ML developer agent — machine learning model development
End-to-end ML workflow config — data analysis, preprocessing, model selection, cross-validation, evaluation and deployment prep, with scikit-learn pipeline patterns
- What
- End-to-end ML workflow config — data analysis, preprocessing, model selection, cross-validation, evaluation and deployment prep, with scikit-learn pipeline patterns
- Cost
- Free
- Needs
- a Python environment with sklearn, pandas and numpy available, plus a dataset to work on — the skill is guidance the agent follows, not software
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @ruvnet's ML developer agent: a full workflow profile covering exploratory data analysis, preprocessing (missing values, scaling, encoding), model development with scikit-learn pipelines, cross-validation and hyperparameter tuning, evaluation (confusion matrices, ROC/AUC, feature importance), and deployment prep with experiment-logging discipline. It is more agent profile than prompt pack — it declares tool budgets, path constraints (data/**, models/**), and human-approval gates for production deployment and large-scale training. Honest caveats: part of the ruv-swarm/rufflo agent ecosystem — its heaviest value is inside that setup; guidance only, it executes nothing itself. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a Python environment with sklearn, pandas and numpy available, plus a dataset to work on — the skill is guidance the agent follows, not software Install "ML developer agent — machine learning model development" for me. It gives my agent @ruvnet's end-to-end ML workflow profile: exploratory data analysis, preprocessing (missing values, scaling, encoding), model development with scikit-learn pipelines, cross-validation and hyperparameter tuning, evaluation (confusion matrices, ROC/AUC, feature importance), and deployment prep with experiment logging. Includes tool budgets, path constraints and human-approval gates for production deployment. MIT-licensed. Repository: https://github.com/ruvnet/ruflo/blob/main/.agents/skills/agent-data-ml-model/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "agent-data-ml-model". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. point the agent at my dataset and confirm before any production deployment; nothing else — it's a guide). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.