure": {{ "score": <0-100>, "reasoning": "<1-2 sentences>" }},
+    "vagueness_index": {{ "score": <0-100>, "reasoning": "<1-2 sentences>" }}
+  }},
+  "mistakes": [
+    {{ "type": "<type>", "text": "<problematic text or null>", "suggestion": "<fix>" }}
+  ],
+  "rewritten_prompt": "<the optimized version of the prompt>"
+}}"""
+
+
+class PromptAnalyzer:
+    """
+    AI-powered prompt quality analyzer.
+
+    Usage:
+        analyzer = PromptAnalyzer()
+        result = await analyzer.analyze("Your prompt here")
+
+    For context-aware analysis (enterprise):
+        result = await analyzer.analyze(
+            prompt="...",
+            project_id="customer_support",
+            source_agent="planner",
+        )
+    """
+
+    def __init__(self):
+        self.llm = AnthropicClient()
+        self.context_store = ContextStore()
+        # Use cl100k_base tokenizer (closest to Claude's tokenization)
+        try:
+            self.tokenizer = tiktoken.get_encoding("cl100k_base")
+        except Exception:
+            self.tokenizer = None
+            logger.warning("tiktoken not available, token counts will be estimated")
+
+    def _count_tokens(self, text: str) -> int:
+        """Count tokens in text."""
+        if self.tokenizer:
+            return len(self.tokenizer.encode(text))
+        # Rough estimate: ~4 chars per token
+        return len(text) // 4
+
+    async def analyze(
+        self,
+        prompt: str,
+        context: Optional[str] = None,
+        project_id: Optional[str] = None,
+        source_agent: Optional[str] = None,
+        target_agent: Optional[str] = None,
+    ) -> AnalysisResult:
+        """
+        Analyze a prompt and return structured quality assessment.
+
+        Args:
+            prompt: The prompt to analyze
+            context: Optional goal/context for the prompt
+            project_id: Optional project ID for context-aware analysis
+            source_agent: Optional agent that authored this prompt
+            target_agent: Optional agent this prompt is directed to
+
+        Returns:
+            AnalysisResult with scores, mistakes, and rewritten prompt
+        """
+        logger.info(
+            "Analyzing prompt (length=%d, project=%s, agent=%s)",
+            len(prompt),
+            project_id,
+            source_agent,
+        )
+
+        # Build context-aware system prompt
+        project_context = self.context_store.build_context_summary(
+            project_id, source_agent
+        )
+        system_prompt = SYSTEM_PROMPT.format(project_context=project_context)
+
+        # Build user message
+        user_message = self._build_user_message(prompt, context)
+
+        # Call Gemini
+        raw_response = await self.llm.invoke(system_prompt, user_message)
+
+        # Parse the JSON response
+        result = self._parse_response(raw_response, prompt, project_id, source_agent, target_agent)
+
+        # Update context store (if project-aware)
+        if project_id:
+            analysis_dict = result.model_dump()
+            self.context_store.append_history(project_id, analysis_dict)
+            self.context_store.update_patterns(project_id, analysis_dict)
+            if source_agent:
+                self.context_store.update_agent_context(
+                    project_id, source_agent, analysis_dict
+                )
+
+        return result
+
+    def _build_user_message(
+        self, prompt: str, context: Optional[str] = None
+    ) -> str:
+        """Build the user message sent to Claude."""
+        parts = ["PROMPT TO ANALYZE:\n---"]
+        parts.append(prompt)
+        parts.append("---")
+
+        if context:
+            parts.append(f"\nCONTEXT/GOAL: {context}")
+
+        return "\n".join(parts)
+
+    def _extract_json(self, raw: str) -> str:
+        """Extract JSON from Claude's response, handling various formatting."""
+        import re
+
+        cleaned = raw.strip()
+
+        # 1. Remove markdown code fences (```json ... ``` or ``` ... ```)
+        fence_pattern = re.compile(r'```(?:json)?\s*\n?(.*?)\n?\s*```', re.DOTALL)
+        match = fence_pattern.search(cleaned)
+        if match:
+            cleaned = match.group(1).strip()
+
+        # 2. If the response doesn't start with {, try to find the JSON object
+        if not cleaned.startswith("{"):
+            brace_start = cleaned.find("{")
+            if brace_start != -1:
+                cleaned = cleaned[brace_start:]
+
+        # 3. Find the matching closing brace
+        if cleaned.startswith("{"):
+            depth = 0
+            in_string = False
+            escape = False
+            end_pos = len(cleaned)
+            for i, ch in enumerate(cleaned):
+                if escape:
+                    escape = False
+                    continue
+                if ch == '\\' and in_string:
+                    escape = True
+                    continue
+                if ch == '"' and not escape:
+                    in_string = not in_string
+                    continue
+                if in_string:
+                    continue
+                if ch == '{':
+                    depth += 1
+                elif ch == '}':
+                    depth -= 1
+                    if depth == 0:
+                        end_pos = i + 1
+                        break
+            cleaned = cleaned[:end_pos]
+
+        # 4. Fix trailing commas before } or ] (common LLM mistake)
+        cleaned = re.sub(r',\s*([}\]])', r'\1', cleaned)
+
+        return cleaned
+
+    def _parse_response(
+        self,
+        raw: str,
+        original_prompt: str,
+        project_id: Optional[str],
+        source_agent: Optional[str],
+        target_agent: Optional[str],
+    ) -> AnalysisResult:
+        """Parse Claude's JSON response into an AnalysisResult."""
+        cleaned = self._extract_json(raw)
+
+        try:
+            data = json.loads(cleaned)
+        except json.JSONDecodeError as e:
+            logger.error("Failed to parse Claude response as JSON: %s", e)
+            logger.error("Raw response (first 500 chars): %s", raw[:500])
+            logger.error("Cleaned response (first 500 chars): %s", cleaned[:500])
+            # Return a fallback result
+            return self._fallback_result(original_prompt, str(e), project_id, source_agent, target_agent)
+
+        # Build structured result
+        try:
+            scores_data = data.get("scores", {})
+            scores = Scores(
+                clarity=Score(**scores_data.get("clarity", {"score": 0, "reasoning": "N/A"})),
+                token_efficiency=Score(**scores_data.get("token_efficiency", {"score": 0, "reasoning": "N/A"})),
+                goal_alignment=Score(**scores_data.get("goal_alignment", {"score": 0, "reasoning": "N/A"})),
+                structure=Score(**scores_data.get("structure", {"score": 0, "reasoning": "N/A"})),
+                vagueness_index=Score(**scores_data.get("vagueness_index", {"score": 0, "reasoning": "N/A"})),
+            )
+
+            mistakes = [
+                Mistake(**m) for m in data.get("mistakes", [])
+            ]
+
+            rewritten = data.get("rewritten_prompt", original_prompt)
+            original_tokens = self._count_tokens(original_prompt)
+            rewritten_tokens = self._count_tokens(rewritten)
+            savings = (
+                round((1 - rewritten_tokens / original_tokens) * 100, 1)
+                if original_tokens > 0
+                else 0.0
+            )
+
+            return AnalysisResult(
+                original_prompt=original_prompt,
+                overall_score=data.get("overall_score", 0),
+                scores=scores,
+                mistakes=mistakes,
+                rewritten_prompt=rewritten,
+                token_comparison=TokenComparison(
+                    original_tokens=original_tokens,
+                    rewritten_tokens=rewritten_tokens,
+                    savings_percent=savings,
+                ),
+                metadata=AnalysisMetadata(
+                    project_id=project_id,
+                    source_agent=source_agent,
+                    target_agent=target_agent,
+                    mode="agent" if source_agent else "human",
+                ),
+            )
+
+        except Exception as e:
+            logger.error("Failed to build AnalysisResult: %s", e)
+            return self._fallback_result(original_prompt, str(e), project_id, source_agent, target_agent)
+
+    def _fallback_result(
+        self,
+        prompt: str,
+        error: str,
+        project_id: Optional[str],
+        source_agent: Optional[str],
+        target_agent: Optional[str],
+    ) -> AnalysisResult:
+        """Return a fallback result when parsing fails."""
+        fallback_score = Score(score=0, reasoning=f"Analysis failed: {error}")
+        return AnalysisResult(
+            original_prompt=prompt,
+            overall_score=0,
+            scores=Scores(
+                clarity=fallback_score,
+                token_efficiency=fallback_score,
+                goal_alignment=fallback_score,
+                structure=fallback_score,
+                vagueness_index=fallback_score,
+            ),
+            mistakes=[
+                Mistake(
+                    type="analysis_error",
+                    text=None,
+                    suggestion=f"Re-run analysis. Error: {error}",
+                )
+            ],
+            rewritten_prompt=prompt,
+            token_comparison=TokenComparison(
+                original_tokens=self._count_tokens(prompt),
+                rewritten_tokens=self._count_tokens(prompt),
+                savings_percent=0.0,
+            ),
+            metadata=AnalysisMetadata(
+                project_id=project_id,
+                source_agent=source_agent,
+                target_agent=target_agent,
+                mode="agent" if source_agent else "human",
+            ),
+        )
--- a/prompt_analyzer/anthropic_client.py
+++ b/prompt_analyzer/anthropic_client.py
@ -0,0 +1,64 @@
+"""Anthropic Claude client for prompt analysis."""
+
+import logging
+
+import anthropic
+
+from prompt_analyzer.config import (
+    ANTHROPIC_API_KEY,
+    ANTHROPIC_MODEL,
+    LLM_MAX_TOKENS,
+    LLM_TEMPERATURE,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AnthropicClient:
+    """Wrapper around the Anthropic Messages API."""
+
+    def __init__(self):
+        if not ANTHROPIC_API_KEY:
+            raise ValueError("ANTHROPIC_API_KEY is not set in .env")
+
+        self.client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)
+        self.model = ANTHROPIC_MODEL
+        self.max_tokens = LLM_MAX_TOKENS
+        self.temperature = LLM_TEMPERATURE
+
+    async def invoke(self, system_prompt: str, user_message: str) -> str:
+        """
+        Send a message to Claude and return the text response.
+        Uses the Anthropic Messages API directly.
+        """
+        logger.info("Invoking Anthropic model=%s", self.model)
+
+        try:
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
+                system=system_prompt,
+                messages=[
+                    {"role": "user", "content": user_message},
+                ],
+            )
+
+            # Extract text from the response
+            result = ""
+            for block in message.content:
+                if block.type == "text":
+                    result += block.text
+
+            logger.info("Anthropic response received, length=%d chars", len(result))
+            return result
+
+        except anthropic.AuthenticationError:
+            logger.error("Anthropic authentication failed — check your API key")
+            raise
+        except anthropic.RateLimitError:
+            logger.error("Anthropic rate limit hit")
+            raise
+        except Exception as e:
+            logger.error("Anthropic invocation failed: %s", str(e))
+            raise
--- a/prompt_analyzer/config.py
+++ b/prompt_analyzer/config.py
@ -0,0 +1,29 @@
+"""Configuration for the Prompt Analyzer."""
+
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env from the project root (two levels up from this file)
+_project_root = Path(__file__).resolve().parent.parent
+load_dotenv(_project_root / ".env")
+
+# Anthropic
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+ANTHROPIC_MODEL = os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-20250514")
+
+# LLM parameters
+LLM_MAX_TOKENS = int(os.getenv("LLM_MAX_TOKENS", "4096"))
+LLM_TEMPERATURE = float(os.getenv("LLM_TEMPERATURE", "0.3"))
+
+# Context store
+CONTEXT_STORE_DIR = os.getenv(
+    "CONTEXT_STORE_DIR",
+    os.path.join(os.path.dirname(os.path.dirname(__file__)), "context_store"),
+)
+
+# Analytics DB
+ANALYTICS_DB_PATH = os.getenv(
+    "ANALYTICS_DB_PATH",
+    os.path.join(os.path.dirname(os.path.dirname(__file__)), "analytics.db"),
+)
--- a/prompt_analyzer/context_store.py
+++ b/prompt_analyzer/context_store.py
@ -0,0 +1,272 @@
+"""
+Per-project context store with strict isolation.
+
+Each project gets its own directory. Context from one project
+is never read or written by another project's operations.
+"""
+
+import json
+import os
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from prompt_analyzer.config import CONTEXT_STORE_DIR
+
+logger = logging.getLogger(__name__)
+
+
+class ContextStore:
+    """Manages per-project context with strict isolation."""
+
+    def __init__(self, base_dir: str = CONTEXT_STORE_DIR):
+        self.base_dir = base_dir
+
+    # ── Path helpers (always scoped to a project) ──────────────
+
+    def _project_dir(self, project_id: str) -> str:
+        """Get the isolated directory for a specific project."""
+        safe_name = project_id.replace("/", "_").replace("..", "_")
+        return os.path.join(self.base_dir, f"project_{safe_name}")
+
+    def _profile_path(self, project_id: str) -> str:
+        return os.path.join(self._project_dir(project_id), "profile.json")
+
+    def _history_path(self, project_id: str) -> str:
+        return os.path.join(self._project_dir(project_id), "history.jsonl")
+
+    def _patterns_path(self, project_id: str) -> str:
+        return os.path.join(self._project_dir(project_id), "patterns.json")
+
+    def _agent_path(self, project_id: str, agent_id: str) -> str:
+        safe_agent = agent_id.replace("/", "_").replace("..", "_")
+        return os.path.join(
+            self._project_dir(project_id), "agents", f"{safe_agent}.json"
+        )
+
+    def _ensure_dir(self, path: str) -> None:
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    # ── Project profile ────────────────────────────────────────
+
+    def get_project_profile(self, project_id: str) -> dict:
+        """Load project profile. Returns empty dict if project is new."""
+        path = self._profile_path(project_id)
+        if not os.path.exists(path):
+            return {}
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def save_project_profile(self, project_id: str, profile: dict) -> None:
+        """Save or update a project profile."""
+        path = self._profile_path(project_id)
+        self._ensure_dir(path)
+        with open(path, "w") as f:
+            json.dump(profile, f, indent=2, default=str)
+        logger.info("Saved profile for project=%s", project_id)
+
+    # ── Analysis history (append-only log) ─────────────────────
+
+    def append_history(self, project_id: str, analysis: dict) -> None:
+        """Append an analysis result to the project's history."""
+        path = self._history_path(project_id)
+        self._ensure_dir(path)
+        entry = {
+            **analysis,
+            "_stored_at": datetime.now(timezone.utc).isoformat(),
+        }
+        with open(path, "a") as f:
+            f.write(json.dumps(entry, default=str) + "\n")
+        logger.debug("Appended history for project=%s", project_id)
+
+    def get_recent_history(
+        self, project_id: str, limit: int = 20
+    ) -> list[dict]:
+        """Get the most recent analyses for a project."""
+        path = self._history_path(project_id)
+        if not os.path.exists(path):
+            return []
+        entries = []
+        with open(path, "r") as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    try:
+                        entries.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        continue
+        return entries[-limit:]
+
+    # ── Learned patterns ───────────────────────────────────────
+
+    def get_patterns(self, project_id: str) -> dict:
+        """
+        Get learned patterns for a project:
+        - common_mistakes: list of frequently seen mistake types
+        - best_templates: high-scoring prompt excerpts
+        - preferred_style: inferred preferences
+        """
+        path = self._patterns_path(project_id)
+        if not os.path.exists(path):
+            return {"common_mistakes": [], "best_templates": [], "preferred_style": ""}
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def update_patterns(self, project_id: str, analysis: dict) -> None:
+        """Update learned patterns based on a new analysis result."""
+        patterns = self.get_patterns(project_id)
+
+        # Track mistake frequencies
+        mistake_types = [m.get("type", "unknown") for m in analysis.get("mistakes", [])]
+        existing_mistakes = {m["type"]: m.get("count", 0) for m in patterns.get("common_mistakes", []) if isinstance(m, dict)}
+        for mt in mistake_types:
+            existing_mistakes[mt] = existing_mistakes.get(mt, 0) + 1
+        patterns["common_mistakes"] = [
+            {"type": k, "count": v}
+            for k, v in sorted(existing_mistakes.items(), key=lambda x: -x[1])
+        ][:10]  # keep top 10
+
+        # Track best-scoring prompts as templates
+        overall_score = analysis.get("overall_score", 0)
+        if overall_score >= 85:
+            rewritten = analysis.get("rewritten_prompt", "")
+            if rewritten:
+                templates = patterns.get("best_templates", [])
+                templates.append(
+                    {"prompt": rewritten[:500], "score": overall_score}
+                )
+                # Keep top 5 by score
+                templates.sort(key=lambda x: -x["score"])
+                patterns["best_templates"] = templates[:5]
+
+        path = self._patterns_path(project_id)
+        self._ensure_dir(path)
+        with open(path, "w") as f:
+            json.dump(patterns, f, indent=2, default=str)
+
+    # ── Per-agent context (within a project) ───────────────────
+
+    def get_agent_context(
+        self, project_id: str, agent_id: str
+    ) -> dict:
+        """Get an agent's context within a specific project."""
+        path = self._agent_path(project_id, agent_id)
+        if not os.path.exists(path):
+            return {
+                "agent_id": agent_id,
+                "total_analyses": 0,
+                "avg_score": 0,
+                "common_mistakes": [],
+                "weakest_dimension": None,
+            }
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def update_agent_context(
+        self, project_id: str, agent_id: str, analysis: dict
+    ) -> None:
+        """Update an agent's context within a project after analysis."""
+        ctx = self.get_agent_context(project_id, agent_id)
+
+        # Update running average score
+        n = ctx["total_analyses"]
+        old_avg = ctx["avg_score"]
+        new_score = analysis.get("overall_score", 0)
+        ctx["total_analyses"] = n + 1
+        ctx["avg_score"] = round((old_avg * n + new_score) / (n + 1), 1)
+
+        # Track agent-specific mistakes
+        mistake_types = [m.get("type", "unknown") for m in analysis.get("mistakes", [])]
+        existing = {m["type"]: m.get("count", 0) for m in ctx.get("common_mistakes", []) if isinstance(m, dict)}
+        for mt in mistake_types:
+            existing[mt] = existing.get(mt, 0) + 1
+        ctx["common_mistakes"] = [
+            {"type": k, "count": v}
+            for k, v in sorted(existing.items(), key=lambda x: -x[1])
+        ][:5]
+
+        # Find weakest dimension
+        scores = analysis.get("scores", {})
+        if scores:
+            if isinstance(scores, dict):
+                weakest = min(
+                    scores.items(),
+                    key=lambda x: x[1].get("score", 100) if isinstance(x[1], dict) else x[1],
+                )
+                ctx["weakest_dimension"] = weakest[0]
+
+        path = self._agent_path(project_id, agent_id)
+        self._ensure_dir(path)
+        with open(path, "w") as f:
+            json.dump(ctx, f, indent=2, default=str)
+        logger.debug(
+            "Updated agent context: project=%s agent=%s", project_id, agent_id
+        )
+
+    # ── Build context summary for Claude's system prompt ───────
+
+    def build_context_summary(
+        self,
+        project_id: Optional[str],
+        source_agent: Optional[str] = None,
+    ) -> str:
+        """
+        Build a context summary string to inject into Claude's system prompt.
+        Only loads data from the specified project (strict isolation).
+        Returns empty string if no project_id is provided.
+        """
+        if not project_id:
+            return ""
+
+        parts = []
+
+        # Project profile
+        profile = self.get_project_profile(project_id)
+        if profile:
+            parts.append(f"PROJECT: {profile.get('name', project_id)}")
+            if profile.get("domain"):
+                parts.append(f"Domain: {profile['domain']}")
+            if profile.get("description"):
+                parts.append(f"Description: {profile['description']}")
+
+        # Learned patterns
+        patterns = self.get_patterns(project_id)
+        if patterns.get("common_mistakes"):
+            mistakes_str = ", ".join(
+                f"{m['type']} ({m['count']}x)" for m in patterns["common_mistakes"][:5]
+            )
+            parts.append(f"RECURRING MISTAKES IN THIS PROJECT: {mistakes_str}")
+
+        if patterns.get("best_templates"):
+            best = patterns["best_templates"][0]
+            parts.append(
+                f"HIGHEST-SCORING PROMPT IN THIS PROJECT (score {best['score']}):\n\"{best['prompt']}\""
+            )
+
+        # Agent-specific context
+        if source_agent:
+            agent_ctx = self.get_agent_context(project_id, source_agent)
+            if agent_ctx.get("total_analyses", 0) > 0:
+                parts.append(
+                    f"AGENT '{source_agent}' IN THIS PROJECT: "
+                    f"avg score={agent_ctx['avg_score']}, "
+                    f"analyses={agent_ctx['total_analyses']}"
+                )
+                if agent_ctx.get("weakest_dimension"):
+                    parts.append(
+                        f"This agent's weakest dimension: {agent_ctx['weakest_dimension']}"
+                    )
+                if agent_ctx.get("common_mistakes"):
+                    am = ", ".join(
+                        m["type"] for m in agent_ctx["common_mistakes"][:3]
+                    )
+                    parts.append(f"This agent's common mistakes: {am}")
+
+        if not parts:
+            return ""
+
+        return (
+            "\n\n--- PROJECT CONTEXT (use this to make recommendations more specific) ---\n"
+            + "\n".join(parts)
+            + "\n--- END PROJECT CONTEXT ---"
+        )
--- a/prompt_analyzer/models.py
+++ b/prompt_analyzer/models.py
@ -0,0 +1,64 @@
+"""Pydantic models for prompt analysis data structures."""
+
+from __future__ import annotations
+from datetime import datetime, timezone
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+class Score(BaseModel):
+    """A single dimension score with reasoning."""
+    score: int = Field(ge=0, le=100, description="Score from 0-100")
+    reasoning: str = Field(description="Brief explanation for the score")
+
+
+class Mistake(BaseModel):
+    """A specific mistake identified in the prompt."""
+    type: str = Field(description="Category: vague_instruction, missing_context, redundancy, contradiction, poor_formatting, missing_output_format")
+    text: Optional[str] = Field(default=None, description="The problematic text from the prompt, if applicable")
+    suggestion: str = Field(description="How to fix this mistake")
+
+
+class TokenComparison(BaseModel):
+    """Token usage comparison between original and rewritten prompts."""
+    original_tokens: int = Field(ge=0)
+    rewritten_tokens: int = Field(ge=0)
+    savings_percent: float = Field(description="Percentage of tokens saved")
+
+
+class Scores(BaseModel):
+    """All 5 analysis dimension scores."""
+    clarity: Score
+    token_efficiency: Score
+    goal_alignment: Score
+    structure: Score
+    vagueness_index: Score
+
+
+class AnalysisMetadata(BaseModel):
+    """Metadata about who/what triggered the analysis."""
+    project_id: Optional[str] = None
+    source_agent: Optional[str] = None
+    target_agent: Optional[str] = None
+    timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    mode: str = Field(default="human", description="'human' or 'agent'")
+
+
+class AnalysisResult(BaseModel):
+    """Complete analysis result returned by the Prompt Analyzer."""
+    original_prompt: str
+    overall_score: int = Field(ge=0, le=100)
+    scores: Scores
+    mistakes: list[Mistake] = Field(default_factory=list)
+    rewritten_prompt: str
+    token_comparison: TokenComparison
+    metadata: AnalysisMetadata = Field(default_factory=AnalysisMetadata)
+
+
+class AnalyzeRequest(BaseModel):
+    """Request payload for prompt analysis."""
+    prompt: str = Field(min_length=1, description="The prompt to analyze")
+    context: Optional[str] = Field(default=None, description="Optional goal or context for the prompt")
+    project_id: Optional[str] = Field(default=None, description="Project ID for context-aware analysis")
+    source_agent: Optional[str] = Field(default=None, description="Agent that sent this prompt")
+    target_agent: Optional[str] = Field(default=None, description="Agent this prompt is directed to")
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,7 @@
+fastapi
+uvicorn[standard]
+anthropic
+pydantic>=2.0
+python-dotenv
+tiktoken
+aiosqlite