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/forgetful-recall

Recall past knowledge before working — prior decisions, solved problems, preferences, project history. Use at the start of any task, when the user references earlier work, when re-entering a project after time away, or before proposing an approach that may already have history.

From plugin
forgetful
29010 skills
Install
$ npx -y skills add ScottRBK/forgetful --skill forgetful-recall --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/forgetful-recall

Context preview

The summary Claude sees to decide when to auto-load this skill.

Recall past knowledge before working — prior decisions, solved problems, preferences, project history. Use at the start of any task, when the user references earlier work, when re-entering a project after time away, or before proposing an approach that may already have history.

SKILL.md

forgetful-recall.SKILL.md
name: forgetful-recall
description: >-
  Recall past knowledge before working — prior decisions, solved problems, preferences,
  project history. Use at the start of any task, when the user references earlier work, when
  re-entering a project after time away, or before proposing an approach that may already
  have history. Covers query shaping, scoping, session-start catch-up, and when to escalate
  to graph exploration.
license: MIT
tags: [memory, retrieval, search, context]
allowed-tools:
  - mcp__forgetful__discover_forgetful_tools
  - mcp__forgetful__how_to_use_forgetful_tool
  - mcp__forgetful__execute_forgetful_tool
  - Bash(forgetful:*)

Recalling knowledge from Forgetful

Retrieval quality is decided by how the query is shaped and scoped, and by treating coverage as something to judge rather than assume. Recall before proposing; history usually exists.

Invoking operations

Operations are named by registry name (`query_memory`, `get_recent_memories`, ...). Invoke via whichever surface this agent has:

  • **MCP**: `execute_forgetful_tool(tool_name="query_memory", arguments={...})`
  • **CLI**: `forgetful call query_memory --args '{"query": "..."}' --json`

Get any operation's schema at runtime: `how_to_use_forgetful_tool` (MCP) or `forgetful tools info <operation>` (CLI) — schemas are deliberately not repeated here.

Step 1 — Shape the query

`query_context` is a required parameter alongside `query`, not optional flavor text — the call errors without it. Pass it deliberately: the two are embedded together, and ranking genuinely shifts with intent ("auth" while implementing a feature ranks differently than "auth" while debugging login). Include exact identifiers verbatim — error codes, function names, config keys — the sparse full-text leg of the search matches them literally.

Done when: both `query` and `query_context` are written, not just a bare keyword.

Step 2 — Scope deliberately

Reads are cross-project by default, and usually should stay that way — knowledge transfers. Narrow with `project_ids` when the task is project-bound; add `strict_project_filter=True` to also keep linked memories inside those projects (the default `False` lets links cross them). Use `importance_threshold` to cut noise — it excludes anything scored below the value given, pairing naturally with `forgetful-remember`'s rubric, where 5 is the noise floor for bulk/automated captures. Adjust `k` to trade breadth for focus. These are filters layered on top of semantic search, which stays the primary retrieval mechanism throughout.

Done when: the scope is a choice, not a default accident.

Step 3 — Judge coverage

Results are budgeted (about 8000 tokens / 20 memories), so assess coverage rather than non-emptiness:

  • `truncated: true` → narrow the query (raise the threshold, scope the project) instead of

accepting silent loss.

  • A miss on the first angle → re-query from a different facet (the feature area, the

technology, the error text) before concluding the knowledge doesn't exist.

Done when: results are judged sufficient, or absence is confirmed from more than one angle.

Step 4 — Expand or escalate

Promising hits get `get_memory` for full content and links. When hits arrive as fragments, reference entities, or trail across domains, the flat list is the wrong shape — switch to `forgetful-explore` and walk the graph instead.

Done when: enough context is in hand, or the exploration skill has taken over.

Step 5 — Report

This skill is the single source of truth for the retrieval reporting convention:

  • Found context: "Found N memories about X" with the load-bearing ones named.
  • Nothing relevant: say so explicitly — "No existing memories about X."
  • Off-target results: flag them — "Retrieved some context but it seems tangential."

A clean miss is also a signal: note the gap as a `forgetful-remember` candidate once the task resolves it.

Done when: the user knows what memory contributed, even when the answer is "nothing".

Session-start catch-up

Re-entering a project after time away: `get_recent_memories` scoped to that project's ID is the catch-up move — recent decisions and milestones without guessing queries. Run it as a deliberate step, then continue into normal recall as the task demands.

Read more
Ships withforgetful

Forgetful is a storage and retrieval tool for AI Agents. Designed as a Model Context Protocol (MCP) server built using the FastMCP framework.

Get the whole plugin

Other skills on forgetful.