Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/matlab-code-reviewer/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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/zhnnky329/mathmodeling-skills/matlab-code-reviewer)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/matlab-code-reviewer"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/matlab-code-reviewer/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/zhnnky329/mathmodeling-skills/matlab-code-reviewer"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/matlab-code-reviewer.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 | $0.00046 | $0.00431 |
| Opus 5 | $0.00023 | $0.00216 |
| Sonnet 5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
matlab-code-reviewer 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 11d 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
Preconditions
- MATLAB code and
code/matlab/Qx/qx_code_plan.mdexist. - Approved decision, method card, data profile, and run summary are available.
- MATLAB or 北太天元 runtime availability is known.
Workflow
- Resolve approved main/baseline scope and any activated fallback.
- Inspect and run the code when a compatible runtime is available.
- Evaluate:
syntaxinput_contractmethod_alignmentreproducibilityoutput_contract
- Include compatibility evidence for toolbox usage,
jsonencode, file I/O, plotting/export, and 北太天元 constraints. - Add only relevant numerical, feasibility, leakage, or scale checks.
- If runtime is unavailable, use
NOT_RUNrather than claiming execution success. - If asked to fix findings, patch minimally and rerun affected checks.
- Save
code/matlab/Qx/reviews/qx_matlab_review.json.
Review Schema
Use the same schema as python-code-reviewer, with:
"language": "matlab"runtimecompatibility_target- optional
compatibilitycheck
Required named checks use PASS, FAIL, or justified NOT_APPLICABLE. Runtime-dependent checks use NOT_RUN when execution was impossible; this blocks G3 until executed.
Rules
- Do not pad pass items.
- Do not fabricate MATLAB/北太天元 execution.
- Do not approve unavailable toolbox dependencies without an explicit target exception.
- Do not change the mathematical model silently.
- Do not require a duplicate Markdown review.
Verification
- Approved main and baseline scope is enforced.
- Compatibility constraints are checked.
- Run summary and outputs agree.
- Verdict follows required check statuses and runtime evidence.
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.
- 11d ago First seen · 53 lines · 46 tokens per session scan A 9b6bea5e2d5d
matlab-code-reviewer is a skill published in the GitHub repository zhnnky329/MathModeling-skills (847 stars, last pushed 16d ago), licensed MIT. It adds 46 tokens to every session and 431 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 2026-08-30.
Other skills, from other repositories
adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
gsd-ns-review
Route to the appropriate quality / review skill based on the user's intent. gsd-code-review-fix was absorbed by gsd-code-review --fix in #2790.
issue
Use when starting a chain from a GitHub issue — turning an issue URL or number into a triaged, planned, dispatched, and reviewed pull request. Classifies the thread (bug → root-cause discipline, feature → plan chain, question → drafted reply), synthesizes a spec from the issue's own acceptance criteria, then runs the…
gitnexus
A code-graph analysis add-on for examining an existing codebase, including symbols, call paths, execution flows, and effects across repositories. It can query GitNexus through its command-line or MCP interfaces.
cleanup-code-inspections
Reduce technical debt and improve code quality by systematically resolving static analysis warnings.
dorodango
Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.