# Agent-ML-skills – Teach Codex/Claude/Cursor to stop making ML mistakes > Source: > Published: 2026-06-06 02:21:42+00:00 **Production-grade Machine Learning, Data Science & MLOps skills for AI coding agents.** Coding agents are great generalists but make the same **ML mistakes over and over**: leaking preprocessing into cross-validation, scoring imbalanced data with accuracy, forgetting `model.eval()` , building RAG with dense-only retrieval. `agent-ml-skills` is a curated pack of **15 battle-tested skills** that teach your agent how an experienced ML engineer actually works — so it stops guessing. Works with **Codex, Claude Code, Cursor, and OpenCode**. Install all skills into your agent with one command — **no install, no dependencies**: ``` # Codex npx agent-ml-skills install --target codex # Claude Code npx agent-ml-skills install --target claude # Cursor npx agent-ml-skills install --target cursor --scope project # OpenCode npx agent-ml-skills install --target opencode # Everything, everywhere npx agent-ml-skills install --target all ``` Browse what's inside first: ``` npx agent-ml-skills list ``` Then restart your agent (or start a new session) and it will pick the right skill up automatically when your task matches. A skill is a single Markdown file with YAML frontmatter telling the agent **when** to use it and **how** to do the task well: ``` --- name: sklearn-pipelines description: Use when building scikit-learn models that must not leak preprocessing... --- # scikit-learn Pipelines ...workflow, code patterns, pitfalls, hand-off... ``` Agents that support skills load the `description` up front and pull in the full body only when the task matches — so you get expert guidance **without bloating every prompt**. | Skill | Use when… | |---|---| exploratory-data-analysis | Starting on a new dataset — profiling, distributions, correlations, leakage & viz. | data-cleaning | Handling missing values, duplicates, types, outliers — with train-only imputation. | feature-engineering | Encoding, scaling, datetime/text/aggregation features, leakage-safe target encoding. | pandas-patterns | Writing idiomatic, vectorized, memory-efficient pandas (no `SettingWithCopyWarning` ). | imbalanced-data | The target is rare (fraud/churn/disease) — metrics, SMOTE, class weights, thresholds. | | Skill | Use when… | |---|---| sklearn-pipelines | Building scikit-learn models that must not leak preprocessing into CV. | pytorch-training-loop | Writing/reviewing a PyTorch loop — eval modes, AMP, checkpointing, devices. | model-evaluation | Choosing metrics, validating, calibration, confusion-matrix analysis. | hyperparameter-tuning | Optimizing params — random vs Optuna, leakage-safe CV, early stopping, budget. | | Skill | Use when… | |---|---| llm-finetuning | Fine-tuning an LLM — full vs LoRA/QLoRA, data formatting, transformers/PEFT/TRL. | rag-pipeline | Building RAG — chunking, embeddings, hybrid + reranking retrieval, eval. | | Skill | Use when… | |---|---| experiment-tracking | Experiments need comparing/reproducing — MLflow/W&B, what to log, registry. | reproducible-ml | A result must be reproducible — seeds, env pinning, data versioning, CUDA determinism. | ml-debugging | A model won't learn, loss is NaN, or metrics look too good — a diagnosis decision tree. | model-serving | Deploying behind an API — FastAPI, safe artifact loading, batching, ONNX, monitoring. | ``` npx agent-ml-skills [options] Commands list List available skills install Install skills into an agent Options -t, --target codex | claude | opencode | cursor | all --scope global (default) | project --skills comma-separated subset (default: all) --dir install into a custom directory (overrides target) -f, --force overwrite existing skills -h, --help show this help ``` **Examples** ``` # Just the LLM skills, into the current project npx agent-ml-skills install --target claude --skills rag-pipeline,llm-finetuning --scope project # Into a custom agent directory npx agent-ml-skills install --dir ./my-agent/skills # Re-install and overwrite npx agent-ml-skills install --target codex --force ``` | Target | Global | Project | |---|---|---| | Codex | `~/.codex/skills` | `.codex/skills` | | Claude Code | `~/.claude/skills` | `.claude/skills` | | OpenCode | `~/.config/opencode/skills` | `.opencode/skills` | | Cursor | — | `.cursor/rules` (flat `.md` rules) | **Leakage-safe by default.** Every data skill fits transforms on train only.**Concrete over abstract.** Real code patterns, not vague advice.**Pitfalls included.** Each skill ends with the mistakes agents actually make.**Composable.** Skills hand off to each other (EDA → cleaning → features → pipeline → eval → serving).**Zero-dependency installer.** Pure Node, nothing to install, nothing to trust. New skills and improvements are very welcome — see ** CONTRIBUTING.md**. Every skill is validated in CI: ``` node scripts/validate-skills.mjs ``` Open a [skill request](https://github.com/param087/agent-ml-skills/issues/new?template=skill_request.md) if there's an ML workflow you want your agent to master. Built by ** Param Bhavsar** — Google Summer of Code '19 @ TensorFlow, ex-HSBC. If this saves you a debugging session, a ⭐ helps others find it.