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 oyi77/1ai-skills --skill review-agentgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/review-agent)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/review-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/review-agent/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/oyi77/1ai-skills/review-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/review-agent.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 | $0.00026 | $0.01323 |
| Opus 5 | $0.00013 | $0.00661 |
| Sonnet 5 | $0.00005 | $0.00265 |
| Haiku 4.5 | $0.00003 | $0.00132 |
Grade A, and why
review-agent 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.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This agent reads code changes with adversarial intent, hunting for bugs, security holes, and broken contracts rather than style nits. Use it before merging anything that touches money, auth, or shared state. It reports concrete, reproducible findings ranked by severity.
Review Agent
Quick Reference — see parent for full agent ecosystem.
The Review Agent reads diffs with adversarial intent — assuming every line could hide a bug, security hole, or performance trap. It classifies findings by severity (P1–P3) and provides concrete fix recommendations, not vague warnings. Unlike human reviewers who fatigue after 20 minutes, the Review Agent checks every changed line systematically against classifiers for injection, logic errors, concurrency bugs, and convention violations.
When Not to Use
- Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
- Already established workflows — follow existing team conventions rather than introducing new frameworks.
- When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.
Dependencies
- Python 3.8+ or Node.js 18+
- Access to relevant APIs/services for your specific use case
- Basic understanding of the domain concepts
Commands
# Refer to the skill's usage section for specific commands
# Adapt these to your workflow
Key Responsibilities
- Adversarial analysis: Read every changed line as if it is wrong — look for injection, logic errors, off-by-one, race conditions, and undefined behavior
- Severity-ranked findings: Report issues as P1 (blocking), P2 (should fix), P3 (consider) with clear reproduction steps and fix recommendations
- Context-aware checks: Understand the project's conventions, framework patterns, and dependency versions to flag real issues — not boilerplate complaints
Code Example
"""Minimal review agent pattern — analyze a diff."""
import json, sys
def review_diff(diff_text: str) -> dict:
findings = []
lines = diff_text.split("\n")
for i, line in enumerate(lines):
if line.startswith("+") and "eval(" in line:
findings.append({
"file": "unknown", "line": i, "severity": "P1",
"type": "Code injection",
"finding": "eval() called with dynamic input",
"recommendation": "Replace with safe parser or AST-based evaluation"
})
if line.startswith("+") and "password" in line.lower() and "=" in line:
findings.append({
"file": "unknown", "line": i, "severity": "P1",
"type": "Secret exposure",
"finding": "Password literal in source code",
"recommendation": "Move to environment variable or secrets manager"
})
if line.startswith("+") and "raw(" in line.lower():
findings.append({
"file": "unknown", "line": i, "severity": "P2",
"type": "SQL injection risk",
"finding": "Raw SQL without parameterization",
"recommendation": "Use parameterized query or ORM"
})
return {
"findings": findings,
"summary": f"{len([f for f in findings if f['severity'] == 'P1'])} P1, "
f"{len([f for f in findings if f['severity'] == 'P2'])} P2",
"verdict": "blocked" if any(f["severity"] == "P1" for f in findings) else "approved"
}
if __name__ == "__main__":
diff = sys.stdin.read()
result = review_diff(diff)
print(json.dumps(result, indent=2))
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 Changed · +4 lines 39a8490b8de9
- 11d ago First seen · 138 lines · 26 tokens per session scan A 9cc89a08a5f9
review-agent is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,323 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 2026-08-30.
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