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ai-ml-data-science

ai-ml-data-science is a skill for Claude Code from joaoguirunas/team-os. It costs 43 tokens per session (4,143 once invoked), scanned A, original, MIT.

A workflow for machine learning and data science tasks, from exploring data to preparing a model for production.

In plain words
What is it for?
Use it for exploratory data analysis, feature engineering, modelling, evaluation, and production handoff.
Why use it?
It organizes work such as finding patterns in data, creating useful inputs, evaluating models, and handing results over for use.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

not rated 5repo changed 1mo ago A scan 43 tokens original MIT

Good fit Use it for exploratory data analysis, feature engineering, modelling, evaluation, and production handoff.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/joaoguirunas/team-os/ai-ml-data-science
View source ↗ joaoguirunas/team-os
Install

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.

Any agent
npx skills add joaoguirunas/team-os --skill ai-ml-data-science
Clone the repo
git clone --depth 1 https://github.com/joaoguirunas/team-os

Made for: Claude Code.

Wrote 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.

agentmods badge for ai-ml-data-science

README.md
[![agentmods](https://agentmods.dev/badge/skills/joaoguirunas/team-os/ai-ml-data-science/github.svg)](https://agentmods.dev/skills/joaoguirunas/team-os/ai-ml-data-science)
Your own site
<a href="https://agentmods.dev/skills/joaoguirunas/team-os/ai-ml-data-science"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/ai-ml-data-science/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.

agentmods 80×15 button for ai-ml-data-science

Your own site · 80×15
<a href="https://agentmods.dev/skills/joaoguirunas/team-os/ai-ml-data-science"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/ai-ml-data-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,143 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.04143
Opus 5.5 $0.00017 $0.01657
Sonnet 5.5 $0.00009 $0.00829
Haiku 4.5 $0.00004 $0.00414

Measured 1mo ago against content hash bf2f3e431ed0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-ml-data-science 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 1mo 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.

.claude/skills/ai-ml-data-science/SKILL.md · 367 lines

How it starts

The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Science Engineering Suite - Quick Reference

This skill turns raw data and questions into validated, documented models ready for production:

  • EDA workflows: Structured exploration with drift detection
  • Feature engineering: Reproducible feature pipelines with leakage prevention and train/serve parity
  • Model selection: Baselines first; strong tabular defaults; escalate complexity only when justified
  • Evaluation & reporting: Slice analysis, uncertainty, model cards, production metrics
  • SQL transformation: SQLMesh for staging/intermediate/marts layers
  • MLOps: CI/CD, CT (continuous training), CM (continuous monitoring)
  • Production patterns: Data contracts, lineage, feedback loops, streaming features

Modern emphasis (2026): Feature stores, automated retraining, drift monitoring (Evidently), train-serve parity, and agentic ML loops (plan -> execute -> evaluate -> improve). Tools: LightGBM, CatBoost, scikit-learn, PyTorch, Polars (lazy eval for larger-than-RAM datasets), lakeFS for data versioning.

Quick Reference

Task Tool/Framework Command When to Use
EDA & Profiling Pandas, Great Expectations df.describe(), ge.validate() Initial data exploration and quality checks
Feature Engineering Pandas, Polars, Feature Stores df.transform(), Feast materialization Creating lag, rolling, categorical features
Model Training Gradient boosting, linear models, scikit-learn lgb.train(), model.fit() Strong baselines for tabular ML
Hyperparameter Tuning Optuna, Ray Tune optuna.create_study(), tune.run() Optimizing model parameters
SQL Transformation SQLMesh sqlmesh plan, sqlmesh run Building staging/intermediate/marts layers
Experiment Tracking MLflow, W&B mlflow.log_metric(), wandb.log() Versioning experiments and models
Model Evaluation scikit-learn, custom metrics metrics.roc_auc_score(), slice analysis Validating model performance

Read the full file on GitHub · 367 lines

Files

What ships with it

26 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 1mo ago Changed · +7 tokens per session bf2f3e431ed0
  2. 1mo ago Changed · -1 lines be92019f0051
  3. 1mo ago First seen · 368 lines · 36 tokens per session scan A b9d59e34b950

Subscribe to this mod's changes

ai-ml-data-science is a skill published in the GitHub repository joaoguirunas/team-os (5 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 4,143 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.

Analytics and metricsClaude CodeData and AIagent-teamsai-agentsanthropicclaudeclaude-skills
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