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 skills add Robot-Inventor/agent-skills --skill review-loopgit clone --depth 1 https://github.com/Robot-Inventor/agent-skillsWrote 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/robot-inventor/agent-skills/review-loop)<a href="https://agentmods.dev/skills/robot-inventor/agent-skills/review-loop"><img src="https://agentmods.dev/badge/skills/robot-inventor/agent-skills/review-loop/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/robot-inventor/agent-skills/review-loop"><img src="https://agentmods.dev/badge/skills/robot-inventor/agent-skills/review-loop.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 | $0.00069 | $0.00935 |
| Opus 5 | $0.00034 | $0.00467 |
| Sonnet 5 | $0.00014 | $0.00187 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
review-loop 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 yesterday.
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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Loop Skill
This skill involves thoroughly improving code quality by repeatedly requesting a review from a sub-agent after editing and improving the code based on the results. Humans typically improve code quality by creating a pull request after editing code and having it reviewed by a reviewer. Such reviews are important because they can identify problems that might be missed through self-review alone.
When to apply
Apply this skill in the following situations:
- When you have performed a task that requires code editing, and
- After completing the task instructed by the user, and
- Before returning the conversation to the user
Steps
The following is the core of this skill; please follow the specified steps.
1. Request a code review
Request a code review from the subagent.
If you have the option to choose whether to fork the context or launch the subagent in an isolated context when starting it, always launch it in an isolated context. Refer to the following for the prompt to send to the review agent.
You are a code review agent. Thoroughly review the code for design issues, bugs, vulnerabilities, and oversights, and report the findings categorized as critical, high, medium, low, or informational. In addition to reviews that increase the amount of code, also check for over-engineering, such as excessive implementation or unnecessary conditional branching, in accordance with simple-engineering skill. In addition to the small details of the code, you should also consider whether there are simpler designs or alternative approaches to the overall logic of the changed parts, following the principles of simple-engineering. If you find any, you should report them, even if it requires rewriting the code being reviewed from scratch.
Flag excessive validation, overly defensive implementation, and unnecessary complexity that does not serve the code's intended purpose. For example, a function that opens product links for a specific website may only need to verify the hostname. Checking the pathname may add complexity without improving security. You might remove validation by defining the function argument as a type such as `https://example.com/product/{string}`, or by accepting only a product ID and constructing the link inside the function. These are examples, but you should look for simpler implementations of the same kind. Ask what purpose the code serves and whether the implementation contains only what that purpose requires. In addition to the aforementioned review output, also use the ponytail-review skill if available.
- Target: {uncomitted changes / filepath etc.}
- User's instructions: {user's request / instructions}
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.
- yesterday Changed · +2 lines b0a3e44d19c2
- 3d ago First seen · 53 lines · 69 tokens per session scan A 9c7e056abe3a
review-loop is a skill published in the GitHub repository Robot-Inventor/agent-skills (2 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 935 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 2026-09-05.
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