# Memory Workflow > 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. - Skill: `tomevault-io/memory-workflow` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/memory-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/memory-workflow/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/memory-workflow --- # 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 1. ALWAYS start with `memory_search` (Layer 1), never jump to `memory_details` 2. Review search results and select only relevant IDs before fetching details 3. Use filters (category, date range) to narrow results 4. Limit `memory_details` to 3-5 IDs per call 5. This workflow saves ~87% tokens vs fetching everything at once --- > Source: [aitytech/agentkits-memory](https://github.com/aitytech/agentkits-memory) — distributed by [TomeVault](https://tomevault.io).