enhance auto-learn hook: multi-turn extraction, dedup, and auto-memory files

- Deep extraction every 5 turns: reads last 5 exchanges for debugging
  insights, workarounds, architectural patterns, and operational knowledge
- Single-turn extraction on every other turn (cheap, corrections/prefs only)
- State tracking per session: turn counter, content hashes for dedup
- Writes to both memory API/SQLite AND auto-memory markdown files
- Expanded judge prompt: now catches debugging (error→cause→fix),
  workarounds, and operational knowledge — not just corrections/facts
- Auto-cleanup of state files older than 24 hours

hooks/auto-learn.py

@@ -2,13 +2,23 @@
 """
 Stop hook (async): automatic learning extraction via haiku-as-judge.
 
-After each Claude response, sends the user message + assistant response to
-haiku to detect corrections, preferences, decisions, or facts worth storing.
-If learning events are detected, stores them via the memory API (or SQLite fallback).
+After each Claude response, reads the recent conversation window and uses
+haiku to detect learnings worth persisting:
+  - User corrections, preferences, decisions, facts (original scope)
+  - Debugging insights: error → root cause → fix mappings
+  - Architectural patterns and workarounds discovered during work
+  - Service/tool-specific operational knowledge
+
+Features:
+  - Multi-turn context window (last 5 exchanges by default)
+  - State tracking to avoid duplicate extraction
+  - Writes to memory API/SQLite AND auto-memory markdown files
+  - Throttled deep extraction: full window every ~5 turns, single-turn otherwise
 
 Runs with async: true — does NOT block the user.
 """
 
+import hashlib
 import io
 import json
 import logging
@@ -18,13 +28,24 @@ import subprocess
 import sys
 import urllib.error
 import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
 
 logger = logging.getLogger(__name__)
 
 API_BASE_URL = os.environ.get("MEMORY_API_URL") or os.environ.get("CLAUDE_MEMORY_API_URL", "")
 API_KEY = os.environ.get("MEMORY_API_KEY") or os.environ.get("CLAUDE_MEMORY_API_KEY", "")
 
-JUDGE_PROMPT = """You are a memory extraction judge. Analyze this exchange between a user and an AI assistant.
+# How many turns between deep (multi-turn) extractions
+DEEP_EXTRACTION_INTERVAL = 5
+# Max exchanges to include in deep extraction
+DEEP_WINDOW_SIZE = 5
+# Max chars per message in the context window
+MAX_MSG_CHARS = 3000
+# State directory
+STATE_DIR = Path.home() / ".claude" / "auto-learn-state"
+
+SINGLE_TURN_PROMPT = """You are a memory extraction judge. Analyze this single exchange between a user and an AI assistant.
 
 USER MESSAGE:
 {user_message}
@@ -39,19 +60,152 @@ Your job: determine if any of these learning events occurred:
 4. FACT — user shared a durable fact about themselves, their team, tools, or environment
 
 If ANY learning event occurred, return JSON:
-{{"events": [{{"type": "correction|preference|decision|fact", "content": "concise fact to remember (one sentence)", "importance": 0.7, "expanded_keywords": "space-separated semantically related search terms for recall (minimum 5 words)", "supersedes": null}}]}}
+{{"events": [{{"type": "correction|preference|decision|fact", "content": "concise fact to remember (one sentence)", "importance": 0.7, "tags": "comma,separated,tags", "expanded_keywords": "space-separated semantically related search terms for recall (minimum 5 words)", "supersedes": null}}]}}
 
 If NO learning event occurred, return:
 {{"events": []}}
 
 Rules:
-- Only extract DURABLE facts, not transient task details
+- Only extract DURABLE facts, not transient task details ("fix this file", "run tests")
 - Corrections are highest value (0.8-0.9)
 - Be conservative — false negatives are better than false positives
-- "expanded_keywords" should include synonyms, related concepts, and adjacent topics that would help find this memory later
 - "supersedes" should be a search query to find the old outdated memory, or null
 - Return ONLY valid JSON, no other text"""
 
+DEEP_EXTRACTION_PROMPT = """You are a knowledge extraction system. Analyze this multi-turn conversation between a user and an AI assistant working on software engineering tasks.
+
+CONVERSATION (last {n_exchanges} exchanges):
+{conversation}
+
+Extract any DURABLE knowledge worth remembering across sessions. Look for:
+
+1. **CORRECTIONS** — user corrected a mistake or misunderstanding (importance: 0.8-0.9)
+2. **PREFERENCES** — user stated how they like things done (importance: 0.7-0.8)
+3. **DECISIONS** — architectural or design decisions reached (importance: 0.7-0.8)
+4. **FACTS** — durable facts about user, team, tools, environment (importance: 0.6-0.8)
+5. **DEBUGGING INSIGHTS** — error → root cause → fix patterns that would help next time (importance: 0.7-0.9)
+6. **WORKAROUNDS** — things that didn't work and what did instead (importance: 0.7-0.8)
+7. **OPERATIONAL KNOWLEDGE** — service-specific learnings, config gotchas, resource requirements (importance: 0.7-0.8)
+
+Return JSON:
+{{"events": [{{"type": "correction|preference|decision|fact|debugging|workaround|operational", "content": "concise knowledge to remember (1-3 sentences max)", "importance": 0.7, "tags": "comma,separated,relevant,tags", "expanded_keywords": "space-separated semantically related search terms for recall (minimum 5 words)", "supersedes": null}}]}}
+
+If NO durable knowledge was found, return:
+{{"events": []}}
+
+Rules:
+- Only extract DURABLE knowledge, not transient task context ("reading file X", "running command Y")
+- Don't extract things that are obvious from the codebase (file paths, function names)
+- DO extract: "X doesn't work because Y — use Z instead", "service A needs B config", "always do X before Y"
+- Merge related learnings into single events rather than splitting into tiny fragments
+- If a debugging session revealed the root cause of an issue, capture the error→cause→fix chain
+- "supersedes" should be a search query to find an old outdated memory this replaces, or null
+- Maximum 5 events per extraction — prioritize by importance
+- Return ONLY valid JSON, no other text"""
+
+
+def _get_state_path(session_id: str) -> Path:
+    """Get state file path for this session."""
+    STATE_DIR.mkdir(parents=True, exist_ok=True)
+    return STATE_DIR / f"{session_id}.json"
+
+
+def _load_state(session_id: str) -> dict:
+    """Load extraction state for this session."""
+    path = _get_state_path(session_id)
+    if path.exists():
+        try:
+            return json.loads(path.read_text())
+        except (json.JSONDecodeError, OSError):
+            pass
+    return {"turn_count": 0, "extracted_hashes": [], "last_deep_turn": 0}
+
+
+def _save_state(session_id: str, state: dict) -> None:
+    """Save extraction state for this session."""
+    path = _get_state_path(session_id)
+    try:
+        path.write_text(json.dumps(state))
+    except OSError:
+        pass
+
+
+def _cleanup_old_state() -> None:
+    """Remove state files older than 24 hours."""
+    if not STATE_DIR.exists():
+        return
+    now = datetime.now().timestamp()
+    try:
+        for f in STATE_DIR.iterdir():
+            if f.suffix == ".json" and (now - f.stat().st_mtime) > 86400:
+                f.unlink(missing_ok=True)
+    except OSError:
+        pass
+
+
+def _content_hash(content: str) -> str:
+    """Hash content for deduplication."""
+    return hashlib.sha256(content.encode()).hexdigest()[:16]
+
+
+def _parse_transcript(transcript_path: str, max_exchanges: int = 1) -> list[dict]:
+    """
+    Parse the transcript and return the last N exchanges as
+    [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...]
+    """
+    try:
+        MAX_TAIL_BYTES = max_exchanges * 100_000  # ~100KB per exchange should be plenty
+        with open(transcript_path, "rb") as f:
+            f.seek(0, io.SEEK_END)
+            size = f.tell()
+            f.seek(max(0, size - MAX_TAIL_BYTES))
+            tail = f.read().decode("utf-8", errors="replace")
+        lines = tail.split("\n")
+    except Exception:
+        return []
+
+    entries = []
+    for line in lines:
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            entry = json.loads(line)
+        except json.JSONDecodeError:
+            continue
+        role = entry.get("role", "")
+        if role not in ("user", "assistant"):
+            continue
+        content = entry.get("content", "")
+        if isinstance(content, list):
+            content = " ".join(
+                b.get("text", "") for b in content
+                if isinstance(b, dict) and b.get("type") == "text"
+            )
+        content = str(content)[:MAX_MSG_CHARS]
+        if content.strip():
+            entries.append({"role": role, "content": content})
+
+    # Extract the last N exchanges (user+assistant pairs)
+    # Walk backwards to find pairs
+    exchanges = []
+    i = len(entries) - 1
+    while i >= 0 and len(exchanges) < max_exchanges * 2:
+        exchanges.insert(0, entries[i])
+        i -= 1
+
+    # Trim to last N complete exchanges
+    result = []
+    pair_count = 0
+    for entry in reversed(exchanges):
+        result.insert(0, entry)
+        if entry["role"] == "user":
+            pair_count += 1
+        if pair_count >= max_exchanges:
+            break
+
+    return result
+
 
 def _api_request(method: str, path: str, body: dict | None = None) -> dict:
     url = f"{API_BASE_URL}{path}"
@@ -73,12 +227,10 @@ def _store_via_api(content, category, tags, importance, expanded_keywords):
 
 def _store_via_sqlite(content, category, tags, importance, expanded_keywords):
     import sqlite3
-    from datetime import datetime, timezone
 
     memory_home = os.environ.get("MEMORY_HOME", os.path.expanduser("~/.claude/claude-memory"))
     db_path = os.path.join(memory_home, "memory", "memory.db")
 
-    # Also check legacy path
     if not os.path.exists(db_path):
         legacy_db = os.path.join(os.path.expanduser("~/.claude/metaclaw"), "memory", "memory.db")
         if os.path.exists(legacy_db):
@@ -95,8 +247,119 @@ def _store_via_sqlite(content, category, tags, importance, expanded_keywords):
     conn.close()
 
 
+def _append_to_auto_memory(content: str, event_type: str) -> None:
+    """Append a learning to the auto-memory markdown file for the current project."""
+    # Find the project memory directory based on CWD
+    cwd = os.getcwd()
+    # Claude Code stores project memory at ~/.claude/projects/<escaped-path>/memory/
+    escaped = cwd.replace("/", "-")
+    if escaped.startswith("-"):
+        escaped = escaped[1:]  # Remove leading dash
+    memory_dir = Path.home() / ".claude" / "projects" / f"-{escaped}" / "memory"
+
+    if not memory_dir.exists():
+        # Try without the leading dash
+        memory_dir = Path.home() / ".claude" / "projects" / escaped / "memory"
+
+    if not memory_dir.exists():
+        return
+
+    auto_learn_file = memory_dir / "auto-learned.md"
+    now = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+
+    header = "# Auto-Learned Knowledge\n\nAutomatically extracted by the auto-learn hook. Review periodically and promote valuable entries to MEMORY.md.\n\n"
+
+    if not auto_learn_file.exists():
+        auto_learn_file.write_text(header)
+
+    # Append the new learning
+    with open(auto_learn_file, "a") as f:
+        f.write(f"- [{now}] **{event_type}**: {content}\n")
+
+
+def _call_judge(prompt: str) -> list[dict]:
+    """Call haiku as judge and return extracted events."""
+    try:
+        result = subprocess.run(
+            ["claude", "-p", prompt, "--model", "haiku"],
+            capture_output=True, text=True, timeout=45,
+            env={**os.environ, "CLAUDECODE": ""},
+        )
+        if result.returncode != 0:
+            return []
+        response_text = result.stdout.strip()
+        # Strip markdown code fences if present
+        if response_text.startswith("```"):
+            lines = response_text.split("\n")
+            lines = [l for l in lines if not l.strip().startswith("```")]
+            response_text = "\n".join(lines).strip()
+        judge_result = json.loads(response_text)
+        return judge_result.get("events", [])
+    except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError):
+        return []
+
+
+def _format_conversation(entries: list[dict]) -> str:
+    """Format conversation entries for the judge prompt."""
+    parts = []
+    for entry in entries:
+        role = "USER" if entry["role"] == "user" else "ASSISTANT"
+        parts.append(f"[{role}]: {entry['content']}")
+    return "\n\n".join(parts)
+
+
+def _store_events(events: list[dict], extracted_hashes: list[str]) -> list[str]:
+    """Store extracted events, return new hashes."""
+    category_map = {
+        "correction": "preferences",
+        "preference": "preferences",
+        "decision": "decisions",
+        "fact": "facts",
+        "debugging": "decisions",
+        "workaround": "decisions",
+        "operational": "facts",
+    }
+
+    new_hashes = []
+    for event in events:
+        content = event.get("content", "")
+        if not content:
+            continue
+
+        # Deduplication: skip if we've already extracted this
+        h = _content_hash(content)
+        if h in extracted_hashes:
+            continue
+
+        event_type = event.get("type", "fact")
+        importance = max(0.0, min(1.0, float(event.get("importance", 0.7))))
+        category = category_map.get(event_type, "facts")
+        tags = event.get("tags", f"auto-learned,{event_type}")
+        if "auto-learned" not in tags:
+            tags = f"auto-learned,{tags}"
+        expanded_keywords = event.get("expanded_keywords", "")
+
+        # Store to memory API or SQLite
+        try:
+            if API_KEY and API_BASE_URL:
+                _store_via_api(content, category, tags, importance, expanded_keywords)
+            else:
+                _store_via_sqlite(content, category, tags, importance, expanded_keywords)
+        except Exception:
+            pass
+
+        # Also append to auto-memory markdown
+        try:
+            _append_to_auto_memory(content, event_type)
+        except Exception:
+            pass
+
+        new_hashes.append(h)
+
+    return new_hashes
+
+
 def main() -> None:
-    # Graceful exit if claude CLI is not available
     if not shutil.which("claude"):
         return
 
@@ -109,100 +372,84 @@ def main() -> None:
         return
 
     transcript_path = ""
+    session_id = ""
     if isinstance(hook_input, dict):
         transcript_path = hook_input.get("transcript_path", "")
+        session_id = hook_input.get("session_id", "")
 
     if not transcript_path or not os.path.exists(transcript_path):
         return
 
-    user_message = ""
-    assistant_response = ""
-    try:
-        MAX_TAIL_BYTES = 50_000
-        with open(transcript_path, "rb") as f:
-            f.seek(0, io.SEEK_END)
-            size = f.tell()
-            f.seek(max(0, size - MAX_TAIL_BYTES))
-            tail = f.read().decode("utf-8", errors="replace")
-        lines = tail.split("\n")
+    # Derive session ID from transcript path if not provided
+    if not session_id:
+        session_id = hashlib.sha256(transcript_path.encode()).hexdigest()[:16]
 
-        for line in reversed(lines):
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                entry = json.loads(line)
-            except json.JSONDecodeError:
-                continue
-            role = entry.get("role", "")
-            content = entry.get("content", "")
-            if isinstance(content, list):
-                content = " ".join(
-                    b.get("text", "") for b in content
-                    if isinstance(b, dict) and b.get("type") == "text"
+    # Load state
+    state = _load_state(session_id)
+    state["turn_count"] = state.get("turn_count", 0) + 1
+    turn_count = state["turn_count"]
+    last_deep_turn = state.get("last_deep_turn", 0)
+    extracted_hashes = state.get("extracted_hashes", [])
+
+    # Decide: single-turn (cheap) or deep (multi-turn) extraction
+    turns_since_deep = turn_count - last_deep_turn
+    do_deep = turns_since_deep >= DEEP_EXTRACTION_INTERVAL
+
+    if do_deep:
+        # Deep extraction: read last N exchanges
+        entries = _parse_transcript(transcript_path, max_exchanges=DEEP_WINDOW_SIZE)
+        if len(entries) < 2:
+            _save_state(session_id, state)
+            return
+
+        # Count actual exchanges
+        n_exchanges = sum(1 for e in entries if e["role"] == "user")
+        conversation = _format_conversation(entries)
+        prompt = DEEP_EXTRACTION_PROMPT.format(
+            n_exchanges=n_exchanges,
+            conversation=conversation[:8000],  # Cap total context
         )
-            content = str(content)[:2000]
-            if role == "assistant" and not assistant_response:
-                assistant_response = content
-            elif role == "user" and not user_message:
-                user_message = content
-            if user_message and assistant_response:
-                break
-    except Exception:
-        return
-
-    if not user_message or len(user_message.strip()) < 10:
-        return
-
-    prompt = JUDGE_PROMPT.format(
-        user_message=user_message,
-        assistant_response=assistant_response[:1000],
-    )
-
-    try:
-        result = subprocess.run(
-            ["claude", "-p", prompt, "--model", "haiku"],
-            capture_output=True, text=True, timeout=30,
-            env={**os.environ, "CLAUDECODE": ""},
-        )
-        if result.returncode != 0:
-            return
-        response_text = result.stdout.strip()
-        if response_text.startswith("```"):
-            lines = response_text.split("\n")
-            lines = [l for l in lines if not l.strip().startswith("```")]
-            response_text = "\n".join(lines).strip()
-        judge_result = json.loads(response_text)
-        events = judge_result.get("events", [])
-        if not events:
-            return
-    except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError):
-        return
-
-    category_map = {
-        "correction": "preferences",
-        "preference": "preferences",
-        "decision": "decisions",
-        "fact": "facts",
-    }
-
-    for event in events:
-        content = event.get("content", "")
-        if not content:
-            continue
-        event_type = event.get("type", "fact")
-        importance = max(0.0, min(1.0, float(event.get("importance", 0.7))))
-        category = category_map.get(event_type, "facts")
-        tags = f"auto-learned,{event_type}"
-        expanded_keywords = event.get("expanded_keywords", "")
-
-        try:
-            if API_KEY and API_BASE_URL:
-                _store_via_api(content, category, tags, importance, expanded_keywords)
+        events = _call_judge(prompt)
+        state["last_deep_turn"] = turn_count
     else:
-                _store_via_sqlite(content, category, tags, importance, expanded_keywords)
-        except Exception:
-            pass  # Never crash the async hook
+        # Single-turn extraction: just the last exchange
+        entries = _parse_transcript(transcript_path, max_exchanges=1)
+        if len(entries) < 2:
+            _save_state(session_id, state)
+            return
+
+        user_msg = ""
+        assistant_msg = ""
+        for entry in entries:
+            if entry["role"] == "user":
+                user_msg = entry["content"]
+            elif entry["role"] == "assistant":
+                assistant_msg = entry["content"]
+
+        if not user_msg or len(user_msg.strip()) < 10:
+            _save_state(session_id, state)
+            return
+
+        prompt = SINGLE_TURN_PROMPT.format(
+            user_message=user_msg,
+            assistant_response=assistant_msg[:2000],
+        )
+        events = _call_judge(prompt)
+
+    # Store events
+    if events:
+        new_hashes = _store_events(events, extracted_hashes)
+        extracted_hashes.extend(new_hashes)
+        # Keep hash list bounded
+        if len(extracted_hashes) > 200:
+            extracted_hashes = extracted_hashes[-200:]
+        state["extracted_hashes"] = extracted_hashes
+
+    _save_state(session_id, state)
+
+    # Periodic cleanup of old state files
+    if turn_count % 20 == 0:
+        _cleanup_old_state()
 
 
 if __name__ == "__main__":