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: ```bash 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. ### 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 or references/ 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: ```bash 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 and source file path — they carry the most signal about what matters. - **Shut down agents** when analysis is complete. Don't leave them idle.