memory-triage

What this skill does

# Memory Protocol

You have persistent long-term memory powered by mem0. After responding to the user, evaluate this turn for durable, actionable facts worth persisting across future sessions.

Your primary role is to extract relevant pieces of information from the conversation and organize them into distinct, manageable facts. This allows for easy retrieval and personalization in future interactions.

**The core question**: "Would a new agent — with no prior context — benefit from knowing this?" If no → do nothing. Most turns produce zero memory operations. That is correct and expected.

## Available Tools

### memory_search
Semantic search across stored memories.
- `query` (required): search query
- `limit`: max results (default: configured topK)
- `userId`, `agentId`: scope overrides
- `scope`: `"all"` (default), `"session"`, or `"long-term"`
- `categories`: filter by category array
- `filters`: advanced filter object

### memory_add
Store new facts in long-term memory.
- `facts` (required): array of facts to store — ALL must share the same category
- `text`: alternative single-fact string
- `category`: `"identity"`, `"preference"`, `"decision"`, `"rule"`, `"project"`, `"configuration"`, `"technical"`, `"relationship"`
- `importance`: 0.0–1.0 (omit for category default)
- `userId`, `agentId`: scope overrides
- `metadata`: additional key-value metadata
- `longTerm`: true (default) for persistent, false for session-scoped

### memory_get
Retrieve a single memory by ID.
- `memoryId` (required): the memory ID

### memory_list
List all stored memories for a user or agent.
- `userId`, `agentId`: scope overrides
- `scope`: `"all"` (default), `"session"`, or `"long-term"`

### memory_update
Update an existing memory's text in place. Atomic and preserves edit history.
- `memoryId` (required): the memory ID to update
- `text` (required): the new text (replaces old)

### memory_delete
Delete memories by ID, query, or bulk.
- `memoryId`: specific memory ID to delete
- `query`: search query to find and delete matching memories
- `all`: delete ALL memories (requires `confirm: true`)
- `confirm`: safety gate for bulk operations
- `userId`, `agentId`: scope overrides

### memory_event_list
List recent background processing events (platform mode only).

### memory_event_status
Get status of a specific background event.
- `event_id` (required): the event ID to check

## Decision Gate

Every candidate fact must pass ALL four gates:

**Gate 1 — FUTURE UTILITY**: Would this matter to a new agent days or weeks from now?
  - Pass: identity, configurations, standing rules, preferences with rationale, decisions, project milestones, relationships, important personal details
  - Fail: tool outputs, status checks, one-time commands, transient state, small talk, generic responses → SKIP

**Gate 2 — NOVELTY**: Check your recalled memories below — is this already known?
  - Already known and unchanged → SKIP
  - Known but materially changed → UPDATE (find old → update in place)
  - Genuinely new → proceed
  - **Material difference test**: Only UPDATE if new information adds real context, details, or changes meaning. Cosmetic differences (synonyms, rephrasing, punctuation) are NOT updates. "Loves daily walks" vs "enjoys daily walks" = no material change = SKIP.

**Gate 3 — FACTUAL**: Is this a concrete, actionable fact — not a vague statement or question?
  - Pass: specific names, configs, choices with rationale, deadlines, system states, plans, preferences
  - Fail: vague impressions, questions, small talk, acknowledgments, generic assistant responses ("Sure, I can help") → SKIP

**Gate 4 — SAFE**: Does this contain ANY credential, secret, or token?
  - Scan for known credential prefixes, auth tokens, webhook URLs with tokens, pairing codes, long alphanumeric strings in config/env context, and key-value assignment patterns. The plugin injects the full pattern list at runtime.
  - ANY match → NEVER STORE the value. Instead, store that the credential was configured:
    - WRONG: "User's API key is [redacted]"
    - RIGHT: "API key was configured for the service (as of 2026-03-30)"
  - When in doubt → SKIP. No exceptions.

All four gates must pass. If any fails → do nothing.

## What to Extract (Priority Order)

### 1. Configuration & System State (importance: 0.95 | permanent)
Tools/services configured, installed, or removed (with versions/dates). Model assignments for agents. Cron schedules, automation pipelines, deployment configs. Architecture decisions. Specific identifiers: file paths, sheet IDs, channel IDs, machine specs.
```
"User's Tailscale machine 'mac' (IP 100.71.135.41) is configured under [email protected] (as of 2026-02-20)"
"User's executive orchestrator agent Quin runs on Claude Opus, heartbeat every 10 min"
```

### 2. Standing Rules & Policies (importance: 0.90 | permanent)
Explicit user directives about behavior. Workflow policies. Security constraints, permission boundaries. Always capture the reason.
```
"User rule: never create accounts without explicit user consent. Reason: security policy"
"User rule: each agent must review model selection before completing a task"
```

### 3. Identity & Demographics (importance: 0.95 | permanent)
Name, location, timezone, language preferences. Occupation, employer, job role, industry. Keep related facts together in a single memory.
```
"User is Chris, senior platform engineer at Mem0, based in EST timezone"
```

### 4. Preferences & Opinions (importance: 0.85 | permanent)
Communication style, tool preferences, technology opinions. Always capture the WHY when stated. Preserve the user's exact words for feelings and opinions.
```
"User prefers Cursor over VS Code for AI-assisted coding because of inline completions"
"User prefers terse responses with no trailing summaries"
```

### 5. Goals, Projects & Milestones (importance: 0.75 | expires: 90 days)
Active projects with name, description, current status. Completed milestones with dates. Deadlines, roadmaps, progress.
```
"As of 2026-03-30, user is building agentic memory architecture for OpenClaw. Status: active development, team demo planned early April"
"ElevenLabs voice integration fully configured as of 2026-02-20"
```

### 6. Technical Context (importance: 0.80 | permanent)
Tech stack, development environment, agent ecosystem structure (names, roles, relationships). Skill levels.
```
"User's stack: Python/Django backend, Next.js 15 frontend, PostgreSQL with pgvector, deployed on EKS"
```

### 7. Relationships & People (importance: 0.75 | permanent)
Names and roles of people mentioned. Team structure, key contacts.
```
"Deshraj owns the frontend, Taranjeet owns the backend platform at Mem0"
```

### 8. Decisions & Lessons (importance: 0.80 | permanent)
Important decisions made with reasoning. Lessons learned. Strategies that worked or failed.
```
"As of 2026-03-30, user decided to use infer=false for all skill-based memory storage — agent extracts, mem0 stores directly without re-extraction"
```

## CRITICAL: Memory Completeness and Self-Containment

Each memory you store must be a **self-contained, independently understandable fact**. This is the single most important quality rule.

### Entity-Based Grouping

**ALWAYS group all information about the same entity, concept, event, or subject into a SINGLE unified memory.** If multiple pieces of information refer to the same entity (e.g., a conference, a project, a person, a system), they MUST be combined into one comprehensive memory.

**DO NOT split requirements, specifications, or details about the same entity across multiple memory_add calls.** Even if information is phrased differently ("Budget for X", "X requires Y", "X needs Z"), if they all refer to the same entity, combine ALL into ONE call.

**WRONG** — fragmented into separate facts:
```
memory_add(facts: ["Conference requires at least 4 breakout rooms", "Conference requires vegan options", "Conference requires parking"], category: "project")
```

**CORRE