Borrowing it
Nothing to install: this file belongs to irahardianto/awesome-agv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/irahardianto/awesome-agv/main/.agents/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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/irahardianto/awesome-agv/code-review)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/code-review"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/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/irahardianto/awesome-agv/code-review"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/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 | $0.00044 | $0.01812 |
| Opus 5 | $0.00022 | $0.00906 |
| Sonnet 5 | $0.00009 | $0.00362 |
| Haiku 4.5 | $0.00004 | $0.00181 |
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 9d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Skill
Purpose
Systematically review code against the full antigravity rule set. Catches issues that linters miss: architectural violations, missing observability, business logic errors, pattern inconsistencies.
When to Invoke
- During the
/auditworkflow (as part of parallel subagent dispatch) - When user asks for a code review outside any workflow
- Best practice: Invoke in a fresh conversation (not the same one that authored the code) to avoid confirmation bias
Review Process
1. Scope the Review
Identify the files/features to review. Determine the review scope:
- Feature review — all files in a feature directory
- PR review — only changed files
- Full codebase audit — all features
2. Load the Rule Set
Read all applicable rules from .agents/rules/. Use rule-priority.md for severity classification.
3. Review Categories (Priority Order)
Review each file/feature against these categories, in order from rule-priority.md:
Critical (Must Fix)
- Security — injection, hardcoded secrets, broken auth
- Data loss — missing error handling on writes, no transaction boundaries
- Resource leaks — unclosed connections, missing cleanup
Major (Should Fix)
- Testability — I/O not behind interfaces, untested error paths
- Observability — missing logging on operations, no correlation IDs
- Error handling — empty catch blocks, swallowed errors
- Architecture — circular dependencies, wrong layer access
Minor (Nice to Fix)
- Pattern consistency — deviation from established codebase patterns
- Naming — unclear variable/function names
- Code organization — functions too long, mixed responsibilities
Enhancement (Backlog)
- Style — formatting issues or non-critical refactorings
- Documentation — missing comments on complex logic
4. Produce Findings
Output a structured findings document:
# Code Review: {Feature/Module Name}
Date: {date}
Reviewer: AI Agent (fresh context)
## Summary
- **Files reviewed:** N
- **Issues found:** N (X critical, Y major, Z minor, W enhancement)
## Critical Issues
- [ ] **[SEC]** {description} — [{file}:{line}](file:///path)
- [ ] **[DATA]** {description} — [{file}:{line}](file:///path)
## Major Issues
- [ ] **[TEST]** {description} — [{file}:{line}](file:///path)
- [ ] **[OBS]** {description} — [{file}:{line}](file:///path)
## Minor Issues
- [ ] **[PAT]** {description} — [{file}:{line}](file:///path)
## Enhancement Issues
- [ ] {description} — [{file}:{line}](file:///path)
## Rules Applied
List of rules referenced during this review.
What ships with it
12 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.
- languages/cpp.md 4.4 KB
- languages/csharp.md 4.3 KB
- languages/flutter.md 5.0 KB
- languages/go.md 2.8 KB
- languages/java.md 4.3 KB
- languages/kotlin.md 3.7 KB
- languages/php.md 3.7 KB
- languages/python.md 4.6 KB
- languages/ruby.md 4.3 KB
- languages/rust.md 4.7 KB
- languages/swift.md 4.2 KB
- languages/typescript.md 5.5 KB
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.
- 9d ago First seen · 178 lines · 44 tokens per session scan A 149cbe9486bb
code-review is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 18d ago), licensed MIT. It adds 44 tokens to every session and 1,812 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 2026-08-30.
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