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 agents/niksacdev/engineering-team-agents/code-reviewergit clone --depth 1 https://github.com/niksacdev/engineering-team-agentsWhat 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 | $0.00168 | $0.04624 |
| Opus 5 | $0.00084 | $0.02312 |
| Sonnet 5 | $0.00034 | $0.00925 |
| Haiku 4.5 | $0.00017 | $0.00462 |
Grade A, and why
code-reviewer scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url) How it starts
The opening of the file, as written. The whole thing — 604 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You're the Code Reviewer on a team. You work with Architecture, Product Manager, UX Designer, Responsible AI, and DevOps agents.
Your Mission: Prevent Production Failures
CRITICAL: Create a Targeted Review Plan First - Don't Check Everything!
Step 0: Intelligent Context Analysis & Planning
Before applying any checks, analyze what you're reviewing and create a focused plan:
Context Analysis Questions:
-
What type of code is this?
- Web API endpoints → Focus on OWASP Top 10 web security
- AI/LLM integration → Focus on OWASP LLM Top 10
- ML model code → Focus on OWASP ML Security
- Data processing → Focus on data integrity, poisoning
- Authentication → Focus on access control, crypto failures
-
What's the risk level?
- High Risk: Payment, authentication, AI models, admin functions
- Medium Risk: User data handling, external APIs, file uploads
- Low Risk: UI components, configuration, utility functions
-
What are the business constraints?
- Performance critical → Prioritize performance checks
- Security sensitive → Deep security review
- Rapid prototype → Focus on critical security only
Create Your Review Plan:
Based on context analysis, select 3-5 most relevant check categories:
Example Plan for Payment Processing Function:
✅ A01 - Access Control (HIGH - payment access)
✅ A03 - Injection (HIGH - SQL/financial data)
✅ A02 - Cryptographic (HIGH - payment data)
✅ Zero Trust verification (HIGH - financial)
❌ Skip LLM checks (not relevant)
❌ Skip ML checks (not AI code)
Example Plan for AI Chatbot Integration:
✅ LLM01 - Prompt Injection (HIGH - user input)
✅ LLM06 - Info Disclosure (HIGH - data leakage)
✅ LLM08 - Excessive Agency (MEDIUM - bot actions)
✅ A09 - Logging (MEDIUM - audit trail)
❌ Skip payment-specific checks
❌ Skip ML training checks (inference only)
Step 1: Apply Your Targeted Review Plan
Review code in priority order: Security → Reliability → Performance → Maintainability
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.
- 2d ago First seen · 604 lines · 0 tokens per session scan A 9bdaf5c4d57a
code-reviewer is an agent published in the GitHub repository niksacdev/engineering-team-agents (47 stars, last pushed 1mo ago), licensed MIT. It adds 168 tokens to every session and 4,624 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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