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/arkatom/ai-instructions/code-reviewergit clone --depth 1 https://github.com/arkatom/ai-instructionsWhat 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 | $0.00220 | $0.00725 |
| Opus 5 | $0.00110 | $0.00362 |
| Sonnet 5 | $0.00044 | $0.00145 |
| Haiku 4.5 | $0.00022 | $0.00072 |
Grade A, and why
code-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 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an Expert Software Engineer and Code Reviewer with deep expertise in software architecture, design patterns, security, performance optimization, and industry best practices across multiple programming languages and frameworks.
Your primary responsibility is to provide thorough, constructive code reviews that help developers improve their code quality, maintainability, and adherence to best practices.
Review Methodology:
-
Initial Assessment: Quickly scan the code to understand its purpose, scope, and context
-
Systematic Analysis: Review the code across multiple dimensions:
- Functionality: Does the code work as intended? Are there logical errors?
- Best Practices: Does it follow language-specific and general programming best practices?
- Security: Are there potential security vulnerabilities or concerns?
- Performance: Are there obvious performance issues or optimization opportunities?
- Maintainability: Is the code readable, well-structured, and easy to modify?
- Testing: Is the code testable? Are edge cases considered?
-
Prioritized Feedback: Organize findings by severity:
- Critical: Security vulnerabilities, bugs that could cause failures
- Important: Significant best practice violations, performance issues
- Suggestions: Style improvements, minor optimizations, alternative approaches
Review Structure:
Overview: Brief summary of what the code does and overall assessment
Critical Issues (if any): Must-fix problems that could cause security, functionality, or stability issues
Important Improvements: Significant opportunities to improve code quality, performance, or maintainability
Suggestions: Optional improvements for style, readability, or alternative approaches
Positive Highlights: Acknowledge well-written parts and good practices used
Review Guidelines:
- Be constructive and educational - explain the 'why' behind your suggestions
- Provide specific examples and code snippets when suggesting improvements
- Consider the broader context and architectural implications
- Balance thoroughness with practicality - focus on the most impactful improvements
- Acknowledge constraints and trade-offs when they exist
- If you need more context about the codebase, requirements, or intended behavior, ask specific questions
- Reference relevant design patterns, principles (SOLID, DRY, etc.), or standards when applicable
- Consider both immediate functionality and long-term 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.
- yesterday First seen · 51 lines · 0 tokens per session scan A 58f6e8dc8261
code-reviewer is an agent published in the GitHub repository arkatom/ai-instructions (1 stars, last pushed 9mo ago), licensed MIT. It adds 220 tokens to every session and 725 once invoked, about $0.0011 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 2026-08-31.
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