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 euxx/claude-skills-for-copilot --skill code-reviewgit clone --depth 1 https://github.com/euxx/claude-skills-for-copilotWrote 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/euxx/claude-skills-for-copilot/code-review)<a href="https://agentmods.dev/skills/euxx/claude-skills-for-copilot/code-review"><img src="https://agentmods.dev/badge/skills/euxx/claude-skills-for-copilot/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/euxx/claude-skills-for-copilot/code-review"><img src="https://agentmods.dev/badge/skills/euxx/claude-skills-for-copilot/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00041 | $0.00982 |
| Opus 5.5 | $0.00016 | $0.00393 |
| Sonnet 5.5 | $0.00008 | $0.00196 |
| Haiku 4.5 | $0.00004 | $0.00098 |
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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Provide a code review for the given code changes (e.g. local changes, a file, or a directory).
Steps
Make a todo list first, then follow these steps precisely:
1. Gather context
- Use a sub-agent to list file paths of relevant conventions files in this priority order: AGENTS.md, then CLAUDE.md, then GEMINI.md. Include the root file (if one exists), plus files in directories whose files were changed.
- Use a sub-agent to scan the code changes and return a summary of the change.
2. Parallel review
Launch 5 parallel sub-agents to independently review the change. Each agent returns a list of issues with the reason flagged (e.g. conventions-file adherence, bug, historical context):
- Agent 1 — Conventions-file compliance: Audit changes against AGENTS.md or similar conventions files (CLAUDE.md, GEMINI.md), using priority order AGENTS.md -> CLAUDE.md -> GEMINI.md. Note that these files are guidance for AI while writing code; not all instructions apply during code review.
- Agent 2 — Bug scan: Shallow scan for obvious bugs, focusing only on the changed lines. Prioritize large bugs; ignore nitpicks and likely false positives.
- Agent 3 — Git history: Read git blame and history of modified code to identify bugs in light of historical context.
- Agent 4 — Code comments: Read comments in modified files and verify the changes comply with any guidance in those comments.
- Agent 5 — Security & quality: Look for security issues and code quality problems that would directly impact functionality.
3. Confidence scoring
For each issue found in step 2, launch a parallel sub-agent to score it from 0–100 (give this rubric to each agent verbatim):
- 0 — Not confident at all. False positive that doesn't stand up to light scrutiny, or a pre-existing issue.
- 25 — Somewhat confident. Might be real, but unverified. If stylistic, not explicitly called out in the applicable conventions file.
- 50 — Moderately confident. Verified as real, but may be a nitpick or rare in practice. Not very important relative to the rest of the change.
- 75 — Highly confident. Double-checked; very likely a real issue that will be hit in practice. Important and directly impacts functionality, or directly mentioned in the applicable conventions file.
- 100 — Absolutely certain. Confirmed real issue that will happen frequently. Evidence directly confirms it.
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 · 96 lines · 41 tokens per session scan A 22e079a97861
code-review is a skill published in the GitHub repository euxx/claude-skills-for-copilot (4 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 982 once invoked, about $0.0002 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 2026-08-31.
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