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/abuango/pos-ai/code-reviewnpx skills add abuango/pos-ai --skill code-reviewgit clone --depth 1 https://github.com/abuango/pos-aiWhat 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 | $0.00041 | $0.00928 |
| Opus 5 | $0.00020 | $0.00464 |
| Sonnet 5 | $0.00008 | $0.00186 |
| Haiku 4.5 | $0.00004 | $0.00093 |
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 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Skill
You are performing a code review for the POS. Reviews ensure code quality, security, and adherence to the approved plan.
Setup
Before starting: check .handoff/sessions/ for active sessions, read context status.yaml, run git status. Follow .rules/universal.md (Plan -> Approve -> Execute).
Review Context
-
Load Review Context
- Identify the project and task being reviewed
- Read the approved plan from
{teams_dir}/{team}/projects/{project}/plans/ - Read the work log from
{teams_dir}/{team}/projects/{project}/logs/ - Understand what was supposed to be implemented
- Check for prior artifacts:
source scripts/lib-artifacts.sh && artifact_find {context} plan
-
Identify Changed Files
- Use git diff if available:
git diff --name-only - Or ask for the list of modified files
- Read each modified file
- Use git diff if available:
Review Checklist
Plan Compliance
- Implementation matches the approved plan
- No scope creep (extra features not in plan)
- All planned tasks are addressed
- Correct files were modified
Code Quality
- Code is clean and readable
- Naming conventions followed
- No unnecessary complexity
- DRY principle applied appropriately
- Functions/methods have single responsibility
- Error handling is appropriate
Security (CRITICAL)
- No hardcoded secrets or credentials
- Input validation present where needed
- No SQL injection vulnerabilities
- No XSS vulnerabilities
- No command injection risks
- Authentication/authorization checked
- Sensitive data handled securely
Testing
- Unit tests included for new code
- Tests are meaningful (not just coverage)
- Edge cases considered
- Tests pass locally
Performance
- No obvious performance issues
- No N+1 query patterns
- Appropriate use of caching
- No memory leaks
Documentation
- Complex logic is commented
- Public APIs documented
- README updated if needed
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 142 lines · 41 tokens per session scan A 55b504e2153f
code-review is a skill published in the GitHub repository abuango/pos-ai (2 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 928 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 2026-08-31.
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