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.
git clone --depth 1 https://github.com/andisab/swe-marketplaceWrote 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/agents/andisab/swe-marketplace/ml-scikit-learn-expert)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/ml-scikit-learn-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-scikit-learn-expert/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/agents/andisab/swe-marketplace/ml-scikit-learn-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-scikit-learn-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00514 |
| Opus 5.5 | $0.00018 | $0.00206 |
| Sonnet 5.5 | $0.00009 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
scikit-learn-expert 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 today.
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
91% identical to scikit-learn-expert — 11 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.
What it actually says
Focus Areas
- Data preprocessing and transformation techniques
- Feature engineering and selection methods
- Model selection and comparison
- Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- Evaluation metrics for regression and classification
- Building and validating pipelines
- Understanding and applying ensemble methods
- Handling imbalanced datasets
- Cross-validation techniques
- Interpreting model performance and outputs
Approach
- Start with a clear understanding of the problem and dataset
- Choose appropriate preprocessing steps for scaling and encoding
- Split data into training and testing sets before any analysis
- Use cross-validation to ensure robustness of model evaluation
- Iterate on feature selection to identify the most predictive features
- Experiment with different models and hyperparameters systematically
- Evaluate models using appropriate metrics for the task
- Focus on minimizing overfitting through regularization and validation
- Document assumptions, findings, and decisions thoroughly
- Rely on scikit-learn's extensive documentation for advanced usage
Quality Checklist
- Code follows PEP 8 guidelines
- Data is cleaned and preprocessed appropriately
- Features are scaled and/or transformed as necessary
- Models are trained, validated, and tested on separate data
- Hyperparameters are optimized using cross-validation
- Model evaluation metrics are clearly justified and reported
- Pipelines are constructed for reproducibility
- Code is modular with reusable components
- Results are compared with baseline models
- Insights and next steps are clearly communicated
Output
- Preprocessed dataset ready for modeling
- Scikit-learn pipelines encapsulating complete workflow
- Well-documented Jupyter notebooks or scripts
- Comparison of different models and their performance metrics
- Hyperparameter tuning results and best model configuration
- Visualizations of model performance and data insights
- Comprehensive report or presentation summarizing the findings
- Recommendations based on model insights and understandings
- Clear documentation of methodology and codebase
- Readiness for deployment with model.pkl or similar artifacts
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.
- today First seen · 66 lines · 46 tokens per session scan A 76fb2bfab71f
scikit-learn-expert is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 514 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to scikit-learn-expert, differing in 11 lines, and is treated as a copy.
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