Agent-Skills.md

Agent Skills: Consolidate -- Synthesize Developer Memory

Consolidate Goldfish checkpoints into memory.yaml -- use when recall flags consolidation needed, before ending long sessions, or on a scheduled cadence to synthesize episodic checkpoints into durable project understanding

UncategorizedID: anortham/goldfish/consolidate

Repository

anortham/goldfish
anortham
1

Install this agent skill to your local

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

Skill Files

Browse the full folder contents for consolidate.

Download Skill

Loading file tree…

skills/consolidate/SKILL.md

Skill Metadata

Name
consolidate
Description
Consolidate Goldfish checkpoints into memory.yaml -- use when recall flags consolidation needed, before ending long sessions, or on a scheduled cadence to synthesize episodic checkpoints into durable project understanding

Consolidate -- Synthesize Developer Memory

When to Consolidate

  • Recall flags it -- consolidation.needed: true in recall response
  • Before ending a long session -- significant new work that should be synthesized
  • Scheduled cadence -- daily wrap-up, weekly review
  • Manual request -- user asks to consolidate or update project memory

Workflow

Step 1: Get the Payload

mcp__goldfish__consolidate({})

Returns a lightweight JSON payload with status, checkpointCount, remainingCount, and a prompt template. No checkpoint content is returned. Checkpoint file paths are embedded in the prompt (not in the payload) to keep the response compact.

Step 2: Check Status

  • status: "current" -- nothing to do, memory is up to date. Tell the user and stop.
  • status: "ready" -- proceed to step 3.

Step 3: Dispatch Background Subagent

The prompt field already contains everything the subagent needs: file paths to read, synthesis instructions, and output paths to write. Dispatch it directly.

Agent({
  description: "Consolidate project memory",
  prompt: payload.prompt,
  run_in_background: true,
  mode: "bypassPermissions"
})

Step 4: Report Remaining

If remainingCount > 0, tell the user: "Consolidated {checkpointCount} checkpoints. {remainingCount} remain. Run /consolidate again to process more, or /consolidate all to process everything."

If remainingCount is 0, the user does not need to know about batching.

/consolidate all -- Process Everything

When the user passes "all" as an argument, loop until fully caught up:

  1. Call consolidate()
  2. If status: "current", done. Report total processed.
  3. If status: "ready", dispatch a foreground subagent (must wait for consolidation state to update before next batch).
  4. If remainingCount > 0, repeat from step 1.
  5. Circuit breaker: Max 10 iterations. If exceeded, stop and tell user how many were processed and how many remain.

Foreground subagents are required because each batch writes the consolidation state file, and the next consolidate() call needs that timestamp to filter correctly.

What the Subagent Does

  1. Reads memory.yaml from disk (if it exists; handles legacy MEMORY.md as baseline)
  2. Reads each checkpoint file from the provided path list
  3. Reads the active plan from disk (if provided)
  4. Synthesizes into structured YAML with four fixed sections (decisions, open_questions, deferred_work, gotchas)
  5. Overwrites contradictions (new facts replace old)
  6. Prunes ephemeral details (keeps decisions, drops debugging steps)
  7. Respects the 40-entry hard cap
  8. Writes updated memory.yaml and consolidation state

The subagent does NOT modify or delete checkpoints or plans.

After Consolidation

Next time recall() runs, it will load the fresh memory.yaml and show fewer (or zero) delta checkpoints. The consolidation flag will show needed: false.