ruminate

name	ruminate
description	Use when mining past Claude Code conversations for patterns, corrections, and knowledge that reflect missed and that were never written into the brain; triggers include "ruminate", "mine my history".

Ruminate

Mine conversation history for brain-worthy knowledge that was never captured. Complements reflect (current session) and meditate (vault audit) by looking at the full archive of past conversations. The invariant: surface only high-signal, recurring patterns — discard one-off incidents aggressively.

When to Use

Use this skill when:

• You want to bootstrap or enrich the brain from existing conversation history.
• Corrections, preferences, or gotchas keep recurring but were never written down.
• The user says "ruminate" or "mine my history".

Do not use this skill for the current session's learnings — use reflect. Do not use it to audit existing brain content — use meditate.

Process

1. Read the brain. Build a snapshot: sh "${CLAUDE_PLUGIN_ROOT}/skills/meditate/scripts/snapshot.sh" brain/ /tmp/brain-snapshot-ruminate.md. Pass the snapshot path to each analysis agent so the orchestrator's context stays clean.

2. Locate conversations in ~/.claude/projects/-<cwd-with-dashes-replacing-slashes>/.

3. Extract them into readable batches:

   python3 "${CLAUDE_PLUGIN_ROOT}/skills/ruminate/scripts/extract-conversations.py" "$CONV_DIR" "$OUT_DIR" --batches N
   

   Choose N: ~1 batch per 20 conversations, min 2, max 10.

4. Spawn an analysis team — N read-only subagents in parallel (one per batch, subagent_type: general-purpose). Give each agent: its batch manifest path, its output path ($OUT_DIR/findings_N.md), and the list of topics already in the brain (from step 1) so it skips known knowledge. Each agent extracts: user corrections, recurring preferences, technical learnings, workflow patterns, frustrations, and skills the user wished for.

5. Synthesize. Read all findings, cross-reference with the brain, dedupe across batches, then filter by frequency (recurring, not one-off), factual accuracy (anything now wrong in the brain is always worth fixing), and impact. Discard aggressively. Done means every mined finding is resolved: either promoted to a proposed brain/skill update, or dismissed with a one-line reason — none left in limbo.

6. Present and apply. Show findings in a table (finding, frequency/evidence, proposed action). A one-off is a dismissed-with-reason row; a pattern is a promoted row — so the table is the resolved set from step 5, no unresolved findings. Route skill-specific learnings into the relevant SKILL.md (read it first). Apply only approved changes, following brain writing conventions; update the relevant index entrypoint. Quote the user's words when a finding stems from a correction.

7. Clean up. rm -rf "$OUT_DIR" and shut down idle agents.

Stop and Ask

STOP and present to the user before writing — ruminate proposes, the user decides what is worth adding. Always ask which findings to apply rather than writing speculative notes directly.

Output

A table of findings with evidence and proposed actions, then the set of approved brain/skill changes applied (files listed one line each). The temporary extraction directory is removed.

References

• scripts/extract-conversations.py — parses JSONL conversations into batched text.
• ${CLAUDE_PLUGIN_ROOT}/skills/meditate/scripts/snapshot.sh — builds the brain snapshot passed to agents.