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 PacificStudio/openase --skill review-codegit clone --depth 1 https://github.com/PacificStudio/openaseWrote 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/pacificstudio/openase/review-code)<a href="https://agentmods.dev/skills/pacificstudio/openase/review-code"><img src="https://agentmods.dev/badge/skills/pacificstudio/openase/review-code/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/pacificstudio/openase/review-code"><img src="https://agentmods.dev/badge/skills/pacificstudio/openase/review-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00627 |
| Opus 5.5 | $0.00007 | $0.00251 |
| Sonnet 5.5 | $0.00004 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
review-code 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 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.
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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Code
Overview
Conduct a thorough code review for quality, security, performance, and maintainability. Focus on the real risks first, then provide concrete, severity-rated feedback with file and line references where possible.
When To Use
- Before merging a pull request.
- After implementing a feature or refactor.
- When the user asks for a review of changed files or a branch.
- When the code looks correct at first glance but may still hide regressions or design debt.
Review Workflow
-
Identify the review scope.
- Review the current diff, named files, or the whole change set.
- Prefer reviewing the actual changed surface before expanding further.
-
Check behavioral correctness first.
- Regressions against existing behavior.
- Missing edge-case handling.
- Broken invariants or data consistency risks.
-
Review the main quality categories.
- Security risks.
- Code quality and complexity.
- Performance concerns.
- Error handling and observability.
- Maintainability, coupling, and testability.
-
Evaluate the test story.
- Are the important paths covered?
- Are the tests proving behavior rather than implementation trivia?
- Is there an obvious missing regression test for the change?
-
Rate findings by severity.
CRITICAL: must fix before merge; security or correctness issue with serious impact.HIGH: should fix before merge; likely bug, major regression risk, or serious design flaw.MEDIUM: worthwhile fix; non-blocking but meaningful quality issue.LOW: suggestion, cleanup, or style improvement.
-
Report findings in priority order.
- File and line reference.
- What is wrong.
- Why it matters.
- Concrete fix guidance.
Review Categories
- Security: secrets, unsafe input handling, authz gaps, injection, XSS, CSRF.
- Code quality: complexity, duplication, large functions, fragile branching.
- Performance: avoidable repeated work, inefficient algorithms, N+1 patterns, unnecessary re-renders.
- Maintainability: unclear naming, tight coupling, hidden assumptions, hard-to-test logic.
- Reliability: missing error handling, weak retries, silent failures, incomplete logging.
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 · 78 lines · 18 tokens per session scan A 57a83930729e
review-code is a skill published in the GitHub repository PacificStudio/openase (269 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 627 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
open-code-review
Performs AI-powered code review on Git changes using the ocr CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply…
review
Comprehensive security and correctness audit. Use for review, deep review, thermo nuclear, or thermonuclear requests and branch or PR diff audits.
meta-codereview-current-diff
Read the current uncommitted diff, run three independent reviewers (safety + tests-coverage + style) in parallel, then arbitrate a single BLOCK / BLOCKWITHOVERRIDE / PASSWITHNOTES verdict. Use before commit when you want a multi-perspective second-opinion instead of a single-reviewer agent loop.
omh-image-cards
This is an OMH img-summary workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
omh-ai-slop-cleaner
This is an OMH ai-slop-cleaner workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).