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/autohandai/code-cli/code-reviewernpx skills add autohandai/code-cli --skill code-reviewergit clone --depth 1 https://github.com/autohandai/code-cliWhat 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 | $0.00026 | $0.00782 |
| Opus 5 | $0.00013 | $0.00391 |
| Sonnet 5 | $0.00005 | $0.00156 |
| Haiku 4.5 | $0.00003 | $0.00078 |
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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Staff-level Software Engineer performing a comprehensive code review. Your review must be thorough, actionable, and prioritized — not a style guide checklist.
Review Methodology
Analyze the codebase across exactly 10 dimensions, scoring each 1-5 and providing specific, actionable findings with file paths and line numbers.
The 10 Review Dimensions
-
Architecture & Design — Is the code well-structured? Are responsibilities clearly separated? Are abstractions appropriate (not premature, not missing)?
-
Security — Are there injection vulnerabilities (SQL, XSS, command)? Hardcoded secrets? Unsafe deserialization? Missing input validation at trust boundaries?
-
Error Handling & Resilience — Are errors caught, logged, and handled? Are there unhandled promise rejections? Missing try/catch around I/O? Silent failures?
-
Performance & Scalability — N+1 queries? Unbounded loops? Missing pagination? Blocking I/O on hot paths? Memory leaks (event listeners, timers)?
-
Type Safety & Correctness — Are types precise (not
any)? Are null checks present where needed? Are edge cases handled (empty arrays, undefined, NaN)? -
Testing & Testability — Is there test coverage for critical paths? Are tests testing behavior (not implementation)? Is the code structured for testability (dependency injection, pure functions)?
-
Maintainability & Readability — Can a new team member understand this? Are names descriptive? Is complexity justified? Are there dead code paths?
-
Dependencies & Imports — Are dependencies up-to-date and maintained? Are there circular imports? Is the dependency tree reasonable? Any known vulnerabilities?
-
API Design & Contracts — Are function signatures clear? Are return types consistent? Are breaking changes handled? Is the public API minimal and well-documented?
-
DevOps & Operational Readiness — Are there proper logs? Health checks? Configuration management? Graceful shutdown? Retry logic for external calls?
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 · 65 lines · 26 tokens per session scan A f6737b47bd01
code-reviewer is a skill published in the GitHub repository autohandai/code-cli (181 stars, last pushed 5d ago), licensed Apache-2.0. It adds 26 tokens to every session and 782 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.
Other skills, from other repositories
neuron-test-engineer
Write tests for Neuron AI agents, RAG systems, workflows, and tools using the built-in testing utilities. Use this skill when the user mentions testing agents, writing unit tests, mocking AI providers, testing tool execution, verifying RAG retrieval, testing workflow behavior, or creating test cases for Neuron AI…
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
neuron-structured-output
Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…
neuron-agent-builder
Create and configure Neuron AI agents with providers, tools, instructions, and memory. Use this skill whenever the user mentions building agents, creating AI assistants, setting up LLM-powered chat bots, configuring chat agents, or wants to create an agent that can talk, use tools, or handle conversations. Also…
neuron-debugger
Debug and monitor Neuron AI applications with Inspector APM, event observability, logging, and performance analysis. Use this skill whenever the user mentions debugging, monitoring, observability, performance analysis, tracing, Inspector, or needs to understand why an agent is behaving a certain way. Also trigger for…
neuron-rag-specialist
Implement RAG (Retrieval-Augmented Generation) with Neuron AI including vector stores, embeddings providers, document loaders, and retrieval strategies. Use this skill whenever the user mentions RAG, retrieval, vector search, document retrieval, semantic search, knowledge bases, chat with documents, or wants to build…