using-forgetful-memory

name	using-forgetful-memory
description	Guidance for using Forgetful semantic memory effectively. Applies Zettelkasten atomic memory principles. Use when deciding whether to query or create memories, structuring memory content, or understanding memory importance scoring.
allowed-tools	mcp__forgetful__discover_forgetful_tools, mcp__forgetful__how_to_use_forgetful_tool, mcp__forgetful__execute_forgetful_tool

Using Forgetful Memory

Forgetful is a semantic memory system using Zettelkasten (atomic note) principles. This skill guides effective memory usage.

When to Query Memory

Query memory proactively when:

• Starting work on a project (check for existing context)
• User references past work, decisions, or discussions
• Encountering a problem that may have been solved before
• Implementing patterns that may already be documented
• Needing context about preferences or approaches

Use execute_forgetful_tool("query_memory", {...}) with:

• query: Natural language search terms
• query_context: Why you're searching (improves ranking)
• include_links: true (to see connected knowledge)

Getting Recent Memories for a Project

To see what's been recorded recently for a specific project:

execute_forgetful_tool("get_recent_memories", {
  "limit": 10,
  "project_ids": [PROJECT_ID]
})

This is useful when:

• Starting a session on a project you haven't worked on recently
• Reviewing what was captured in previous conversations
• Getting a quick overview of project knowledge

When to Create Memory

Create memories for knowledge worth preserving:

• Important decisions with rationale (importance 8-9)
• Technical patterns or approaches (importance 7-8)
• Architectural choices (importance 9-10)
• Preferences and workflows (importance 8-9)
• Project milestones (importance 6-7)
• Solutions to non-trivial problems (importance 7-8)

Do NOT create memories for:

• Temporary context (current file paths, transient issues)
• Common knowledge available elsewhere
• Trivial or throwaway information
• Content that changes frequently

Atomic Memory Principles

Each memory must pass the atomicity test:

1. Can you understand it at first glance?
2. Can you title it in 5-50 words?
3. Does it represent ONE concept/fact/decision?

Constraints

Field	Limit	Guidance
Title	200 chars	Short, searchable phrase
Content	2000 chars	Single concept (~300-400 words)
Context	500 chars	WHY this matters
Keywords	10 max	For semantic clustering
Tags	10 max	For categorization

Importance Scoring

Score	Use For
9-10	Personal facts, foundational patterns
8-9	Critical solutions, major decisions
7-8	Useful patterns, preferences
6-7	Milestones, specific solutions
5-6	Minor context (use sparingly)

Project Discovery

Before creating memories, find the correct project:

1. Get current repo - Check the git remote:

   git remote get-url origin
   

   Extract the repo identifier (e.g., ScottRBK/forgetful-plugin)

2. Search by repo - Filter projects directly:

   execute_forgetful_tool("list_projects", {"repo_name": "owner/repo"})
   

3. Use the project_id - Never assume project 1 - always discover first

If no project exists for the current repo:

• Ask user if they want to create one (with repo_name set)
• Or scope the memory without a project_id (global memory)

Query Before Create

Always check for existing memories before creating:

execute_forgetful_tool("query_memory", {
  "query": "<topic of potential new memory>",
  "query_context": "Checking for existing memories before creating",
  "k": 5
})

If similar memory exists:

• Update it instead of creating duplicate
• Or mark it obsolete if superseded
• Or link new memory to existing one

Announcing Memory Operations

When creating a memory (importance >= 7), announce:

💾 Saved to memory: "[title]"
   Tags: [tags]
   Related: [auto-linked memory titles]

When querying, summarize:

Found X memories about [topic]:
- [Memory 1]: [brief insight]
- [Memory 2]: [brief insight]

Content That's Too Long

If content exceeds 2000 chars:

1. Use create_document for full content
2. Extract 3-5 atomic memories as entry points
3. Link memories to document via document_ids

Example: Architecture overview (document) → separate memories for each layer/decision.

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Tool Quick Reference

Common tools you can call directly via execute_forgetful_tool(tool_name, args):

Memory Tools

Tool	Required Params	Description
query_memory	query, query_context	Semantic search
create_memory	title, content, context, keywords, tags, importance	Store atomic memory
get_memory	memory_id	Get full memory details
update_memory	memory_id	PATCH update fields
link_memories	memory_id, related_ids	Manual bidirectional linking
mark_memory_obsolete	memory_id, reason	Soft delete with audit
get_recent_memories	(none)	Recent memories list

Project Tools

Tool	Required Params	Description
list_projects	(none)	List all projects
create_project	name, description, project_type	Create project container
get_project	project_id	Get project details

Entity Tools

Tool	Required Params	Description
create_entity	name, entity_type	Create org/person/device
search_entities	query	Text search by name/aka
link_entity_to_memory	entity_id, memory_id	Link entity↔memory
get_entity_memories	entity_id	All memories for entity
create_entity_relationship	source_entity_id, target_entity_id, relationship_type	Knowledge graph edge

Document & Code Artifact Tools

Tool	Required Params	Description
create_document	title, description, content	Long-form content
create_code_artifact	title, description, code, language	Reusable code

Full schemas: See TOOL_REFERENCE.md for complete parameter details and examples.