feat: add automated skill discovery pipeline

Implements a background process that monitors session logs for successful
agent action sequences, uses the LLM router to extract reusable skill
templates, and stores them in a SQLite database. Discovered skills are
surfaced via dashboard notifications (push + WebSocket + event bus) and
a new /skills page with HTMX polling. Users can confirm, reject, or
archive discovered skills.

- src/timmy/skill_discovery.py: Core engine with LLM analysis + heuristic fallback
- src/dashboard/routes/skills.py: CRUD routes for skill management
- src/dashboard/templates/skills.html: Main skills page
- src/dashboard/templates/partials/skills_list.html: HTMX partial
- Background scheduler in app.py runs every 10 minutes
- 31 unit tests covering DB ops, clustering, parsing, dedup, and scan

Fixes #1011

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/dashboard/app.py

@@ -45,6 +45,7 @@ from dashboard.routes.models import api_router as models_api_router
 from dashboard.routes.models import router as models_router
 from dashboard.routes.quests import router as quests_router
 from dashboard.routes.scorecards import router as scorecards_router
+from dashboard.routes.skills import router as skills_router
 from dashboard.routes.spark import router as spark_router
 from dashboard.routes.system import router as system_router
 from dashboard.routes.tasks import router as tasks_router
@@ -218,6 +219,32 @@ async def _loop_qa_scheduler() -> None:
         await asyncio.sleep(interval)
 
 
+_SKILL_DISCOVERY_INTERVAL = 600  # 10 minutes
+
+
+async def _skill_discovery_scheduler() -> None:
+    """Background task: scan session logs for reusable skill patterns."""
+    await asyncio.sleep(20)  # Stagger after other schedulers
+
+    while True:
+        try:
+            from timmy.skill_discovery import get_skill_discovery_engine
+
+            engine = get_skill_discovery_engine()
+            discovered = await engine.scan()
+            if discovered:
+                logger.info(
+                    "Skill discovery: found %d new skill(s)",
+                    len(discovered),
+                )
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            logger.error("Skill discovery scheduler error: %s", exc)
+
+        await asyncio.sleep(_SKILL_DISCOVERY_INTERVAL)
+
+
 _PRESENCE_POLL_SECONDS = 30
 _PRESENCE_INITIAL_DELAY = 3
 
@@ -380,6 +407,7 @@ def _startup_background_tasks() -> list[asyncio.Task]:
         asyncio.create_task(_loop_qa_scheduler()),
         asyncio.create_task(_presence_watcher()),
         asyncio.create_task(_start_chat_integrations_background()),
+        asyncio.create_task(_skill_discovery_scheduler()),
     ]
 
 
@@ -631,6 +659,7 @@ app.include_router(tower_router)
 app.include_router(daily_run_router)
 app.include_router(quests_router)
 app.include_router(scorecards_router)
+app.include_router(skills_router)
 
 
 @app.websocket("/ws")

src/dashboard/routes/skills.py

@@ -0,0 +1,82 @@
+"""Skill Discovery routes — view and manage auto-discovered skills."""
+
+import logging
+
+from fastapi import APIRouter, Form, HTTPException, Request
+from fastapi.responses import HTMLResponse
+
+from dashboard.templating import templates
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/skills", tags=["skills"])
+
+
+@router.get("", response_class=HTMLResponse)
+async def skills_page(request: Request):
+    """Main skill discovery page."""
+    from timmy.skill_discovery import get_skill_discovery_engine
+
+    engine = get_skill_discovery_engine()
+    skills = engine.list_skills(limit=50)
+    counts = engine.skill_count()
+    return templates.TemplateResponse(
+        request,
+        "skills.html",
+        {"skills": skills, "counts": counts},
+    )
+
+
+@router.get("/list", response_class=HTMLResponse)
+async def skills_list_partial(request: Request, status: str = ""):
+    """HTMX partial: return skill list for polling."""
+    from timmy.skill_discovery import get_skill_discovery_engine
+
+    engine = get_skill_discovery_engine()
+    skills = engine.list_skills(status=status or None, limit=50)
+    counts = engine.skill_count()
+    return templates.TemplateResponse(
+        request,
+        "partials/skills_list.html",
+        {"skills": skills, "counts": counts},
+    )
+
+
+@router.post("/{skill_id}/status", response_class=HTMLResponse)
+async def update_skill_status(request: Request, skill_id: str, status: str = Form(...)):
+    """Update a skill's status (confirm / reject / archive)."""
+    from timmy.skill_discovery import get_skill_discovery_engine
+
+    engine = get_skill_discovery_engine()
+    if not engine.update_status(skill_id, status):
+        raise HTTPException(status_code=400, detail=f"Invalid status: {status}")
+
+    skills = engine.list_skills(limit=50)
+    counts = engine.skill_count()
+    return templates.TemplateResponse(
+        request,
+        "partials/skills_list.html",
+        {"skills": skills, "counts": counts},
+    )
+
+
+@router.post("/scan", response_class=HTMLResponse)
+async def trigger_scan(request: Request):
+    """Manually trigger a skill discovery scan."""
+    from timmy.skill_discovery import get_skill_discovery_engine
+
+    engine = get_skill_discovery_engine()
+    try:
+        discovered = await engine.scan()
+        msg = f"Scan complete: {len(discovered)} new skill(s) found."
+    except Exception as exc:
+        logger.warning("Manual skill scan failed: %s", exc)
+        msg = f"Scan failed: {exc}"
+
+    skills = engine.list_skills(limit=50)
+    counts = engine.skill_count()
+    return templates.TemplateResponse(
+        request,
+        "partials/skills_list.html",
+        {"skills": skills, "counts": counts, "scan_message": msg},
+    )

src/dashboard/templates/partials/skills_list.html

@@ -0,0 +1,74 @@
+{% if scan_message is defined and scan_message %}
+<div class="alert alert-info mb-3" style="border-color: var(--green); background: var(--bg-card); color: var(--text);">
+  {{ scan_message }}
+</div>
+{% endif %}
+
+{% if skills %}
+<div class="table-responsive">
+  <table class="table table-sm" style="color: var(--text);">
+    <thead>
+      <tr style="color: var(--text-dim); border-bottom: 1px solid var(--border);">
+        <th>Name</th>
+        <th>Category</th>
+        <th>Confidence</th>
+        <th>Status</th>
+        <th>Discovered</th>
+        <th>Actions</th>
+      </tr>
+    </thead>
+    <tbody>
+      {% for skill in skills %}
+      <tr style="border-bottom: 1px solid var(--border);">
+        <td>
+          <strong>{{ skill.name }}</strong>
+          {% if skill.description %}
+          <br><small class="mc-muted">{{ skill.description[:100] }}</small>
+          {% endif %}
+        </td>
+        <td><span class="badge" style="background: var(--bg-panel); color: var(--text-dim);">{{ skill.category }}</span></td>
+        <td>
+          {% set conf = skill.confidence * 100 %}
+          <span style="color: {% if conf >= 80 %}var(--green){% elif conf >= 60 %}var(--amber){% else %}var(--red){% endif %};">
+            {{ "%.0f"|format(conf) }}%
+          </span>
+        </td>
+        <td>
+          {% if skill.status == 'confirmed' %}
+            <span style="color: var(--green);">confirmed</span>
+          {% elif skill.status == 'rejected' %}
+            <span style="color: var(--red);">rejected</span>
+          {% elif skill.status == 'archived' %}
+            <span class="mc-muted">archived</span>
+          {% else %}
+            <span style="color: var(--amber);">discovered</span>
+          {% endif %}
+        </td>
+        <td class="mc-muted">{{ skill.created_at[:10] if skill.created_at else '' }}</td>
+        <td>
+          {% if skill.status == 'discovered' %}
+          <form style="display:inline;" hx-post="/skills/{{ skill.id }}/status" hx-target="#skills-list" hx-swap="innerHTML">
+            <input type="hidden" name="status" value="confirmed">
+            <button type="submit" class="btn btn-sm btn-outline-success" title="Confirm">&#10003;</button>
+          </form>
+          <form style="display:inline;" hx-post="/skills/{{ skill.id }}/status" hx-target="#skills-list" hx-swap="innerHTML">
+            <input type="hidden" name="status" value="rejected">
+            <button type="submit" class="btn btn-sm btn-outline-danger" title="Reject">&#10007;</button>
+          </form>
+          {% elif skill.status == 'confirmed' %}
+          <form style="display:inline;" hx-post="/skills/{{ skill.id }}/status" hx-target="#skills-list" hx-swap="innerHTML">
+            <input type="hidden" name="status" value="archived">
+            <button type="submit" class="btn btn-sm btn-outline-secondary" title="Archive">&#9744;</button>
+          </form>
+          {% endif %}
+        </td>
+      </tr>
+      {% endfor %}
+    </tbody>
+  </table>
+</div>
+{% else %}
+<div class="mc-muted text-center py-4">
+  No skills discovered yet. Click "Scan Now" to analyze recent activity.
+</div>
+{% endif %}

src/dashboard/templates/skills.html

@@ -0,0 +1,38 @@
+{% extends "base.html" %}
+
+{% block title %}Skill Discovery - Timmy Time{% endblock %}
+
+{% block extra_styles %}{% endblock %}
+
+{% block content %}
+<div class="py-3">
+
+  {% from "macros.html" import panel %}
+
+  {% call panel("SKILL DISCOVERY", id="skills-panel") %}
+    <div class="d-flex justify-content-between align-items-center mb-3">
+      <div>
+        <span class="mc-muted">
+          Discovered: {{ counts.get('discovered', 0) }} |
+          Confirmed: {{ counts.get('confirmed', 0) }} |
+          Archived: {{ counts.get('archived', 0) }}
+        </span>
+      </div>
+      <button class="btn btn-sm btn-outline-light"
+              hx-post="/skills/scan"
+              hx-target="#skills-list"
+              hx-swap="innerHTML">
+        Scan Now
+      </button>
+    </div>
+
+    <div id="skills-list"
+         hx-get="/skills/list"
+         hx-trigger="every 30s"
+         hx-swap="innerHTML">
+      {% include "partials/skills_list.html" %}
+    </div>
+  {% endcall %}
+
+</div>
+{% endblock %}

src/timmy/skill_discovery.py

@@ -0,0 +1,495 @@
+"""Automated Skill Discovery Pipeline.
+
+Monitors the agent's session logs for high-confidence successful outcomes,
+uses the LLM router to deconstruct successful action sequences into
+reusable skill templates, and stores discovered skills with metadata.
+
+Notifies the dashboard when new skills are crystallized.
+"""
+
+import json
+import logging
+import sqlite3
+import uuid
+from collections.abc import Generator
+from contextlib import closing, contextmanager
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Database
+# ---------------------------------------------------------------------------
+
+DB_PATH = Path(settings.repo_root) / "data" / "skills.db"
+
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS discovered_skills (
+    id TEXT PRIMARY KEY,
+    name TEXT NOT NULL,
+    description TEXT DEFAULT '',
+    category TEXT DEFAULT 'general',
+    source_entries TEXT DEFAULT '[]',
+    template TEXT DEFAULT '',
+    confidence REAL DEFAULT 0.0,
+    status TEXT DEFAULT 'discovered',
+    created_at TEXT DEFAULT (datetime('now')),
+    updated_at TEXT DEFAULT (datetime('now'))
+);
+CREATE INDEX IF NOT EXISTS idx_skills_status ON discovered_skills(status);
+CREATE INDEX IF NOT EXISTS idx_skills_category ON discovered_skills(category);
+CREATE INDEX IF NOT EXISTS idx_skills_created ON discovered_skills(created_at);
+"""
+
+VALID_STATUSES = {"discovered", "confirmed", "rejected", "archived"}
+
+
+@contextmanager
+def _get_db() -> Generator[sqlite3.Connection, None, None]:
+    DB_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with closing(sqlite3.connect(str(DB_PATH))) as conn:
+        conn.row_factory = sqlite3.Row
+        conn.execute(f"PRAGMA busy_timeout = {settings.db_busy_timeout_ms}")
+        conn.executescript(_SCHEMA)
+        yield conn
+
+
+# ---------------------------------------------------------------------------
+# Data model
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class DiscoveredSkill:
+    """A skill extracted from successful agent actions."""
+
+    id: str = field(default_factory=lambda: f"skill_{uuid.uuid4().hex[:12]}")
+    name: str = ""
+    description: str = ""
+    category: str = "general"
+    source_entries: list[dict] = field(default_factory=list)
+    template: str = ""
+    confidence: float = 0.0
+    status: str = "discovered"
+    created_at: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
+    updated_at: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "id": self.id,
+            "name": self.name,
+            "description": self.description,
+            "category": self.category,
+            "source_entries": self.source_entries,
+            "template": self.template,
+            "confidence": self.confidence,
+            "status": self.status,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Prompt template for LLM analysis
+# ---------------------------------------------------------------------------
+
+_ANALYSIS_PROMPT = """\
+You are a skill extraction engine. Analyze the following sequence of \
+successful agent actions and extract a reusable skill template.
+
+Actions:
+{actions}
+
+Respond with a JSON object containing:
+- "name": short skill name (2-5 words)
+- "description": one-sentence description of what this skill does
+- "category": one of "research", "coding", "devops", "communication", "analysis", "general"
+- "template": a step-by-step template that generalizes this action sequence
+- "confidence": your confidence that this is a genuinely reusable skill (0.0-1.0)
+
+Respond ONLY with valid JSON, no markdown fences or extra text."""
+
+
+# ---------------------------------------------------------------------------
+# Core engine
+# ---------------------------------------------------------------------------
+
+
+class SkillDiscoveryEngine:
+    """Scans session logs for successful action patterns and extracts skills."""
+
+    def __init__(
+        self,
+        confidence_threshold: float = 0.7,
+        min_actions: int = 2,
+    ):
+        self.confidence_threshold = confidence_threshold
+        self.min_actions = min_actions
+
+    # -- Public API ---------------------------------------------------------
+
+    async def scan(self) -> list[DiscoveredSkill]:
+        """Scan recent session logs and discover new skills.
+
+        Returns a list of newly discovered skills.
+        """
+        entries = self._load_recent_successful_actions()
+        if len(entries) < self.min_actions:
+            logger.debug(
+                "Skill discovery: only %d actions found (need %d), skipping",
+                len(entries),
+                self.min_actions,
+            )
+            return []
+
+        # Group entries into action sequences (tool calls clustered together)
+        sequences = self._cluster_action_sequences(entries)
+        discovered: list[DiscoveredSkill] = []
+
+        for seq in sequences:
+            if len(seq) < self.min_actions:
+                continue
+
+            skill = await self._analyze_sequence(seq)
+            if skill and skill.confidence >= self.confidence_threshold:
+                # Check for duplicates
+                if not self._is_duplicate(skill):
+                    self._save_skill(skill)
+                    await self._notify(skill)
+                    discovered.append(skill)
+                    logger.info(
+                        "Discovered skill: %s (confidence=%.2f)",
+                        skill.name,
+                        skill.confidence,
+                    )
+
+        return discovered
+
+    def list_skills(
+        self,
+        status: str | None = None,
+        limit: int = 50,
+    ) -> list[dict[str, Any]]:
+        """Return discovered skills from the database."""
+        with _get_db() as conn:
+            if status and status in VALID_STATUSES:
+                rows = conn.execute(
+                    "SELECT * FROM discovered_skills WHERE status = ? "
+                    "ORDER BY created_at DESC LIMIT ?",
+                    (status, limit),
+                ).fetchall()
+            else:
+                rows = conn.execute(
+                    "SELECT * FROM discovered_skills ORDER BY created_at DESC LIMIT ?",
+                    (limit,),
+                ).fetchall()
+        return [dict(r) for r in rows]
+
+    def get_skill(self, skill_id: str) -> dict[str, Any] | None:
+        """Get a single skill by ID."""
+        with _get_db() as conn:
+            row = conn.execute(
+                "SELECT * FROM discovered_skills WHERE id = ?",
+                (skill_id,),
+            ).fetchone()
+        return dict(row) if row else None
+
+    def update_status(self, skill_id: str, new_status: str) -> bool:
+        """Update a skill's status (confirm, reject, archive)."""
+        if new_status not in VALID_STATUSES:
+            return False
+        with _get_db() as conn:
+            conn.execute(
+                "UPDATE discovered_skills SET status = ?, updated_at = ? WHERE id = ?",
+                (new_status, datetime.now(UTC).isoformat(), skill_id),
+            )
+            conn.commit()
+        return True
+
+    def skill_count(self) -> dict[str, int]:
+        """Return counts of skills by status."""
+        with _get_db() as conn:
+            rows = conn.execute(
+                "SELECT status, COUNT(*) as cnt FROM discovered_skills GROUP BY status"
+            ).fetchall()
+        return {r["status"]: r["cnt"] for r in rows}
+
+    # -- Internal -----------------------------------------------------------
+
+    def _load_recent_successful_actions(self, limit: int = 100) -> list[dict]:
+        """Load recent successful tool calls from session logs."""
+        try:
+            from timmy.session_logger import get_session_logger
+
+            sl = get_session_logger()
+            entries = sl.get_recent_entries(limit=limit)
+            # Filter for successful tool calls and high-confidence messages
+            return [
+                e
+                for e in entries
+                if (e.get("type") == "tool_call")
+                or (
+                    e.get("type") == "message"
+                    and e.get("role") == "timmy"
+                    and (e.get("confidence") or 0) >= 0.7
+                )
+            ]
+        except Exception as exc:
+            logger.warning("Failed to load session entries: %s", exc)
+            return []
+
+    def _cluster_action_sequences(
+        self,
+        entries: list[dict],
+        max_gap_seconds: int = 300,
+    ) -> list[list[dict]]:
+        """Group entries into sequences based on temporal proximity."""
+        if not entries:
+            return []
+
+        from datetime import datetime as dt
+
+        sequences: list[list[dict]] = []
+        current_seq: list[dict] = [entries[0]]
+
+        for entry in entries[1:]:
+            try:
+                prev_ts = dt.fromisoformat(current_seq[-1].get("timestamp", ""))
+                curr_ts = dt.fromisoformat(entry.get("timestamp", ""))
+                gap = abs((curr_ts - prev_ts).total_seconds())
+            except (ValueError, TypeError):
+                gap = max_gap_seconds + 1
+
+            if gap <= max_gap_seconds:
+                current_seq.append(entry)
+            else:
+                if current_seq:
+                    sequences.append(current_seq)
+                current_seq = [entry]
+
+        if current_seq:
+            sequences.append(current_seq)
+
+        return sequences
+
+    async def _analyze_sequence(self, sequence: list[dict]) -> DiscoveredSkill | None:
+        """Use the LLM router to analyze an action sequence."""
+        actions_text = self._format_actions(sequence)
+        prompt = _ANALYSIS_PROMPT.format(actions=actions_text)
+
+        try:
+            from infrastructure.router.cascade import get_router
+
+            router = get_router()
+            response = await router.complete(
+                messages=[
+                    {
+                        "role": "system",
+                        "content": "You extract reusable skills from agent actions.",
+                    },
+                    {"role": "user", "content": prompt},
+                ],
+            )
+            content = response.get("content", "")
+            return self._parse_llm_response(content, sequence)
+        except Exception as exc:
+            logger.warning("LLM analysis failed, using heuristic: %s", exc)
+            return self._heuristic_extraction(sequence)
+
+    def _format_actions(self, sequence: list[dict]) -> str:
+        """Format action sequence for the LLM prompt."""
+        lines = []
+        for i, entry in enumerate(sequence, 1):
+            etype = entry.get("type", "unknown")
+            if etype == "tool_call":
+                tool = entry.get("tool", "unknown")
+                result = (entry.get("result") or "")[:200]
+                lines.append(f"{i}. Tool: {tool} → {result}")
+            elif etype == "message":
+                content = (entry.get("content") or "")[:200]
+                lines.append(f"{i}. Response: {content}")
+            elif etype == "decision":
+                decision = (entry.get("decision") or "")[:200]
+                lines.append(f"{i}. Decision: {decision}")
+        return "\n".join(lines)
+
+    def _parse_llm_response(
+        self,
+        content: str,
+        source_entries: list[dict],
+    ) -> DiscoveredSkill | None:
+        """Parse LLM JSON response into a DiscoveredSkill."""
+        try:
+            # Strip markdown fences if present
+            cleaned = content.strip()
+            if cleaned.startswith("```"):
+                cleaned = cleaned.split("\n", 1)[1] if "\n" in cleaned else cleaned[3:]
+                if cleaned.endswith("```"):
+                    cleaned = cleaned[:-3]
+                cleaned = cleaned.strip()
+
+            data = json.loads(cleaned)
+            return DiscoveredSkill(
+                name=data.get("name", "Unnamed Skill"),
+                description=data.get("description", ""),
+                category=data.get("category", "general"),
+                template=data.get("template", ""),
+                confidence=float(data.get("confidence", 0.0)),
+                source_entries=source_entries[:5],  # Keep first 5 for reference
+            )
+        except (json.JSONDecodeError, ValueError, TypeError) as exc:
+            logger.debug("Failed to parse LLM response: %s", exc)
+            return None
+
+    def _heuristic_extraction(self, sequence: list[dict]) -> DiscoveredSkill | None:
+        """Fallback: extract skill from action patterns without LLM."""
+        tool_calls = [e for e in sequence if e.get("type") == "tool_call"]
+        if not tool_calls:
+            return None
+
+        # Name from the dominant tool
+        tool_names = [e.get("tool", "unknown") for e in tool_calls]
+        dominant_tool = max(set(tool_names), key=tool_names.count)
+
+        # Simple template from the tool sequence
+        steps = []
+        for i, tc in enumerate(tool_calls[:10], 1):
+            steps.append(f"Step {i}: Use {tc.get('tool', 'unknown')}")
+
+        return DiscoveredSkill(
+            name=f"{dominant_tool.replace('_', ' ').title()} Pattern",
+            description=f"Automated pattern using {dominant_tool} ({len(tool_calls)} steps)",
+            category="general",
+            template="\n".join(steps),
+            confidence=0.5,  # Lower confidence for heuristic
+            source_entries=sequence[:5],
+        )
+
+    def _is_duplicate(self, skill: DiscoveredSkill) -> bool:
+        """Check if a similar skill already exists."""
+        with _get_db() as conn:
+            rows = conn.execute(
+                "SELECT name FROM discovered_skills WHERE name = ? AND status != 'rejected'",
+                (skill.name,),
+            ).fetchall()
+        return len(rows) > 0
+
+    def _save_skill(self, skill: DiscoveredSkill) -> None:
+        """Persist a discovered skill to the database."""
+        with _get_db() as conn:
+            conn.execute(
+                """INSERT INTO discovered_skills
+                   (id, name, description, category, source_entries,
+                    template, confidence, status, created_at, updated_at)
+                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+                (
+                    skill.id,
+                    skill.name,
+                    skill.description,
+                    skill.category,
+                    json.dumps(skill.source_entries),
+                    skill.template,
+                    skill.confidence,
+                    skill.status,
+                    skill.created_at,
+                    skill.updated_at,
+                ),
+            )
+            conn.commit()
+
+    def _write_skill_file(self, skill: DiscoveredSkill) -> Path:
+        """Write a skill template to the skills/ directory."""
+        skills_dir = Path(settings.repo_root) / "skills" / "discovered"
+        skills_dir.mkdir(parents=True, exist_ok=True)
+
+        filename = skill.name.lower().replace(" ", "_") + ".md"
+        filepath = skills_dir / filename
+
+        content = f"""# {skill.name}
+
+**Category:** {skill.category}
+**Confidence:** {skill.confidence:.0%}
+**Discovered:** {skill.created_at[:10]}
+**Status:** {skill.status}
+
+## Description
+
+{skill.description}
+
+## Template
+
+{skill.template}
+"""
+        filepath.write_text(content)
+        logger.info("Wrote skill file: %s", filepath)
+        return filepath
+
+    async def _notify(self, skill: DiscoveredSkill) -> None:
+        """Notify the dashboard about a newly discovered skill."""
+        # Push notification
+        try:
+            from infrastructure.notifications.push import notifier
+
+            notifier.notify(
+                title="Skill Discovered",
+                message=f"{skill.name} (confidence: {skill.confidence:.0%})",
+                category="system",
+            )
+        except Exception as exc:
+            logger.debug("Push notification failed: %s", exc)
+
+        # WebSocket broadcast
+        try:
+            from infrastructure.ws_manager.handler import ws_manager
+
+            await ws_manager.broadcast(
+                "skill_discovered",
+                {
+                    "id": skill.id,
+                    "name": skill.name,
+                    "confidence": skill.confidence,
+                    "category": skill.category,
+                },
+            )
+        except Exception as exc:
+            logger.debug("WebSocket broadcast failed: %s", exc)
+
+        # Event bus
+        try:
+            from infrastructure.events.bus import Event, get_event_bus
+
+            await get_event_bus().publish(
+                Event(
+                    type="skill.discovered",
+                    source="skill_discovery",
+                    data=skill.to_dict(),
+                )
+            )
+        except Exception as exc:
+            logger.debug("Event bus publish failed: %s", exc)
+
+        # Write skill file to skills/ directory
+        try:
+            self._write_skill_file(skill)
+        except Exception as exc:
+            logger.debug("Skill file write failed: %s", exc)
+
+
+# ---------------------------------------------------------------------------
+# Singleton
+# ---------------------------------------------------------------------------
+
+_engine: SkillDiscoveryEngine | None = None
+
+
+def get_skill_discovery_engine() -> SkillDiscoveryEngine:
+    """Get or create the global skill discovery engine."""
+    global _engine
+    if _engine is None:
+        _engine = SkillDiscoveryEngine()
+    return _engine

tests/unit/test_skill_discovery.py

@@ -0,0 +1,410 @@
+"""Unit tests for the skill discovery pipeline.
+
+Tests the discovery engine's core logic: action clustering, skill extraction,
+database persistence, deduplication, and status management.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timedelta
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from timmy.skill_discovery import (
+    DiscoveredSkill,
+    SkillDiscoveryEngine,
+)
+
+
+@pytest.fixture
+def engine():
+    """Create a fresh SkillDiscoveryEngine for each test."""
+    return SkillDiscoveryEngine(confidence_threshold=0.7, min_actions=2)
+
+
+@pytest.fixture(autouse=True)
+def temp_db(tmp_path, monkeypatch):
+    """Use a temporary database for each test."""
+    db_path = tmp_path / "skills.db"
+    monkeypatch.setattr("timmy.skill_discovery.DB_PATH", db_path)
+    return db_path
+
+
+# ---------------------------------------------------------------------------
+# DiscoveredSkill dataclass
+# ---------------------------------------------------------------------------
+
+
+class TestDiscoveredSkill:
+    def test_defaults(self):
+        skill = DiscoveredSkill()
+        assert skill.name == ""
+        assert skill.status == "discovered"
+        assert skill.confidence == 0.0
+        assert skill.id.startswith("skill_")
+
+    def test_to_dict(self):
+        skill = DiscoveredSkill(name="Test Skill", confidence=0.85)
+        d = skill.to_dict()
+        assert d["name"] == "Test Skill"
+        assert d["confidence"] == 0.85
+        assert "id" in d
+        assert "created_at" in d
+
+    def test_custom_fields(self):
+        skill = DiscoveredSkill(
+            name="Code Review",
+            category="coding",
+            confidence=0.92,
+            template="Step 1: Read code\nStep 2: Analyze",
+        )
+        assert skill.category == "coding"
+        assert "Step 1" in skill.template
+
+
+# ---------------------------------------------------------------------------
+# Database operations
+# ---------------------------------------------------------------------------
+
+
+class TestDatabase:
+    def test_save_and_list(self, engine):
+        skill = DiscoveredSkill(
+            name="Git Workflow",
+            description="Automates git operations",
+            category="devops",
+            confidence=0.88,
+        )
+        engine._save_skill(skill)
+        skills = engine.list_skills()
+        assert len(skills) == 1
+        assert skills[0]["name"] == "Git Workflow"
+        assert skills[0]["category"] == "devops"
+
+    def test_list_by_status(self, engine):
+        s1 = DiscoveredSkill(name="Skill A", status="discovered")
+        s2 = DiscoveredSkill(name="Skill B", status="confirmed")
+        engine._save_skill(s1)
+        engine._save_skill(s2)
+
+        discovered = engine.list_skills(status="discovered")
+        assert len(discovered) == 1
+        assert discovered[0]["name"] == "Skill A"
+
+        confirmed = engine.list_skills(status="confirmed")
+        assert len(confirmed) == 1
+        assert confirmed[0]["name"] == "Skill B"
+
+    def test_get_skill(self, engine):
+        skill = DiscoveredSkill(name="Find Me")
+        engine._save_skill(skill)
+        found = engine.get_skill(skill.id)
+        assert found is not None
+        assert found["name"] == "Find Me"
+
+    def test_get_skill_not_found(self, engine):
+        assert engine.get_skill("nonexistent") is None
+
+    def test_update_status(self, engine):
+        skill = DiscoveredSkill(name="Status Test")
+        engine._save_skill(skill)
+        assert engine.update_status(skill.id, "confirmed")
+        found = engine.get_skill(skill.id)
+        assert found["status"] == "confirmed"
+
+    def test_update_invalid_status(self, engine):
+        skill = DiscoveredSkill(name="Invalid Status")
+        engine._save_skill(skill)
+        assert not engine.update_status(skill.id, "bogus")
+
+    def test_skill_count(self, engine):
+        engine._save_skill(DiscoveredSkill(name="A", status="discovered"))
+        engine._save_skill(DiscoveredSkill(name="B", status="discovered"))
+        engine._save_skill(DiscoveredSkill(name="C", status="confirmed"))
+        counts = engine.skill_count()
+        assert counts["discovered"] == 2
+        assert counts["confirmed"] == 1
+
+    def test_list_limit(self, engine):
+        for i in range(5):
+            engine._save_skill(DiscoveredSkill(name=f"Skill {i}"))
+        assert len(engine.list_skills(limit=3)) == 3
+
+
+# ---------------------------------------------------------------------------
+# Action clustering
+# ---------------------------------------------------------------------------
+
+
+class TestActionClustering:
+    def test_empty_entries(self, engine):
+        assert engine._cluster_action_sequences([]) == []
+
+    def test_single_sequence(self, engine):
+        now = datetime.now()
+        entries = [
+            {"type": "tool_call", "tool": "read", "timestamp": now.isoformat()},
+            {
+                "type": "tool_call",
+                "tool": "write",
+                "timestamp": (now + timedelta(seconds=30)).isoformat(),
+            },
+        ]
+        sequences = engine._cluster_action_sequences(entries)
+        assert len(sequences) == 1
+        assert len(sequences[0]) == 2
+
+    def test_split_by_gap(self, engine):
+        now = datetime.now()
+        entries = [
+            {"type": "tool_call", "tool": "read", "timestamp": now.isoformat()},
+            {
+                "type": "tool_call",
+                "tool": "write",
+                "timestamp": (now + timedelta(seconds=600)).isoformat(),
+            },
+        ]
+        sequences = engine._cluster_action_sequences(entries, max_gap_seconds=300)
+        assert len(sequences) == 2
+
+    def test_bad_timestamps(self, engine):
+        entries = [
+            {"type": "tool_call", "tool": "read", "timestamp": "not-a-date"},
+            {"type": "tool_call", "tool": "write", "timestamp": "also-bad"},
+        ]
+        sequences = engine._cluster_action_sequences(entries)
+        # Should still produce sequences (split on bad parse)
+        assert len(sequences) >= 1
+
+
+# ---------------------------------------------------------------------------
+# LLM response parsing
+# ---------------------------------------------------------------------------
+
+
+class TestLLMParsing:
+    def test_parse_valid_json(self, engine):
+        response = json.dumps(
+            {
+                "name": "API Search",
+                "description": "Searches APIs efficiently",
+                "category": "research",
+                "template": "1. Identify API\n2. Call endpoint",
+                "confidence": 0.85,
+            }
+        )
+        skill = engine._parse_llm_response(response, [])
+        assert skill is not None
+        assert skill.name == "API Search"
+        assert skill.confidence == 0.85
+        assert skill.category == "research"
+
+    def test_parse_with_markdown_fences(self, engine):
+        response = '```json\n{"name": "Fenced", "confidence": 0.9}\n```'
+        skill = engine._parse_llm_response(response, [])
+        assert skill is not None
+        assert skill.name == "Fenced"
+
+    def test_parse_invalid_json(self, engine):
+        assert engine._parse_llm_response("not json", []) is None
+
+    def test_parse_empty(self, engine):
+        assert engine._parse_llm_response("", []) is None
+
+
+# ---------------------------------------------------------------------------
+# Heuristic extraction
+# ---------------------------------------------------------------------------
+
+
+class TestHeuristicExtraction:
+    def test_extract_from_tool_calls(self, engine):
+        seq = [
+            {"type": "tool_call", "tool": "git_commit", "result": "ok"},
+            {"type": "tool_call", "tool": "git_push", "result": "ok"},
+            {"type": "tool_call", "tool": "git_commit", "result": "ok"},
+        ]
+        skill = engine._heuristic_extraction(seq)
+        assert skill is not None
+        assert "Git Commit" in skill.name
+        assert skill.confidence == 0.5
+
+    def test_extract_no_tool_calls(self, engine):
+        seq = [{"type": "message", "role": "user", "content": "hello"}]
+        assert engine._heuristic_extraction(seq) is None
+
+
+# ---------------------------------------------------------------------------
+# Deduplication
+# ---------------------------------------------------------------------------
+
+
+class TestDeduplication:
+    def test_not_duplicate(self, engine):
+        skill = DiscoveredSkill(name="Unique Skill")
+        assert not engine._is_duplicate(skill)
+
+    def test_is_duplicate(self, engine):
+        skill = DiscoveredSkill(name="Duplicate Check")
+        engine._save_skill(skill)
+        new_skill = DiscoveredSkill(name="Duplicate Check")
+        assert engine._is_duplicate(new_skill)
+
+    def test_rejected_not_duplicate(self, engine):
+        skill = DiscoveredSkill(name="Rejected Skill", status="rejected")
+        engine._save_skill(skill)
+        new_skill = DiscoveredSkill(name="Rejected Skill")
+        assert not engine._is_duplicate(new_skill)
+
+
+# ---------------------------------------------------------------------------
+# Format actions
+# ---------------------------------------------------------------------------
+
+
+class TestFormatActions:
+    def test_format_tool_call(self, engine):
+        seq = [{"type": "tool_call", "tool": "shell", "result": "output text"}]
+        text = engine._format_actions(seq)
+        assert "shell" in text
+        assert "output text" in text
+
+    def test_format_message(self, engine):
+        seq = [{"type": "message", "role": "timmy", "content": "I analyzed the code"}]
+        text = engine._format_actions(seq)
+        assert "I analyzed the code" in text
+
+    def test_format_decision(self, engine):
+        seq = [{"type": "decision", "decision": "Use async"}]
+        text = engine._format_actions(seq)
+        assert "Use async" in text
+
+
+# ---------------------------------------------------------------------------
+# Scan integration (mocked)
+# ---------------------------------------------------------------------------
+
+
+class TestScan:
+    @pytest.mark.asyncio
+    async def test_scan_too_few_actions(self, engine):
+        with patch.object(engine, "_load_recent_successful_actions", return_value=[]):
+            result = await engine.scan()
+            assert result == []
+
+    @pytest.mark.asyncio
+    async def test_scan_discovers_skill(self, engine):
+        now = datetime.now()
+        entries = [
+            {
+                "type": "tool_call",
+                "tool": "search",
+                "result": "found results",
+                "timestamp": now.isoformat(),
+            },
+            {
+                "type": "tool_call",
+                "tool": "analyze",
+                "result": "analysis complete",
+                "timestamp": (now + timedelta(seconds=10)).isoformat(),
+            },
+            {
+                "type": "tool_call",
+                "tool": "report",
+                "result": "report generated",
+                "timestamp": (now + timedelta(seconds=20)).isoformat(),
+            },
+        ]
+
+        llm_response = json.dumps(
+            {
+                "name": "Research Pipeline",
+                "description": "Search, analyze, and report",
+                "category": "research",
+                "template": "1. Search\n2. Analyze\n3. Report",
+                "confidence": 0.9,
+            }
+        )
+
+        with (
+            patch.object(engine, "_load_recent_successful_actions", return_value=entries),
+            patch(
+                "infrastructure.router.cascade.get_router",
+                return_value=MagicMock(complete=AsyncMock(return_value={"content": llm_response})),
+            ),
+            patch.object(engine, "_notify", new_callable=AsyncMock),
+            patch.object(engine, "_write_skill_file"),
+        ):
+            result = await engine.scan()
+            assert len(result) == 1
+            assert result[0].name == "Research Pipeline"
+            assert result[0].confidence == 0.9
+
+    @pytest.mark.asyncio
+    async def test_scan_skips_low_confidence(self, engine):
+        now = datetime.now()
+        entries = [
+            {
+                "type": "tool_call",
+                "tool": "a",
+                "result": "ok",
+                "timestamp": now.isoformat(),
+            },
+            {
+                "type": "tool_call",
+                "tool": "b",
+                "result": "ok",
+                "timestamp": (now + timedelta(seconds=10)).isoformat(),
+            },
+        ]
+
+        llm_response = json.dumps(
+            {"name": "Low Conf", "confidence": 0.3, "category": "general", "template": "..."}
+        )
+
+        with (
+            patch.object(engine, "_load_recent_successful_actions", return_value=entries),
+            patch(
+                "infrastructure.router.cascade.get_router",
+                return_value=MagicMock(complete=AsyncMock(return_value={"content": llm_response})),
+            ),
+        ):
+            result = await engine.scan()
+            assert result == []
+
+    @pytest.mark.asyncio
+    async def test_scan_falls_back_to_heuristic(self, engine):
+        engine.confidence_threshold = 0.4  # Lower for heuristic
+        now = datetime.now()
+        entries = [
+            {
+                "type": "tool_call",
+                "tool": "deploy",
+                "result": "ok",
+                "timestamp": now.isoformat(),
+            },
+            {
+                "type": "tool_call",
+                "tool": "deploy",
+                "result": "ok",
+                "timestamp": (now + timedelta(seconds=10)).isoformat(),
+            },
+        ]
+
+        with (
+            patch.object(engine, "_load_recent_successful_actions", return_value=entries),
+            patch(
+                "infrastructure.router.cascade.get_router",
+                return_value=MagicMock(
+                    complete=AsyncMock(side_effect=Exception("LLM unavailable"))
+                ),
+            ),
+            patch.object(engine, "_notify", new_callable=AsyncMock),
+            patch.object(engine, "_write_skill_file"),
+        ):
+            result = await engine.scan()
+            assert len(result) == 1
+            assert "Deploy" in result[0].name
+            assert result[0].confidence == 0.5