Borrowing it
Nothing to install: this file belongs to poteto/brainmaxxing. 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/brainmaxxing/main/.agents/skills/ruminate/SKILL.mdgit clone --depth 1 https://github.com/poteto/brainmaxxingWrote 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/brainmaxxing/ruminate)<a href="https://agentmods.dev/skills/poteto/brainmaxxing/ruminate"><img src="https://agentmods.dev/badge/skills/poteto/brainmaxxing/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/brainmaxxing/ruminate"><img src="https://agentmods.dev/badge/skills/poteto/brainmaxxing/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 | $0.00038 | $0.00968 |
| Opus 5.5 | $0.00015 | $0.00387 |
| Sonnet 5.5 | $0.00008 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade C, and why
ruminate scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$OUT_DIR" How it starts
The opening of the file, as written. The whole thing — 94 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.
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.
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 · 94 lines · 38 tokens per session scan C 58f2176ec066
ruminate is a skill published in the GitHub repository poteto/brainmaxxing (342 stars, last pushed 7mo ago), licensed MIT. It adds 38 tokens to every session and 968 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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