| 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
-
Read the brain. Build a snapshot:
sh "${CLAUDE_PLUGIN_ROOT}/skills/meditate/scripts/snapshot.sh" brain/ /tmp/brain-snapshot-ruminate.md.
Pass the snapshot path to each analysis agent so the orchestrator's context
stays clean.
-
Locate conversations in ~/.claude/projects/-<cwd-with-dashes-replacing-slashes>/.
-
Extract them into readable batches:
python3 "${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.
-
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 ($OUT_DIR/findings_N.md), and the list of
topics already in the brain (from step 1) so it skips known knowledge.
Each agent extracts: user corrections, recurring preferences, technical
learnings, workflow patterns, frustrations, and skills the user wished for.
-
Synthesize. Read all findings, cross-reference with the brain, dedupe
across batches, then filter by frequency (recurring, not one-off),
factual accuracy (anything now wrong in the brain is always worth fixing),
and impact. Discard aggressively. Done means every mined finding is
resolved: either promoted to a proposed brain/skill update, or dismissed with
a one-line reason — none left in limbo.
-
Present and apply. Show findings in a table (finding, frequency/evidence,
proposed action). A one-off is a dismissed-with-reason row; a pattern is a
promoted row — so the table is the resolved set from step 5, no unresolved
findings. Route skill-specific learnings into the relevant SKILL.md (read it
first). Apply only approved changes, following brain writing conventions;
update the relevant index entrypoint. Quote the user's words when a finding
stems from a correction.
-
Clean up. rm -rf "$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.
