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 jellydn/my-ai-tools --skill code-reviewgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/code-review)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/code-review"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/code-review/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/jellydn/my-ai-tools/code-review"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/code-review.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 | $0.00020 | $0.02610 |
| Opus 5.5 | $0.00008 | $0.01044 |
| Sonnet 5.5 | $0.00004 | $0.00522 |
| Haiku 4.5 | $0.00002 | $0.00261 |
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 21d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Two-axis review of the diff between HEAD and a fixed point the user supplies:
- Conventions — does the code follow this repo's documented coding standards and Tidy First practices?
- Intent — does the change faithfully do what it claims to do?
Both axes run as parallel sub-agents so they don't pollute each other's context, then this skill aggregates their findings.
Where This Fits
This skill owns micro / local quality — conventions, clarity, correctness of individual changes. It answers: "Is this change well-crafted and does it do what it says?"
For macro / structural quality (architecture, code judo, 1k-line limits, abstraction quality), use code-quality-review. That skill asks: "Is there a dramatically simpler structure hiding inside this implementation?"
| Concern | code-review (this skill) | code-quality-review |
|---|---|---|
| Clean code & naming | ✅ Primary owner | — |
| Tidy First practices | ✅ Primary owner | — |
| Behavior matches commits | ✅ Primary owner | — |
| Guard clauses, helper vars | ✅ Primary owner | — |
| File under 1k lines | Flag if crossed | Enforce strictly |
| Structural simplification | Note opportunities | Demand code judo |
| Abstraction quality | Flag thin wrappers | Delete unnecessary layers |
Companion Skills
A complete quality pipeline, in order:
| Phase | Skills | Purpose |
|---|---|---|
| 1. Discovery | blindspot-passcontext-discovery |
Find unknown unknowns and gather project context before starting |
| 2. During implementation | implementation-logger |
Track deviations from plan as you go |
| 3. Pre-review cleanup | slop |
Remove AI-generated clutter so the review focuses on substance |
| 4. Review | code-review (this skill) |
Conventions + Intent, side by side |
| 5. Structural audit | code-quality-review |
Code judo, 1k-line limits, abstraction quality |
| 6. Fix & wrap | pr-review → commit-atomic → quiz-me |
Apply fixes, group into logical commits, verify understanding |
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.
- 21d ago Changed · +4 lines 2c2a842bf256
- 1mo ago Changed · -65 tokens per session f4d55086ab51
- 1mo ago First seen · 183 lines · 85 tokens per session scan A f49495c9a62e
code-review is a skill published in the GitHub repository jellydn/my-ai-tools (124 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 2,610 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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