AI编程代理学习系统

1,581 "dont" entries   →  5-9 principles per project    (LLM consolidation)
  each with positiveRule  →  affirmative-only injection  (Pink Elephant fix)
29 config entries      →  4-5 thematic summaries        (LLM consolidation)
21,800 chars raw data  →  6,200 chars injected           (71% reduction)

Session Start (Hook) — injection mode
  → Checks if consolidation is stale
  → Spawns background LLM worker if needed (non-blocking)
  → Spawns background embedding backfill worker (non-blocking)
  → Injects consolidated config + principles (layer 1) + recent 30-day entries (layer 2) + owner profile
  → Vector search injects semantically related short-term memories (layer 3)
  → Cross-project vector search injects related memories from other active projects (layer 4)
  → Only customized settings injected (defaults stripped)

Session Start (Hook) — agent mode
  → Injects dont summary + config index + owner profile (minimal footprint)
  → No vector search at startup (deferred to on-demand recall)

User Prompt (Hook) — agent mode
  → Injects 1-line reminder: "search memory if relevant"
  → Main agent spawns memory-recall sub-agent as needed
  → Sub-agent runs memory_search → returns summary only (no raw data in main context)
  → Survives compaction (re-injected on every user message)

During Session
  → memory_save auto-generates embedding via local inference (no API call)
  → memory_save enriches tags with LLM-assigned weights (0.0-1.0) (when API key available)
  → Theme shift triggers background re-tagging of related past entries
  → memory_search merges keyword + vector semantic + tag-weighted results
  → Vector hits increment access counts → auto-promote to intensity 5 at threshold

Session End (Hook)
  → LLM analyzes the conversation
  → Detects mistakes, frustration, retry patterns
  → Auto-saves lessons learned (with embedding)
  → Deduplicates against existing entries before saving
  → Updates active projects tracker (top 5 recent projects)

Background (async workers)
  → Consolidates "dont" entries → behavioral principles
  → Consolidates "config" entries → thematic summaries
  → Backfills embeddings for entries created before vector layer (20/run)
  → Results used in next session start

npm install -g wasurenagusa-mcp

git clone https://github.com/tsutushi0628/wasurenagusa-mcp.git
cd wasurenagusa-mcp
npm install && npm run build
npm link

# Set at least one API key
GEMINI_API_KEY=your-key-here
# OPENAI_API_KEY=your-key-here
# ANTHROPIC_API_KEY=your-key-here

claude mcp add wasurenagusa -- wasurenagusa-mcp

{
  "hooks": {
    "SessionStart": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "wasurenagusa-context",
            "timeout": 5
          }
        ]
      }
    ],
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "wasurenagusa-context",
            "timeout": 5
          }
        ]
      }
    ],
    "Stop": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "wasurenagusa-analyze",
            "timeout": 30
          }
        ]
      }
    ],
    "PreCompact": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "wasurenagusa-context",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

{
  "outputMode": "agent"
}

- Read/write memories via sub-agents (memory_search / memory_get_detail / memory_save)
- Do not bring raw memory data into the main context
- When system-reminder suggests memory recall, spawn a sub-agent to run memory_search and return summary only

memory_save
  → Text → local inference (Hugging Face Transformers) → embedding → SQLite (sqlite-vec)

memory_search "authentication setup"
  → Full-text search (FTS5, Japanese support) ─┐
  → Embed query → vector similarity search     ─┤→ merge, deduplicate → results
                                                └→ increment access count
                                                   → auto-promote if threshold met

SessionStart Hook
  → Embed project name → short-tier search → inject related memories

# Install (macOS: launchd, Linux: crontab)
wasurenagusa-scheduler install

# Check status
wasurenagusa-scheduler status

# Remove
wasurenagusa-scheduler uninstall

npm run build        # Compile TypeScript
npm test             # Run tests
npm run test:watch   # Watch mode