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 gitstq/awesome-ai-agent-skills --skill super-reviewergit clone --depth 1 https://github.com/gitstq/awesome-ai-agent-skillsWrote 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/gitstq/awesome-ai-agent-skills/super-reviewer)<a href="https://agentmods.dev/skills/gitstq/awesome-ai-agent-skills/super-reviewer"><img src="https://agentmods.dev/badge/skills/gitstq/awesome-ai-agent-skills/super-reviewer/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/gitstq/awesome-ai-agent-skills/super-reviewer"><img src="https://agentmods.dev/badge/skills/gitstq/awesome-ai-agent-skills/super-reviewer.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 | $0.00143 | $0.02284 |
| Opus 5.5 | $0.00057 | $0.00914 |
| Sonnet 5.5 | $0.00029 | $0.00457 |
| Haiku 4.5 | $0.00014 | $0.00228 |
Grade A, and why
super-reviewer 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Super Reviewer - AI Senior Code Review Engine
The most comprehensive AI code review skill. One skill replaces 10+ specialized review tools.
Why This Skill?
Most code review skills only check one dimension (style or security). Super Reviewer performs a 7-dimensional holistic review that mimics how a senior staff engineer reviews code in production:
- Correctness - Logic bugs, null/undefined handling, edge cases, race conditions
- Security - OWASP Top 10, injection attacks, auth bypasses, data exposure
- Performance - N+1 queries, unnecessary re-renders, memory leaks, algorithm complexity
- Code Style - Naming conventions, consistency, readability, DRY principle
- Architecture - SOLID principles, coupling/cohesion, design patterns
- Testing - Test coverage gaps, missing edge case tests, test quality
- Accessibility - WCAG 2.1 AA compliance, keyboard navigation, screen reader support
Quick Start
No setup needed. Simply say any of:
- "Review this code/PR"
- "Check this file for issues"
- "Do a security audit on..."
- "Review this code for performance"
The skill auto-detects language and framework.
Review Process
Phase 1: Context Analysis
- Read all changed files in the PR/diff
- Detect programming languages and frameworks used
- Identify affected modules, APIs, and data flows
- Check for existing tests related to changes
Phase 2: Multi-Dimensional Scan
For each changed file, run ALL of the following checks:
2.1 Correctness Check
- Null/undefined dereferences and missing null checks
- Off-by-one errors in loops and array indexing
- Unhandled promise rejections and async/await errors
- Incorrect boolean comparisons (== vs ===, assignment in conditions)
- Missing return statements in conditional branches
- Floating point precision issues
- String encoding problems
- Date/time zone handling errors
- Integer overflow in mathematical operations
2.2 Security Check (OWASP-aligned)
- Injection: SQL injection, NoSQL injection, command injection, XSS, template injection
- Auth: Hardcoded credentials, missing auth checks, insecure token handling
- Data: Sensitive data in logs, PII exposure, insecure data storage
- Crypto: Weak hash algorithms, missing salt, predictable random values
- Network: Missing rate limiting, CORS misconfiguration, open redirects
- Dependencies: Known vulnerable packages, outdated dependencies
- File ops: Path traversal, insecure file uploads, directory listing
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 · 255 lines · 143 tokens per session scan A 90bfd2db5292
super-reviewer is a skill published in the GitHub repository gitstq/awesome-ai-agent-skills (1 stars, last pushed 7mo ago), licensed MIT. It adds 143 tokens to every session and 2,284 once invoked, about $0.0006 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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