Ruminate

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 across both providers.

Process

Use Tasks to track progress. Create a task for each step below (TaskCreate), mark each in_progress when starting and completed when done (TaskUpdate). Check TaskList after each step.

1. Read the brain

Build a brain snapshot: sh .claude/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 both provider roots:

1. Claude project directory: ~/.claude/projects/-<cwd-with-dashes-replacing-slashes>/
2. Codex sessions root: ~/.codex/sessions/

For example, /Users/lauren/code/noodle maps to ~/.claude/projects/-Users-lauren-code-noodle/ for Claude and uses ~/.codex/sessions/ for Codex.

3. Extract conversations

Run the extraction script to parse both JSONL formats into readable text and split into batches:

SKILL_DIR="$(dirname "$(realpath "$0")")/.."  # adjust path as needed
CLAUDE_DIR="$HOME/.claude/projects/-<project-slug>"
CODEX_DIR="$HOME/.codex/sessions"
OUT_DIR="/tmp/ruminate-$(date +%s)"

python3 "$SKILL_DIR/scripts/extract-conversations.py" "$OUT_DIR" \
  --claude-dir "$CLAUDE_DIR" \
  --codex-dir "$CODEX_DIR" \
  --cwd "$PWD" \
  --batches N

Choose N based on total extracted conversations (Claude + Codex): ~1 batch per 20 conversations, minimum 2, maximum 10.

4. Spawn analysis team

Create an agent team (TeamCreate) with N agents (one per batch, matching the batch count from step 3), 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
• A reminder that each extracted file includes provider/source metadata headers ([PROVIDER], [CWD], [SOURCE_FILE]) and should be used as evidence context
• 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? (e.g. a rule was disabled but the brain still documents it as active). These are always worth fixing regardless of frequency.
• Impact: Would failing to capture this cause repeated wasted effort in future sessions? A gotcha that cost 5 minutes once is low-impact. A pattern that caused 3 rounds of corrections is high-impact.

Discard aggressively. It's better to present 3 high-signal findings than 9 that include noise. If a finding only happened once and isn't a factual correction, skip it.