Ruminate

Mine conversation history for brain-worthy knowledge that was never captured. Complements reflect (current session) and meditate (brain vault audit) by looking at the full archive of past conversations.

Process

1. Read the brain

Build a brain snapshot: sh .agents/skills/meditate/scripts/snapshot.sh brain/ /tmp/brain-snapshot-ruminate.md. Pass the snapshot path to each analysis agent. This avoids loading the full brain into the ruminate orchestrator's context.

2. Locate conversations

Find the project conversation directory:

~/.claude/projects/-<cwd-with-dashes-replacing-slashes>/

3. Extract conversations

Run the extraction script to parse JSONL conversation files into readable text and split into batches:

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

Choose N based on the number of conversations found: ~1 batch per 20 conversations, minimum 2, maximum 10.

4. Spawn analysis team

Create an agent team (TeamCreate) with N agents (one per batch), each with subagent_type: general-purpose and model: opus. Run all N in parallel.

Each agent's prompt should include:

• The batch manifest path ($OUT_DIR/batches/batch_N.txt)
• The output path ($OUT_DIR/findings_N.md)
• The list of topics already captured in the brain (compiled from step 1) — so agents skip known knowledge
• Instructions to extract from each conversation:
  • User corrections: times the user corrected the assistant's approach, code, or understanding
  • Recurring preferences: things the user explicitly asked for or pushed back on repeatedly
  • Technical learnings: codebase-specific knowledge, gotchas, patterns discovered
  • Workflow patterns: how the user prefers to work
  • Frustrations: friction points, wasted effort, things that went wrong
  • Skills wished for: capabilities the user expressed wanting

Agents write structured findings to their output files.

5. Synthesize

After all agents complete, read all findings files. Cross-reference with existing brain content. Deduplicate across batches.

Filter by frequency and impact. Most findings won't be worth adding. Apply these filters before presenting:

• Frequency: Did this come up in multiple conversations, or was the user correcting the same mistake repeatedly? One-off corrections are usually not worth a brain entry — the brain should capture patterns, not incidents.
• Factual accuracy: Is something in the brain now wrong? These are always worth fixing regardless of frequency.
• Impact: Would failing to capture this cause repeated wasted effort in future sessions?

Discard aggressively. It's better to present 3 high-signal findings than 9 that include noise.

6. Present and apply

Present findings to the user in a table with columns: finding, frequency/evidence, and proposed action. Be honest about which findings are one-offs vs. recurring patterns — let the user decide what's worth adding.

Route skill-specific learnings. Check if any findings are about how a specific skill should work — its process, prompts, edge cases, or troubleshooting. Update the skill's SKILL.md directly. Read the skill first to avoid duplicating or contradicting existing content.

Apply only the changes the user approves. Follow brain writing conventions:

• One topic per file, organized in directories
• Use [[wikilinks]] to connect related notes
• Update brain/index.md after all changes
• Default to updating existing notes over creating new ones

7. Clean up

Remove the temporary extraction directory:

rm -rf "$OUT_DIR"

Guidelines

• Filter aggressively. Most conversations will have low signal — automated tasks, trivial exchanges, already-captured knowledge. Only surface what's genuinely new and impactful.
• Prefer reduction. If a finding is a special case of an existing brain principle, update the existing note rather than creating a new one.
• Quote the user. When a finding stems from a direct user correction, include the user's words — they carry the most signal about what matters.
• Shut down agents when analysis is complete. Don't leave them idle.