| name | memory-workflow |
| description | Use when you need to recall past work, previous decisions, error solutions, or project history. Activates the 3-layer memory search workflow for token-efficient retrieval. |
AgentKits Memory Workflow
When to Activate
Use this skill when:
- User asks about past work, previous sessions, or what was done before
- User references a decision, pattern, or error you don't have context for
- You need project history, conventions, or architectural decisions
- User asks "what did we do about X?" or "how did we handle Y?"
- You're missing context that should exist from earlier sessions
- Starting work on a feature that may have prior decisions recorded
Prerequisites
Before searching, check if memories exist:
memory_status()
If the database is empty, skip recall and inform the user.
3-Layer Search Workflow
Layer 1: Search Index (lightweight, ~50 tokens/result)
memory_search(query="your search term")
- Returns IDs, titles, categories, dates, and relevance scores
- Filter by category:
decision, pattern, error, context, observation
- Filter by date:
dateStart="2025-01-01", dateEnd="2025-12-31"
- Sort:
orderBy="relevance" (default), "date_asc", "date_desc"
Layer 2: Timeline Context (understand what happened around a result)
memory_timeline(anchor="MEMORY_ID")
- Shows what happened before/after a specific memory
- Helps understand the sequence of events
- Use when you need temporal context
Layer 3: Full Details (only for filtered IDs)
memory_details(ids=["ID1", "ID2"])
- Returns complete content for selected memories
- Limit to 3-5 IDs at a time to conserve tokens
- NEVER fetch details without filtering through Layer 1 first
Quick Topic Recall
For a fast overview of everything known about a topic:
memory_recall(topic="authentication")
This returns a grouped summary. Follow up with memory_details for specifics.
Saving Memories
Save important information for future sessions:
memory_save(content="...", category="decision", tags="auth,security", importance="high")
Categories: decision, pattern, error, context, observation
Importance: low, medium, high, critical
Token Efficiency Rules
- ALWAYS start with
memory_search (Layer 1), never jump to memory_details
- Review search results and select only relevant IDs before fetching details
- Use filters (category, date range) to narrow results
- Limit
memory_details to 3-5 IDs per call
- This workflow saves ~87% tokens vs fetching everything at once
