Add Claude Code plugin scaffold for single-repo install
Consolidates plugin components (hooks, commands, skills, MCP config) into this repo so it can be installed with: claude plugins install github:ViktorBarzin/claude-memory-mcp - .claude-plugin/plugin.json: manifest with all hook events - mcp/memory-mcp.json: MCP server config using existing src/ - hooks/: compaction survival, auto-recall, auto-learn, auto-approve - commands/: /remember and /recall slash commands - skills/: memory-management skill - Bump MCP server to v2.0.0 with metaclaw migration fallback - Update README with quick install and plugin hooks docs
This commit is contained in:
Viktor Barzin
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66bb407bae
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12 changed files with 653 additions and 2 deletions
209
hooks/auto-learn.py
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209
hooks/auto-learn.py
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#!/usr/bin/env python3
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"""
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Stop hook (async): automatic learning extraction via haiku-as-judge.
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After each Claude response, sends the user message + assistant response to
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haiku to detect corrections, preferences, decisions, or facts worth storing.
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If learning events are detected, stores them via the memory API (or SQLite fallback).
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Runs with async: true — does NOT block the user.
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"""
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import io
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import json
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import logging
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import os
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import shutil
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import subprocess
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import sys
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import urllib.error
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import urllib.request
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logger = logging.getLogger(__name__)
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API_BASE_URL = os.environ.get("MEMORY_API_URL") or os.environ.get("CLAUDE_MEMORY_API_URL", "")
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API_KEY = os.environ.get("MEMORY_API_KEY") or os.environ.get("CLAUDE_MEMORY_API_KEY", "")
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JUDGE_PROMPT = """You are a memory extraction judge. Analyze this exchange between a user and an AI assistant.
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USER MESSAGE:
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{user_message}
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ASSISTANT RESPONSE:
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{assistant_response}
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Your job: determine if any of these learning events occurred:
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1. USER CORRECTION — user corrected the assistant's mistake or misunderstanding
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2. PREFERENCE — user stated a preference, habit, or "I like/prefer/want" statement
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3. DECISION — a decision was reached about how to do something
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4. FACT — user shared a durable fact about themselves, their team, tools, or environment
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If ANY learning event occurred, return JSON:
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{{"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}}]}}
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If NO learning event occurred, return:
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{{"events": []}}
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Rules:
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- Only extract DURABLE facts, not transient task details
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- Corrections are highest value (0.8-0.9)
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- Be conservative — false negatives are better than false positives
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- "expanded_keywords" should include synonyms, related concepts, and adjacent topics that would help find this memory later
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- "supersedes" should be a search query to find the old outdated memory, or null
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- Return ONLY valid JSON, no other text"""
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def _api_request(method: str, path: str, body: dict | None = None) -> dict:
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url = f"{API_BASE_URL}{path}"
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data = json.dumps(body).encode() if body else None
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req = urllib.request.Request(
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url, data=data, method=method,
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headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=15) as resp:
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return json.loads(resp.read().decode())
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def _store_via_api(content, category, tags, importance, expanded_keywords):
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_api_request("POST", "/api/memories", {
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"content": content, "category": category, "tags": tags,
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"expanded_keywords": expanded_keywords, "importance": importance,
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})
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def _store_via_sqlite(content, category, tags, importance, expanded_keywords):
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import sqlite3
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from datetime import datetime, timezone
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memory_home = os.environ.get("MEMORY_HOME", os.path.expanduser("~/.claude/claude-memory"))
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db_path = os.path.join(memory_home, "memory", "memory.db")
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# Also check legacy path
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if not os.path.exists(db_path):
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legacy_db = os.path.join(os.path.expanduser("~/.claude/metaclaw"), "memory", "memory.db")
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if os.path.exists(legacy_db):
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db_path = legacy_db
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conn = sqlite3.connect(db_path, timeout=10.0)
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conn.execute("PRAGMA journal_mode=WAL")
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now = datetime.now(timezone.utc).isoformat()
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conn.execute(
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"INSERT INTO memories (content, category, tags, importance, expanded_keywords, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?)",
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(content, category, tags, importance, expanded_keywords, now, now),
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)
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conn.commit()
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conn.close()
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def main() -> None:
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# Graceful exit if claude CLI is not available
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if not shutil.which("claude"):
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return
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try:
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hook_input = json.load(sys.stdin)
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except (json.JSONDecodeError, EOFError):
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return
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if isinstance(hook_input, dict) and hook_input.get("stop_hook_active", False):
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return
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transcript_path = ""
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if isinstance(hook_input, dict):
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transcript_path = hook_input.get("transcript_path", "")
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if not transcript_path or not os.path.exists(transcript_path):
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return
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user_message = ""
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assistant_response = ""
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try:
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MAX_TAIL_BYTES = 50_000
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with open(transcript_path, "rb") as f:
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f.seek(0, io.SEEK_END)
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size = f.tell()
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f.seek(max(0, size - MAX_TAIL_BYTES))
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tail = f.read().decode("utf-8", errors="replace")
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lines = tail.split("\n")
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for line in reversed(lines):
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line = line.strip()
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if not line:
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continue
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try:
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entry = json.loads(line)
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except json.JSONDecodeError:
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continue
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role = entry.get("role", "")
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content = entry.get("content", "")
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if isinstance(content, list):
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content = " ".join(
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b.get("text", "") for b in content
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if isinstance(b, dict) and b.get("type") == "text"
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)
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content = str(content)[:2000]
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if role == "assistant" and not assistant_response:
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assistant_response = content
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elif role == "user" and not user_message:
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user_message = content
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if user_message and assistant_response:
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break
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except Exception:
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return
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if not user_message or len(user_message.strip()) < 10:
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return
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prompt = JUDGE_PROMPT.format(
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user_message=user_message,
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assistant_response=assistant_response[:1000],
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)
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try:
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result = subprocess.run(
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["claude", "-p", prompt, "--model", "haiku"],
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capture_output=True, text=True, timeout=30,
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env={**os.environ, "CLAUDECODE": ""},
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)
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if result.returncode != 0:
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return
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response_text = result.stdout.strip()
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if response_text.startswith("```"):
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lines = response_text.split("\n")
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lines = [l for l in lines if not l.strip().startswith("```")]
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response_text = "\n".join(lines).strip()
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judge_result = json.loads(response_text)
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events = judge_result.get("events", [])
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if not events:
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return
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except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError):
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return
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category_map = {
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"correction": "preferences",
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"preference": "preferences",
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"decision": "decisions",
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"fact": "facts",
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}
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for event in events:
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content = event.get("content", "")
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if not content:
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continue
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event_type = event.get("type", "fact")
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importance = max(0.0, min(1.0, float(event.get("importance", 0.7))))
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category = category_map.get(event_type, "facts")
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tags = f"auto-learned,{event_type}"
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expanded_keywords = event.get("expanded_keywords", "")
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try:
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if API_KEY and API_BASE_URL:
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_store_via_api(content, category, tags, importance, expanded_keywords)
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else:
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_store_via_sqlite(content, category, tags, importance, expanded_keywords)
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except Exception:
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pass # Never crash the async hook
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if __name__ == "__main__":
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main()
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