add fallback chain for judge: claude CLI → ollama → heuristic

- claude CLI: run from /tmp to avoid internet-mode-used marker prompts
- ollama: try small local models (qwen2.5:3b, llama3.2:3b, etc.)
- heuristic: pattern matching for corrections, preferences, decisions
- better JSON extraction: handles markdown fences and surrounding text

hooks/auto-learn.py

@@ -173,10 +173,12 @@ def _parse_transcript(transcript_path: str, max_exchanges: int = 1) -> list[dict
             entry = json.loads(line)
         except json.JSONDecodeError:
             continue
-        role = entry.get("role", "")
+        # Transcript format: role can be at top level or nested in message
+        msg = entry.get("message", entry)
+        role = msg.get("role", "") or entry.get("type", "")
         if role not in ("user", "assistant"):
             continue
-        content = entry.get("content", "")
+        content = msg.get("content", "")
         if isinstance(content, list):
             content = " ".join(
                 b.get("text", "") for b in content
@@ -277,26 +279,158 @@ def _append_to_auto_memory(content: str, event_type: str) -> None:
         f.write(f"- [{now}] **{event_type}**: {content}\n")
 
 
-def _call_judge(prompt: str) -> list[dict]:
-    """Call haiku as judge and return extracted events."""
+def _parse_llm_response(response_text: str) -> list[dict]:
+    """Parse LLM response text into events list."""
+    response_text = response_text.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()
+    # Try to extract JSON from the response
+    # Sometimes the LLM adds text before/after the JSON
+    start = response_text.find("{")
+    end = response_text.rfind("}") + 1
+    if start >= 0 and end > start:
+        response_text = response_text[start:end]
+    judge_result = json.loads(response_text)
+    return judge_result.get("events", [])
+
+
+def _call_judge_claude(prompt: str) -> list[dict] | None:
+    """Try claude CLI as judge. Returns None if unavailable."""
+    if not shutil.which("claude"):
+        return None
     try:
         result = subprocess.run(
             ["claude", "-p", prompt, "--model", "haiku"],
-            capture_output=True, text=True, timeout=45,
+            capture_output=True, text=True, timeout=60,
+            # Run from /tmp to avoid internet-mode-used marker prompts
+            # Clear CLAUDECODE to prevent recursion
+            cwd="/tmp",
             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", [])
+            return None
+        return _parse_llm_response(result.stdout)
     except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError):
-        return []
+        return None
+
+
+def _call_judge_ollama(prompt: str) -> list[dict] | None:
+    """Try local ollama as judge. Returns None if unavailable."""
+    ollama_url = os.environ.get("OLLAMA_HOST", "http://localhost:11434")
+    # Prefer small models for speed
+    models_to_try = ["qwen2.5:3b", "llama3.2:3b", "gemma2:2b", "phi3:mini"]
+    for model in models_to_try:
+        try:
+            data = json.dumps({
+                "model": model,
+                "prompt": prompt,
+                "stream": False,
+                "options": {"temperature": 0, "num_predict": 512},
+            }).encode()
+            req = urllib.request.Request(
+                f"{ollama_url}/api/generate",
+                data=data, method="POST",
+                headers={"Content-Type": "application/json"},
+            )
+            with urllib.request.urlopen(req, timeout=30) as resp:
+                result = json.loads(resp.read().decode())
+                return _parse_llm_response(result.get("response", ""))
+        except Exception:
+            continue
+    return None
+
+
+def _call_judge_heuristic(entries: list[dict]) -> list[dict]:
+    """
+    Heuristic fallback: extract learnings via pattern matching.
+    Less accurate than LLM but works without any external dependencies.
+    """
+    events = []
+    correction_patterns = [
+        "actually", "that's wrong", "no,", "not correct", "instead of",
+        "don't use", "never use", "always use", "the correct way",
+        "the issue was", "the problem was", "root cause",
+    ]
+    preference_patterns = [
+        "i prefer", "i like", "i want", "please always", "please never",
+        "remember to", "from now on", "going forward",
+    ]
+    decision_patterns = [
+        "let's go with", "we decided", "the approach is",
+        "we'll use", "switched to", "migrated to",
+    ]
+
+    for entry in entries:
+        if entry["role"] != "user":
+            continue
+        text_lower = entry["content"].lower()
+
+        for pattern in correction_patterns:
+            if pattern in text_lower:
+                # Extract the sentence containing the pattern
+                for sentence in entry["content"].replace("\n", ". ").split(". "):
+                    if pattern in sentence.lower() and len(sentence) > 20:
+                        events.append({
+                            "type": "correction",
+                            "content": sentence.strip()[:200],
+                            "importance": 0.8,
+                            "tags": "auto-learned,heuristic,correction",
+                            "expanded_keywords": " ".join(sentence.lower().split()[:10]),
+                        })
+                        break
+                break
+
+        for pattern in preference_patterns:
+            if pattern in text_lower:
+                for sentence in entry["content"].replace("\n", ". ").split(". "):
+                    if pattern in sentence.lower() and len(sentence) > 15:
+                        events.append({
+                            "type": "preference",
+                            "content": sentence.strip()[:200],
+                            "importance": 0.7,
+                            "tags": "auto-learned,heuristic,preference",
+                            "expanded_keywords": " ".join(sentence.lower().split()[:10]),
+                        })
+                        break
+                break
+
+        for pattern in decision_patterns:
+            if pattern in text_lower:
+                for sentence in entry["content"].replace("\n", ". ").split(". "):
+                    if pattern in sentence.lower() and len(sentence) > 20:
+                        events.append({
+                            "type": "decision",
+                            "content": sentence.strip()[:200],
+                            "importance": 0.7,
+                            "tags": "auto-learned,heuristic,decision",
+                            "expanded_keywords": " ".join(sentence.lower().split()[:10]),
+                        })
+                        break
+                break
+
+    return events[:5]  # Max 5 events
+
+
+def _call_judge(prompt: str, entries: list[dict] | None = None) -> list[dict]:
+    """Call judge with fallback chain: claude CLI → ollama → heuristic."""
+    # Try claude CLI first
+    result = _call_judge_claude(prompt)
+    if result is not None:
+        return result
+
+    # Try ollama
+    result = _call_judge_ollama(prompt)
+    if result is not None:
+        return result
+
+    # Fall back to heuristic (only for deep extraction with entries)
+    if entries:
+        return _call_judge_heuristic(entries)
+
+    return []
 
 
 def _format_conversation(entries: list[dict]) -> str:
@@ -409,7 +543,7 @@ def main() -> None:
             n_exchanges=n_exchanges,
             conversation=conversation[:8000],  # Cap total context
         )
-        events = _call_judge(prompt)
+        events = _call_judge(prompt, entries)
         state["last_deep_turn"] = turn_count
     else:
         # Single-turn extraction: just the last exchange
@@ -434,7 +568,7 @@ def main() -> None:
             user_message=user_msg,
             assistant_response=assistant_msg[:2000],
         )
-        events = _call_judge(prompt)
+        events = _call_judge(prompt, entries)
 
     # Store events
     if events: