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 commands/affaan-m/ecc/code-reviewgit clone --depth 1 https://github.com/affaan-m/ECCWrote 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/commands/affaan-m/ecc/code-review)<a href="https://agentmods.dev/commands/affaan-m/ecc/code-review"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/code-review.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00390 |
| Opus 5 | $0.00005 | $0.00195 |
| Sonnet 5 | $0.00002 | $0.00078 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
code-review 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 today.
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.
What it actually says
Code Review Command
Review code changes for quality, security, and maintainability: $ARGUMENTS
Your Task
- Get changed files: Run
git diff --name-only HEAD - Analyze each file for issues
- Generate structured report
- Provide actionable recommendations
Check Categories
Security Issues (CRITICAL)
- Hardcoded credentials, API keys, tokens
- SQL injection vulnerabilities
- XSS vulnerabilities
- Missing input validation
- Insecure dependencies
- Path traversal risks
- Authentication/authorization flaws
Code Quality (HIGH)
- Functions > 50 lines
- Files > 800 lines
- Nesting depth > 4 levels
- Missing error handling
- console.log statements
- TODO/FIXME comments
- Missing JSDoc for public APIs
Best Practices (MEDIUM)
- Mutation patterns (use immutable instead)
- Unnecessary complexity
- Missing tests for new code
- Accessibility issues (a11y)
- Performance concerns
Style (LOW)
- Inconsistent naming
- Missing type annotations
- Formatting issues
Report Format
For each issue found:
**[SEVERITY]** file.ts:123
Issue: [Description]
Fix: [How to fix]
Decision
- CRITICAL or HIGH issues: Block commit, require fixes
- MEDIUM issues: Recommend fixes before merge
- LOW issues: Optional improvements
IMPORTANT: Never approve code with security vulnerabilities!
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.
- today First seen · 69 lines · 10 tokens per session scan A 0a4687d21a13
code-review is a command published in the GitHub repository affaan-m/ECC (246,988 stars, last pushed yesterday), licensed MIT. It adds 10 tokens to every session and 390 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 2026-09-03.
Other commands, from other repositories
awesome-chatgpt
Search awesome-ChatGPT-repositories for open-source GitHub repositories related to ChatGPT and LLMs.
init
Scaffold a new MindBase project (v2 layout). Usage: /mb:init [template] [-- mission ...].
commit
智能生成 Git 提交信息并提交.
pr
Handle the full workflow from current branch state to an open, CI-monitored pull request.
doctor.es
Diagnostica problemas de inferencia LLM en Mac: asiai doctor verifica el estado de los motores, conflictos de puertos, carga de modelos y estado de la GPU.
requirement-review
需求文档多角色评审(requirement-review):需求文档 → 7-Agent 并行评审 → 重构高质量需求文档(Runtime 受控流程,0-7 阶段状态机).