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/omnigentx/jarvis/code-reviewnpx skills add omnigentx/jarvis --skill code-reviewgit clone --depth 1 https://github.com/omnigentx/jarvisWhat 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 | $0.00012 | $0.00450 |
| Opus 5 | $0.00006 | $0.00225 |
| Sonnet 5 | $0.00002 | $0.00090 |
| Haiku 4.5 | $0.00001 | $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 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.
What it actually says
Code Review Skill
You are performing a code review. Follow this protocol for consistent, actionable feedback.
Review Process
Step 1: Understand the Context
- Read the project brief and any related documentation in the workspace
- Understand what the author was trying to accomplish
- Check
team_roster.jsonto know who wrote what
Step 2: Review the Code
- Read all relevant files in the workspace
- Check for:
- Correctness — Does the code do what it's supposed to?
- Quality — Is it readable, maintainable, well-structured?
- Edge cases — Are there unhandled scenarios?
- Security — Any obvious security issues?
- Performance — Any obvious performance problems?
Step 3: Write Your Review
Write your review to: reviews/<step_name>_review.md
Include:
- Summary — Overall assessment
- Issues found — Specific problems with file/line references
- Suggestions — Improvements that aren't blockers
- Verdict — Your final decision
Step 4: Declare Verdict
Always end your review with one of these exact strings:
✅ When code is acceptable:
[DECISION] VERDICT: PASS — <brief reason>
❌ When code needs fixes:
[DECISION] VERDICT: FAIL — <brief reason with key issues>
Communication
After writing your review:
- Use
send_email(to="<author_name>", body="...", my_name="<your_name>")to notify the author - Include a summary of key issues if FAIL
- Be constructive — suggest fixes, don't just point out problems
As a Code Author (receiving review)
When you receive a FAIL verdict:
- Read the review feedback carefully
- Fix the identified issues in your workspace files
- Message the reviewer:
send_email(to="<reviewer_name>", body="Fixes applied, ready for re-review", my_name="<your_name>")
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 · 62 lines · 12 tokens per session scan A 28a1fb958292
code-review is a skill published in the GitHub repository omnigentx/jarvis (35 stars, last pushed 5d ago), licensed MIT. It adds 12 tokens to every session and 450 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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