Back to list
hungson175

memory-recall

by hungson175

Production-ready autonomous AI software development system built on Claude Code with multi-agent tmux teams and self-improving memory system

5🍴 0📅 Jan 22, 2026

SKILL.md


name: memory-recall description: Retrieve coding patterns from GLOBAL vector database (cross-project learning). Auto-invokes when TodoWrite has >3 tasks or when user says "--recall". Searches relevant role collections based on task context.

The memory-only agent has ZERO access to Read/Write/Edit/Glob/Bash - it can ONLY use MCP memory tools. This prevents file reading pollution by design.

Skip when: Obvious tasks, basic file operations, standard workflows, problems solvable with basic knowledge.

Hook Activation Condition:

  • TodoWrite tasks >3: Activate memory-recall (complex task needs context)
  • TodoWrite tasks ≤3: Don't activate (simple task, no need)
  • One search PER TASK in TodoWrite (e.g., 4 tasks = 4 searches)

Example: "Need to implement rate limiting for REST API to prevent abuse. Backend service in Node.js with Express. Want proven pattern that prevents thundering herd. Current setup uses Redis for session storage. Need to rate limit by IP address with 100 requests per minute. Must handle distributed deployments with multiple server instances. Looking for battle-tested implementation that won't add significant latency."

Step 2: Detect relevant roles Determine which role collections to search based on task context. Use role_mapping below. Default to ["OTHER"] if unclear. Can search multiple roles when task spans domains.

Step 3: Search previews Use search_memory with query, roles=["detected_role", "OTHER"], limit=30.

Note: The roles parameter tells MCP which collections to search.

Step 4: Analyze previews Review returned previews (preview text ONLY). Select 3-5 most relevant based on:

  • Does preview indicate relevant solution?
  • Does preview match problem domain?

Note: Score doesn't matter - don't trust similarity scores. Focus on analyzing the preview text content.

Step 5: Retrieve full content Use batch_get_memories with selected doc_ids and roles=["detected_role", "OTHER"].

Note: batch_get_memories needs roles parameter to know which collections to search in.

Step 6: Present results Format retrieved memories clearly. Let the main agent decide what to apply.

<role_mapping> Available roles (maps to Qdrant collections):

  • backend: API, endpoint, database, server, auth
  • frontend: React, Vue, component, UI, CSS
  • devops: Deploy, Docker, Kubernetes, CI/CD
  • scrum-master: Agile, sprint, standup, retrospective, planning
  • qa: Testing, quality assurance, verification, validation
  • OTHER: General patterns, cross-domain knowledge

Default to "OTHER" if unclear. Each role corresponds to a separate Qdrant collection.

Search multiple role collections when task spans domains (e.g., ["backend", "devops", "OTHER"] for API deployment task). </role_mapping>

Score

Total Score

60/100

Based on repository quality metrics

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

0/10
説明文

100文字以上の説明がある

+10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

Reviews

💬

Reviews coming soon