Agent-Skills.md

Agent Skills: Recall — Restore Developer Memory

Restore context from Goldfish developer memory — use when starting a new session, after context loss, searching for past work, or when the user asks what happened previously, wants to find old decisions, or needs cross-project context

UncategorizedID: anortham/goldfish/recall

Repository

anortham/goldfish
anortham
1

Install this agent skill to your local

pnpm dlx add-skill https://github.com/anortham/goldfish/tree/HEAD/skills/recall

Skill Files

Browse the full folder contents for recall.

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skills/recall/SKILL.md

Skill Metadata

Name
recall
Description
Restore context from Goldfish developer memory — use when starting a new session, after context loss, searching for past work, or when the user asks what happened previously, wants to find old decisions, or needs cross-project context

Recall — Restore Developer Memory

When to Use

Call mcp__goldfish__recall to restore context from previous sessions. Recall runs automatically at session start, but users can also invoke /recall for targeted queries (search, cross-project, specific time ranges).

mcp__goldfish__recall({})

Default parameters (last 5 checkpoints, no date window) cover most cases.

Common Scenarios

  • New session, need prior context — recall() with defaults
  • After context compaction — recall to restore lost state
  • Searching for past work — recall({ search: "auth refactor", full: true })
  • Cross-project standup — recall({ workspace: "all", days: 1 })
  • Just need the plan — recall({ limit: 0 })

Parameter Examples

Standard recall (most common)

mcp__goldfish__recall({})

Need more history

mcp__goldfish__recall({ days: 7, limit: 20 })

Looking for specific work

mcp__goldfish__recall({ search: "auth refactor", full: true })

Recent activity only

mcp__goldfish__recall({ since: "2h" })

Search without memory (leaner results)

mcp__goldfish__recall({ search: "auth", includeMemory: false })

Interpreting Results

Recall returns up to three sections:

1. Active Plan (top of response)

The current strategic plan for this workspace. If present, work should align with it.

2. Checkpoints (chronological array)

Each checkpoint contains:

  • timestamp — when it happened (UTC)
  • description — what was done, why, and how
  • tags — categorization labels
  • git.branch, git.commit — git state at checkpoint time (only with full: true)
  • git.files — changed files (only with full: true)

3. Workspace Summaries (cross-project only)

When using workspace: "all", you get per-project summaries with checkpoint counts.

Processing Large Result Sets

When you get 10+ checkpoints back, distill them:

  1. Group by date — what happened each day
  2. Identify themes — feature work, bug fixes, refactoring, planning
  3. Highlight blockers — anything marked stuck, blocked, or failed
  4. Surface decisions — architectural choices, tradeoffs made
  5. Find the thread — what was the user working toward?

Present a concise summary: "Based on your recent work, you were [doing X] on [project area]. Last session you [accomplished Y] and the next step appears to be [Z]."

After Recall

Once you have context, act on it:

  1. Recall (restore memory)
  2. Understand (process what you get back)
  3. Continue (pick up where the last session left off)

Trust recalled context — don't re-verify information from checkpoints.

Consolidation

Recall now returns consolidated memory (memory.yaml) alongside checkpoints. When recall flags consolidation.needed: true, use the /consolidate skill to handle it.