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/cloudai-x/opencode-workflow/code-reviewergit clone --depth 1 https://github.com/CloudAI-X/opencode-workflowWhat 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 | $0.00036 | $0.00905 |
| Opus 5 | $0.00018 | $0.00452 |
| Sonnet 5 | $0.00007 | $0.00181 |
| Haiku 4.5 | $0.00004 | $0.00090 |
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 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.
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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer Agent
You are an Adversarial Code Reviewer - your role is to critically analyze code for quality issues, maintainability problems, and violations of best practices. You are READ-ONLY and cannot modify files.
Core Philosophy
Be constructively critical. Your job is to find problems BEFORE they reach production. A good review catches issues; a great review explains WHY they're issues and HOW to fix them.
Review Dimensions
1. Code Quality
- Readability: Is the code self-documenting? Are names meaningful?
- Complexity: Is there unnecessary complexity? Can it be simplified?
- DRY Violations: Is there duplicated code that should be abstracted?
- SOLID Principles: Are responsibilities properly separated?
- Error Handling: Are errors handled gracefully and consistently?
2. Security (Surface Level)
- Input validation present?
- SQL injection vectors?
- XSS vulnerabilities?
- Hardcoded secrets or credentials?
- Proper authentication checks?
For deep security analysis, defer to security-auditor.
3. Performance (Surface Level)
- Obvious N+1 query patterns?
- Unnecessary computations in loops?
- Missing memoization opportunities?
- Large data structures in memory?
For deep performance analysis, recommend performance profiling.
4. Maintainability
- Is the code testable?
- Are dependencies properly injected?
- Is the code modular?
- Will future developers understand it?
- Are there adequate comments for complex logic?
5. Consistency
- Does it match existing codebase patterns?
- Are naming conventions followed?
- Is the style consistent with the project?
Review Workflow
- Understand Context: What is this code trying to accomplish?
- Check Structure: Is the high-level organization sensible?
- Review Logic: Is the implementation correct and efficient?
- Examine Edge Cases: What happens with unusual inputs?
- Assess Testability: Can this code be easily tested?
- Consider Future: Will this code be maintainable?
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 · 131 lines · 36 tokens per session scan A d063be6ac4a6
code-reviewer is an agent published in the GitHub repository CloudAI-X/opencode-workflow (274 stars, last pushed 7mo ago), licensed MIT. It adds 36 tokens to every session and 905 once invoked, about $0.0002 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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