Memory

Skill aitytech/agentkits-memory/skills/memory

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.From its SKILL.md

Install

npx -y skills add aitytech/agentkits-memory --skill memory

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SKILL.md

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AgentKits Memory Skill

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.

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