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 open-octo/octo-agent --skill code-reviewgit clone --depth 1 https://github.com/open-octo/octo-agentWrote 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/open-octo/octo-agent/code-review)<a href="https://agentmods.dev/skills/open-octo/octo-agent/code-review"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/code-review/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/open-octo/octo-agent/code-review"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/code-review.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 | $0.00083 | $0.00824 |
| Opus 5.5 | $0.00033 | $0.00330 |
| Sonnet 5.5 | $0.00017 | $0.00165 |
| Haiku 4.5 | $0.00008 | $0.00082 |
Grade A, and why
code-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 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review local code changes. Choose the reviewer based on where the implementation context lives:
- You implemented the changes in this session (or took part in the design/implementation conversation): dispatch an isolated sub-agent — a fresh agent with no knowledge of this session's implementation decisions. This eliminates the bias of reviewing your own work.
- Fresh session, review-only request (the user opened this session just to have you review a diff/PR you had no part in): you already ARE the unbiased fresh pair of eyes — review the code yourself, following the same template in code-reviewer.md. Spawning a sub-agent here only adds latency and a context hand-off with zero isolation benefit. Skip step 2 and produce the review report directly.
Process
1. Gather context for the review
Collect everything the reviewer needs:
# What changed
git log --oneline main..HEAD
git diff main...HEAD
git diff
git diff --cached
Also identify:
- Tech design doc path (if one exists in conversation context or commit messages)
- Which services/modules are touched
- Test files added or modified
2. Dispatch the sub-agent (only when you wrote the code)
Call the sub_agent tool with subagent_type: "code-review" — a read-only reviewer that starts with zero context. Pass it the diff range, file list, and tech design reference in the prompt — but NOT your implementation reasoning or conversation history.
If you need to review multiple independent changes (e.g., several PRs), dispatch one sub-agent per change in the same round — they run in parallel. When a dispatch comes back as a handle rather than a result, end your turn and use the completion notification when it arrives. Do not poll sub_agent_status while waiting; use it only if you suspect an agent is stuck or need to list running agents.
The sub-agent prompt should follow the template in code-reviewer.md.
Fill in:
{DESCRIPTION}— brief summary of what these changes do{TECH_DESIGN_PATH}— path to tech design doc (or "none"){BASE_SHA}/{HEAD_SHA}— git range{DIFF_STAT}— output ofgit diff --stat
What ships with it
1 file 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.
- 1mo ago Changed e4e8b44ac789
- 1mo ago Changed · +3 lines · +16 tokens per session af9d5585e8e5
- 1mo ago First seen · 62 lines · 67 tokens per session scan A 63410f747e26
code-review is a skill published in the GitHub repository open-octo/octo-agent (135 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 824 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 2026-08-30.
Other skills, from other repositories
ha-code-review
Hope-native review of uncommitted, staged, commit, branch, or PR changes: discover concrete regressions, independently verify candidates, and report actionable findings first without speculative noise.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
github-code-review
Review PRs: diffs, inline comments via gh or REST.
github-pr-workflow
GitHub PR lifecycle: branch, commit, open, CI, merge.
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
pr-writing-review
Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer…