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 tobihagemann/turbo --skill review-codegit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/review-code)<a href="https://agentmods.dev/skills/tobihagemann/turbo/review-code"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/review-code.svg" alt="Measured on agentmods" 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 | $0.00144 | $0.02343 |
| Opus 5 | $0.00072 | $0.01171 |
| Sonnet 5 | $0.00029 | $0.00469 |
| Haiku 4.5 | $0.00014 | $0.00234 |
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 today.
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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Code
Review code against type-specific criteria. Runs internal reviews and /peer-review in parallel by default. Returns combined structured findings.
Types: correctness, security, api-usage, consistency, simplicity, coverage
With a type argument, runs a single-concern internal review plus the peer review. With no type argument, runs all six internal reviews plus the peer review.
Step 1: Determine the Scope
Determine what to review:
- If a specific diff command was provided (e.g.,
git diff --cached,git diff origin/main...HEAD), use that. - If a file list or directory was provided, review those files directly (read the full files, not a diff).
- If neither was provided, default to diffing against the repository's default branch (detect via
gh repo view --json defaultBranchRef --jq '.defaultBranchRef.name'). If there are no changes against the default branch, stop and state that there is nothing to review.
State the resolved file list before continuing: add --name-only to a diff command, or list the files for a file or directory scope. When the scope is a staged diff, also state how many further files git diff HEAD --name-only reports, so a scope narrower than intended stays visible before fanning out.
Step 2: Run Reviews in Parallel
Each active type maps to a criteria reference file:
- Correctness — references/correctness-review.md
- Security — references/security-review.md
- API usage — references/api-usage-review.md
- Consistency — references/consistency-review.md
- Simplicity — references/simplicity-review.md
- Coverage — references/coverage-review.md
Full review activates all six types; a single-concern argument activates one. Skip peer review when instructed (e.g., "without peer review", "no peer", "internal only").
What ships with it
6 files 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.
- today Changed 30006adb6ad5
- 8d ago First seen · 91 lines · 144 tokens per session scan A 8c9178d5f27f
review-code is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed yesterday), licensed MIT. It adds 144 tokens to every session and 2,343 once invoked, about $0.0007 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.
Other skills, from other repositories
agentic-review
Deep multi-agent code review for local changes. Inspired by AmpCode's agentic review. Use when you want comprehensive analysis of staged changes, unstaged changes, specific commits, or branch differences. Spawns parallel specialized agents (security, performance, patterns, architecture) and synthesizes actionable…
qa
Full QA on all session changes using Codex as a second pair of eyes. Use when user says "QA", "full QA", "QA my changes", "QA all your changes", or "use codex to review". Runs git diff, sends changes to Codex for thorough review, and synthesizes findings.
best-practices
Apply modern web development best practices for security, compatibility, and code quality. Use when asked to "apply best practices", "security audit", "modernize code", "code quality review", or "check for vulnerabilities".
refactor-advisor
A code review helper that finds common design and maintenance problems in a codebase and suggests ways to restructure the code.
zh-code-reviewer
A Chinese-language code-review specialist that produces a structured review report. It examines coding style, possible bugs, performance, security, and design choices.
review-implementing
Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.