Borrowing it
Nothing to install: this file belongs to poteto/noodle. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/poteto/noodle/main/.agents/skills/ruminate/SKILL.mdgit clone --depth 1 https://github.com/poteto/noodleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/poteto/noodle/ruminate)<a href="https://agentmods.dev/skills/poteto/noodle/ruminate"><img src="https://agentmods.dev/badge/skills/poteto/noodle/ruminate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/poteto/noodle/ruminate"><img src="https://agentmods.dev/badge/skills/poteto/noodle/ruminate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.01300 |
| Opus 5.5 | $0.00024 | $0.00520 |
| Sonnet 5.5 | $0.00012 | $0.00260 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade C, and why
ruminate scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$OUT_DIR" How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Claude project directory:
~/.claude/projects/-<cwd-with-dashes-replacing-slashes>/ - 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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 111 lines · 59 tokens per session scan C 948821ca57bf
ruminate is a skill published in the GitHub repository poteto/noodle (529 stars, last pushed 7mo ago), licensed MIT. It adds 59 tokens to every session and 1,300 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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