memory-store

<execution> Use Task tool with subagent_type: "memory-only" to keep main context clean.

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. </execution>

<selectivity> EXTREMELY SELECTIVE - Most of the time: DOESN'T insert.

Safe: <1 insertion per task. 2-3 insertions almost NEVER happen.

Store when: Non-obvious bugs, hard-won failure lessons, universal patterns across projects, user frustration signals.

Skip when: Standard practices, project-specific config, routine fixes, vague insights. </selectivity>

<workflow> Step 1: Format memory

**Title:** [Concise title]
**Preview:** [2-3 sentence summary - CRITICAL for search]

**Content:** [What happened, what was tried, what worked/failed, key lesson]

**Tags:** #role #topic #success|#failure

Step 2: Extract metadata Parse the formatted text to extract:

• title: Plain text without markdown (from "Title:")
• preview: Plain text without markdown (from "Preview:")

Step 3: Detect role for storage Determine which role collection to store in based on task context. Use role_mapping below. Default to "OTHER" if unclear.

Step 4: Search for duplicates Use searchmemory with full formatted text as query, roles=["detectedrole", "OTHER"], limit=10.

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

Step 4.5: Review and fetch specific memories After search returns previews:

1. LLM reads the preview texts (10 results)
2. Select 3-4 that might match or conflict with new memory
3. FETCH those specific ones using batchgetmemories(docids, roles=["detectedrole", "OTHER"])
4. Read fetched full content to decide: duplicate? merge? update?

Important: Don't rely on similarity score - rely on LLM review of actual content.

Step 5: Decide action Based on fetched content review:

• Near-identical exists → MERGE (combine, delete old)
• Related exists → UPDATE (enhance existing)
• Pattern emerges from 2+ episodic → GENERALIZE to semantic
• Different topic → CREATE new

Step 6: Store Use store_memory(document, role, metadata) with:

• document: Full formatted markdown text (Title + Preview + Content + Tags)
• role: Collection name (backend, frontend, scrum-master, qa, OTHER)
• metadata: 3-field dict extracted from document:

{
  "title": "Plain text title (extracted from **Title:** line)",
  "preview": "2-3 sentence summary (extracted from **Preview:** line)",
  "content": "[Full formatted markdown document - same as document parameter]"
}

API Clarification:

• document = the full markdown text
• role = which collection to store in
• metadata = {title, preview, content} - 3 fields extracted from document

</workflow>

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

• backend: API, endpoint, database, server, auth
• frontend: React, Vue, component, UI, CSS
• scrum-master: Agile, sprint, standup, retrospective, planning
• po: Product management, backlog, priorities, stakeholders
• 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.

Note: Can add new roles via MCP as needed. </role_mapping>