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/aaaaqwq/agi-super-team/code-reviewnpx skills add aAAaqwq/AGI-Super-Team --skill code-reviewgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/code-review)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/code-review"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/code-review.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.06995 |
| Opus 5 | $0.00005 | $0.03497 |
| Sonnet 5 | $0.00002 | $0.01399 |
| Haiku 4.5 | $0.00001 | $0.00700 |
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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- code-review — 97% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 762 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Comprehensive AI-powered code review for PRs and local changes — enterprise-grade alternative to CodeRabbit
When to use
- "code review"
- "review my PR"
- "review PR #123"
- "check my changes"
- "what's wrong with my code"
- "security review"
- "full review"
- "/review"
- "/review-pr"
Dependencies
- External:
ghCLI (GitHub),git
Modes
1. Local review (uncommitted changes)
Reviews git diff — changes not yet committed.
2. Branch review (vs main/master)
Reviews all changes in current branch compared to main.
3. PR review (GitHub)
Fetches diff from GitHub PR and can post comments.
4. Focused review
User can request specific focus: security, performance, bugs, style, etc.
How to execute
Step 0: Check if review needed
Skip review if:
- PR is draft (
gh pr view --json isDraft) - PR is already closed/merged
- Only documentation changes (.md, .txt, LICENSE)
- Only config changes (.json, .yaml, .toml) without code impact
- Trivial changes (<5 lines, whitespace only, version bumps)
Inform user and ask to confirm if they still want review.
Step 1: Determine mode
Ask user or detect automatically:
- If PR number provided → PR review
- If uncommitted changes exist → local review
- If on feature branch → branch review
- If specific focus requested → apply focus filter
Step 2: Get diff
Local:
git diff HEAD
Branch (vs main):
DEFAULT_BRANCH=$(git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@' || echo "main")
git diff $DEFAULT_BRANCH...HEAD
PR:
gh pr diff <PR_NUMBER>
Step 3: Get context
For thorough review, read related files:
# List changed files
git diff --name-only HEAD
# Read each file fully for context
# Check package.json for dependencies
# Check tsconfig/eslint config for project standards
Step 3b: Filter pre-existing issues
Before reporting an issue, check if it was introduced in this PR:
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.
- yesterday First seen · 762 lines · 11 tokens per session scan A 1781dcd08ef0
code-review is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed 2d ago), licensed MIT. It adds 11 tokens to every session and 6,995 once invoked, about $0.0001 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 2026-09-05.
Other skills, from other repositories
code-review
The depth half of a review - the dimensions a diff is read against (correctness, boundaries, concurrency, failure paths, secrets, data access, structure, test quality) and the rule that a finding is refuted before it is reported. The verdict stays with the reviewer agent. Use when reviewing a diff or a pull request…
deslop
The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
pr-feedback
Work a reviewer's comments on a pull request to the end - all three comment surfaces enumerated before any is triaged, every comment fixed, declined with a reason, or ticketed, push before you reply. Use when a PR comes back with review feedback or a red check.
aigon-feature-code-review
Review feature - code review with fixes by a different agent.
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.
aigon-feature-code-revise
Revise the current feature worktree after code review — decide accept/revert/modify.