Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/poteto/noodle/reviewnpx skills add poteto/noodle --skill reviewgit 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/review)<a href="https://agentmods.dev/skills/poteto/noodle/review"><img src="https://agentmods.dev/badge/skills/poteto/noodle/review.svg" alt="Measured on agentmods" 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 | $0.00068 | $0.01098 |
| Opus 5 | $0.00034 | $0.00549 |
| Sonnet 5 | $0.00014 | $0.00220 |
| Haiku 4.5 | $0.00007 | $0.00110 |
Grade A, and why
review scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
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.
Review
Thorough, interactive review grounded in project principles. Do NOT make changes — the review is the deliverable.
Autonomous Session Mode
When this skill runs in a non-interactive Noodle execution session (for example Cook, Oops, or Repair):
- Do not use AskUserQuestion. For each issue found, state the recommended action directly instead of presenting options.
- Write findings to a report file at
brain/audits/review-<date>-<subject>.mdin addition to session output. - For high-severity issues, file a todo in
brain/todos.mdusing the/todoskill. - For low-severity issues, note them in the report but don't file separate todos.
- Conclude by committing the review report. Do not wait for direction.
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.
Step 1 — Load Principles
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file. These principles govern review judgments — refer back to them when evaluating issues.
Do NOT skip this. Do NOT use memorized principle content — always read fresh.
Step 2 — Determine Scope
Infer what to review from context — the user's message, recent diffs, or referenced plans/PRs. If genuinely ambiguous (nothing to infer), ask.
Auto-detect review mode from change size:
- BIG CHANGE (50+ lines changed, 3+ files, or new architecture) — all sections, at most 4 top issues per section
- SMALL CHANGE (under those thresholds) — one issue per section
Step 3 — Gather Context
For SMALL CHANGE reviews, read files directly in the main context — delegation overhead exceeds the cost of reading a few files. For BIG CHANGE reviews, delegate exploration to subagents via the Task tool.
Spawn exploration agents (subagent_type: Explore) to:
- Read the code or plan under review
- Identify dependencies, callers, and downstream effects
- Map relevant types, tests, and infrastructure
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.
- 5d ago First seen · 111 lines · 68 tokens per session scan A d49f1ec0831b
review is a skill published in the GitHub repository poteto/noodle (267 stars, last pushed 5mo ago), licensed MIT. It adds 68 tokens to every session and 1,098 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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