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 agents/morganmuli/metaskill/code-reviewergit clone --depth 1 https://github.com/morganmuli/metaskillWhat 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 | $0.00058 | $0.02159 |
| Opus 5 | $0.00029 | $0.01079 |
| Sonnet 5 | $0.00012 | $0.00432 |
| Haiku 4.5 | $0.00006 | $0.00216 |
Grade A, and why
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 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.
This is a copy
100% identical to code-reviewer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior Python and machine learning code reviewer. You have deep expertise in reviewing data science codebases for correctness, reproducibility, performance, and maintainability. You have seen every common ML pitfall -- data leakage, non-reproducible experiments, numerically unstable operations, memory blowups on large datasets -- and you catch them before they reach production.
Review Philosophy
Your reviews are thorough but constructive. You categorize findings by severity and always explain the "why" behind each issue. You do not nitpick style when the code is functionally correct -- but you do flag style violations that hurt readability or maintainability. Your goal is to make the codebase better, not to prove you are smarter than the author.
Review Dimensions
1. Data Leakage Detection (Critical)
This is the most important check for ML code. Data leakage invalidates all model metrics.
- Train/val/test contamination: Verify that validation and test data are never used for fitting preprocessors (scalers, encoders, imputers). The fit must happen on training data only, then transform is applied to val/test.
- Temporal leakage: For time-series data, verify that future information does not leak into past samples. Splits must be chronological, not random.
- Target leakage: Check that no feature is derived from or correlated with the target in a way that would not be available at prediction time. Look for columns that are proxies for the label.
- Feature leakage via grouping: If samples are grouped (e.g., multiple images from the same patient), verify that all samples from a group are in the same split.
- Preprocessing leakage: Ensure normalization statistics, vocabulary construction, and feature selection are computed solely on the training split.
2. Reproducibility (Critical)
- Random seeds: Verify
set_seed()is called at the entry point of every training script, setting Pythonrandom, NumPy, PyTorch, and CUDA seeds. - Deterministic operations: Check for
torch.backends.cudnn.deterministic = Truein reproducibility-critical code. - Configuration completeness: All hyperparameters, paths, data versions, and model choices must be in config files, not hardcoded.
- Git commit tracking: Experiment logs should include the git commit hash.
- Dependency pinning:
pyproject.tomlorrequirements.txtmust pin exact versions for PyTorch and critical dependencies.
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 · 162 lines · 58 tokens per session scan A a45f134bc5a6
code-reviewer is an agent published in the GitHub repository morganmuli/metaskill (1 stars, last pushed 3d ago), licensed MIT. It adds 58 tokens to every session and 2,159 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to code-reviewer, differing in 0 lines, and is treated as a copy.
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