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/cloudshipai/station/code-reviewnpx skills add cloudshipai/station --skill code-reviewgit clone --depth 1 https://github.com/cloudshipai/stationWhat 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 | $0.00018 | $0.00451 |
| Opus 5 | $0.00009 | $0.00226 |
| Sonnet 5 | $0.00004 | $0.00090 |
| Haiku 4.5 | $0.00002 | $0.00045 |
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 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.
What it actually says
Code Review Skill
When to Use
- User asks for code review or feedback
- Reviewing a pull request or diff
- Analyzing code quality or security
Review Checklist
1. Functionality
- Does the code do what it's supposed to?
- Are edge cases handled?
- Is error handling appropriate?
2. Code Quality
- Is the code readable and maintainable?
- Are functions and variables named clearly?
- Is there unnecessary duplication?
- Are comments helpful and accurate?
3. Security
- Input validation present?
- No hardcoded secrets?
- SQL injection prevention?
- XSS prevention?
4. Performance
- Efficient algorithms used?
- No N+1 query problems?
- Resources properly managed?
5. Testing
- Tests cover main functionality?
- Edge cases tested?
- Tests are readable?
Output Format
For each finding, provide:
- Location: File and line number
- Severity: Critical / High / Medium / Low
- Issue: Clear description of the problem
- Suggestion: How to fix it
- Example: Code snippet showing the fix (if applicable)
Example Review Comment
Location: src/api/users.go:42
Severity: High
Issue: SQL query built using string concatenation
Suggestion: Use parameterized queries to prevent SQL injection
Example:
// Before (vulnerable)
query := "SELECT * FROM users WHERE id = " + userID
// After (safe)
query := "SELECT * FROM users WHERE id = ?"
db.Query(query, userID)
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 · 75 lines · 18 tokens per session scan A 2f37cc6532e3
code-review is a skill published in the GitHub repository cloudshipai/station (429 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 451 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.
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