How do I install the Memory Workflow skill?

Run npx skillmds@latest add tomevault-io/memory-workflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Memory Workflow safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Memory Workflow?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Memory Workflow free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Memory Workflow?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Memory Workflow by tomevault-io — Agent Skill

Name: Memory Workflow
Author: tomevault-io

File contents

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

Source: aitytech/agentkits-memory — distributed by TomeVault.

---name: memory-workflowdescription: 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 ActivateUse 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## PrerequisitesBefore 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 RecallFor 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 MemoriesSave 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 Rules1. ALWAYS start with `memory_search` (Layer 1), never jump to `memory_details`2. Review search results and select only relevant IDs before fetching details3. Use filters (category, date range) to narrow results4. Limit `memory_details` to 3-5 IDs per call5. 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).

Frequently asked questions

Memory Workflow