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 skills add omnigentx/jarvis --skill review-implementinggit clone --depth 1 https://github.com/omnigentx/jarvisWrote 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/skills/omnigentx/jarvis/review-implementing)<a href="https://agentmods.dev/skills/omnigentx/jarvis/review-implementing"><img src="https://agentmods.dev/badge/skills/omnigentx/jarvis/review-implementing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/omnigentx/jarvis/review-implementing"><img src="https://agentmods.dev/badge/skills/omnigentx/jarvis/review-implementing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00028 | $0.00380 |
| Opus 5.5 | $0.00011 | $0.00152 |
| Sonnet 5.5 | $0.00006 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
review-implementing 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 1mo 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
Implementing Review Feedback
Adapted from mhattingpete/claude-skills-marketplace.
When to Use
- After receiving a FAIL verdict in a meeting
- When reviewer lists issues to address
- When code review comments need to be implemented
Systematic Workflow
1. Parse Feedback
- Extract individual issues from the review
- Categorize: Critical → Important → Minor
- Clarify ambiguous items before starting
2. Prioritize
Fix in order:
- Critical — Must fix before anything else
- Important — Fix before requesting re-review
- Minor — Fix if time permits, or note for later
3. Fix Each Issue
For each item:
- Locate the relevant code
- Understand the reviewer's intent
- Make the change — minimal, focused fix
- Verify — check syntax, run relevant tests
- Report — show what you changed in your
speak()
4. Request Re-Review
After fixing Critical and Important issues:
- Summarize what you fixed
- Show test results
- Ask reviewer to re-check
Handling Edge Cases
Conflicting feedback:
- Ask PM for guidance in the meeting
Breaking changes required:
- Notify team before implementing
Tests fail after fix:
- Fix tests before reporting done
Can't reproduce the issue:
- Ask reviewer for more details
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.
- 1mo ago First seen · 56 lines · 28 tokens per session scan A 095e80473d23
review-implementing is a skill published in the GitHub repository omnigentx/jarvis (39 stars, last pushed 11d ago), licensed MIT. It adds 28 tokens to every session and 380 once invoked, about $0.0001 per session on Opus 5.5. 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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