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-quality-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-quality-review)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/code-quality-review"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/code-quality-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-quality-review"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/code-quality-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 | $0.00015 | $0.00675 |
| Opus 5.5 | $0.00006 | $0.00270 |
| Sonnet 5.5 | $0.00003 | $0.00135 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
code-quality-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 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.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Quality Review
Review the current branch diff for structural quality. Preserve behavior, but actively look for a simpler design that deletes concepts, branches, wrappers, or layers instead of moving complexity around.
Review Boundaries
- Review only the changed code and the surrounding code needed to judge it.
- Treat repository conventions and existing canonical helpers as the source of truth.
- Skip formatter and linter findings that automation already reports.
- Prefer a small set of high-confidence structural findings over cosmetic notes.
- Do not propose a large refactor unless its benefit is clear and it stays within the change's ownership boundary.
Review Criteria
Check each meaningful change for:
- Simpler structure — Can a different model or ownership boundary remove branches, modes, helpers, or layers?
- Cohesive control flow — Flag scattered special cases, repeated conditions, deep nesting, and mixed responsibilities.
- Useful abstractions — Flag thin wrappers, generic magic, speculative extension points, and duplicate helpers.
- Clean boundaries — Keep feature logic in its canonical module and make types and invariants explicit.
- Reasonable file size — Treat a change that pushes a file above 1,000 lines as a decomposition signal unless the file has a clear structural reason to stay whole.
- Sound orchestration — Flag unnecessarily sequential independent work and updates that can leave related state partially applied when a clearer atomic design exists.
- Legibility — Prefer direct, boring code with clear names and comments that explain only non-obvious reasons.
Finding Bar
Report a finding only when you can name:
- the file and relevant hunk;
- the concrete maintenance cost or failure mode;
- the smallest practical remedy; and
- why the remedy is better than the current design.
Treat these as blockers unless the implementation has a clear justification:
- a structural regression or avoidable boundary leak;
- a file newly crossing 1,000 lines without useful decomposition;
- ad hoc branching added to an already busy flow;
- duplicated logic where a canonical helper exists;
- an abstraction, cast, or optional contract that adds indirection without clarity; or
- a clear simplification that removes substantial incidental complexity.
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 Changed · -119 lines · -11 tokens per session 131c36456448
- 1mo ago First seen · 196 lines · 26 tokens per session scan A ed96ca9dfd79
code-quality-review is a skill published in the GitHub repository jellydn/my-ai-tools (124 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 675 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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