review-loop

review-loop is a skill for Claude Code, Codex from Robot-Inventor/agent-skills. It costs 69 tokens per session (968 once invoked), scanned A, original, MIT.

A code-editing workflow that asks a separate agent to review changes before the work is returned to the user. The review checks for design problems, bugs, security issues, and missed details.

In plain words
What is it for?
Use it after completing any task that changes code or configuration. It is intended for isolated review followed by corrections before the final response.
Why use it?
A second review can catch problems that the agent who made the changes overlooked. Repeating the fix-and-review cycle improves confidence in edited code or configuration.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it after completing any task that changes code or configuration. It is intended for isolated review followed by corrections before the final response.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/robot-inventor/agent-skills/review-loop
Install

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.

Any agent
npx skills add Robot-Inventor/agent-skills --skill review-loop
Clone the repo
git clone --depth 1 https://github.com/Robot-Inventor/agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for review-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/robot-inventor/agent-skills/review-loop/github.svg)](https://agentmods.dev/skills/robot-inventor/agent-skills/review-loop)
Your own site
<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.

agentmods 80×15 button for review-loop

Your own site · 80×15
<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>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 968 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00069 $0.00968
Opus 5.5 $0.00028 $0.00387
Sonnet 5.5 $0.00014 $0.00194
Haiku 4.5 $0.00007 $0.00097

Measured 3d ago against content hash 6119065a400e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 3d 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.

skills/review-loop/SKILL.md · 55 lines

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. Ignore this skill when the user explicitly instructs to skip the review-loop.

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.

Submit review requests to the review agent using a prompt that strictly adheres to the following format. Review request prompts are not free-form; you must use the template below.

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}

Read the full file on GitHub · 55 lines

Changes

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.

  1. 3d ago Changed 6119065a400e
  2. 1mo ago Changed 47299fc8da53
  3. 1mo ago Changed · +2 lines b0a3e44d19c2
  4. 1mo ago First seen · 53 lines · 69 tokens per session scan A 9c7e056abe3a

Subscribe to this mod's changes

review-loop is a skill published in the GitHub repository Robot-Inventor/agent-skills (3 stars, last pushed 3d ago), licensed MIT. It adds 69 tokens to every session and 968 once invoked, about $0.0003 per session on Opus 5.5. 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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