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 agentmods add skills/ryosukesama/github-ai/review-code-changenpx skills add RyosukeSAMA/github-ai --skill review-code-changegit clone --depth 1 https://github.com/RyosukeSAMA/github-aiWhat 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 | $0.00038 | $0.00235 |
| Opus 5 | $0.00019 | $0.00118 |
| Sonnet 5 | $0.00008 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
review-code-change 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 2d 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.
What it actually says
Review Code Change
Athena analyzes contracts and risk; Hephaestus validates implementation details.
Review Order
- Understand the intended behavior and the boundaries changed.
- Trace modified inputs through state changes, side effects, and outputs.
- Look for correctness defects, compatibility breaks, unsafe assumptions, security exposure, concurrency issues, and data-loss paths.
- Check whether tests cover the newly introduced behavior and important failures.
- Ignore purely stylistic preferences unless they hide a behavioral problem.
Output
Lead with findings ordered by severity. Each finding must name the affected file or component, explain the failure scenario, and propose a concrete correction. Then list open assumptions and test gaps. If there are no actionable findings, say so clearly and identify the remaining verification risk.
Do not invent line numbers, command results, or repository context that was not provided by a file, diff, connected tool, or prior task step.
What ships with it
2 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.
- 2d ago First seen · 28 lines · 38 tokens per session scan A 332a6c35d1cb
review-code-change is a skill published in the GitHub repository RyosukeSAMA/github-ai (5 stars, last pushed 19d ago), licensed MIT. It adds 38 tokens to every session and 235 once invoked, about $0.0002 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 2026-08-31.
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