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Agent skill

Building Automl Pipelines

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning.

MITAuto-check passedData & Analytics

Install Building Automl Pipelines

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-automl-pipelines -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace building-automl-pipelines --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/building-automl-pipelines .claude/skills/building-automl-pipelines && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
building-automl-pipelines
GitHub stars
2.8k
Token cost
~702 tokens
SKILL.md length
251 words
Files
11 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning.

  • Works in 11 steps: Identify problem type… → Define evaluation metrics (accuracy, F1,… → Set time and resource budgets for AutoML… → …
  • Automating ML workflows from data preparation through model deployment
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Building Automl Pipelines is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".

Its SKILL.md is about 700 tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/pipeline_template.yaml` and `references/README.md`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning. It works with scikit-learn. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Automating ML workflows from data preparation through model deployment
  • With phrases like build automl pipeline
  • Automate ml workflow
  • Create automated training pipeline

Example prompts

  • “build automl pipeline”
  • “automate ml workflow”
  • “create automated training pipeline”
  • “/building-automl-pipelines”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(python:*)

Workflow steps

11 steps, taken from the first numbered list in SKILL.md.

  1. Identify problem type (binary/multi-class classification, regression, etc.)
  2. Define evaluation metrics (accuracy, F1, RMSE, etc.)
  3. Set time and resource budgets for AutoML search
  4. Specify feature types and preprocessing needs
  5. Determine model interpretability requirements
  6. Load training data using Read tool
  7. Perform initial data quality assessment
  8. Configure train/validation/test split strategy
  9. Define feature engineering transformations
  10. Set up data validation checks
  11. Initialize AutoML pipeline with configuration

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(python:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Building Automl Pipelines loads about 702 tokens when it runs, and up to ~1.6k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 251 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~702
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 251 words, ~702 tokens.

Download SKILL.mdSave it as .claude/skills/building-automl-pipelines/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
building-automl-pipelines
description
Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(python:*)
compatibility
Designed for Claude Code
version
1.25.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, deployment, ml

Building Automl Pipelines

Overview

Build an end-to-end AutoML pipeline: data checks, feature preprocessing, model search/tuning, evaluation, and exportable deployment artifacts. Use this when you want repeatable training runs with a clear budget (time/compute) and a structured output (configs, reports, and a runnable pipeline).

Prerequisites

Before using this skill, ensure you have:

  • Python environment with AutoML libraries (Auto-sklearn, TPOT, H2O AutoML, or PyCaret)
  • Training dataset in accessible format (CSV, Parquet, or database)
  • Understanding of problem type (classification, regression, time-series)
  • Sufficient computational resources for automated search
  • Knowledge of evaluation metrics appropriate for task
  • Target variable and feature columns clearly defined

Instructions

  1. Identify problem type (binary/multi-class classification, regression, etc.)
  2. Define evaluation metrics (accuracy, F1, RMSE, etc.)
  3. Set time and resource budgets for AutoML search
  4. Specify feature types and preprocessing needs
  5. Determine model interpretability requirements
  6. Load training data using Read tool
  7. Perform initial data quality assessment
  8. Configure train/validation/test split strategy
  9. Define feature engineering transformations
  10. Set up data validation checks
  11. Initialize AutoML pipeline with configuration

See ${CLAUDE_SKILL_DIR}/references/implementation.md for detailed implementation guide.

Output

  • Complete Python implementation of AutoML pipeline
  • Data loading and preprocessing functions
  • Feature engineering transformations
  • Model training and evaluation logic
  • Hyperparameter search configuration
  • Best model architecture and hyperparameters

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed examples.

Resources

  • Auto-sklearn: Automated scikit-learn pipeline construction with metalearning
  • TPOT: Genetic programming for pipeline optimization
  • H2O AutoML: Scalable AutoML with ensemble methods
  • PyCaret: Low-code ML library with automated workflows
  • Automated feature selection techniques

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references, assets) in skills/.curated/building-automl-pipelines of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/evaluation_report_template.html
  • assets/example_dataset.csv
  • assets/pipeline_template.yaml
  • references/README.md
  • references/errors.md
  • references/examples.md
  • references/implementation.md
  • scripts/README.md
  • scripts/pipeline_deployment.py

Open the folder on GitHubat commit cfae287

Compare with similar skills

Building Automl Pipelines next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Building Automl Pipelines compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Building Automl Pipelines this skilljeremylongshore/tons-of-skills-marketplace2.8k—~702Automated safety check: PassMIT
Scikit LearnzLanqing/codex-claude-academic-skills4.7k16 repos~3.9kAutomated safety check: PassBSD-3-Clause
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT
Time Series Analytics Useropen-edge-platform/edge-ai-libraries171—~3.1kAutomated safety check: PassApache-2.0
Estimate Online Covariancemicroprediction/precise337—~535Automated safety check: PassMIT
Precisemicroprediction/precise337—~782Automated safety check: PassMIT

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Works with

Categories

Questions about Building Automl Pipelines

What does Building Automl Pipelines do?

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Building Automl Pipelines is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning.

When should I use Building Automl Pipelines?

Building Automl Pipelines fits situations like: automating ML workflows from data preparation through model deployment; with phrases like build automl pipeline; automate ml workflow; create automated training pipeline.

How do I install Building Automl Pipelines in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-automl-pipelines -a claude-code`. Or copy the skill folder (skills/.curated/building-automl-pipelines in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/building-automl-pipelines in your project. Claude Code loads it when a task matches its description.

How do I install Building Automl Pipelines in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-automl-pipelines -a codex`. Or copy the skill folder (skills/.curated/building-automl-pipelines in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/building-automl-pipelines in your project. Codex loads it when a task matches its description.

Can I use Building Automl Pipelines in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-automl-pipelines -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-automl-pipelines, .gemini/skills/building-automl-pipelines, .github/skills/building-automl-pipelines and .opencode/skills/building-automl-pipelines in your project.

What does Building Automl Pipelines need to run?

Going by SKILL.md and its folder, Building Automl Pipelines needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(python:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Building Automl Pipelines access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Building Automl Pipelines safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Building Automl Pipelines use?

Building Automl Pipelines is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Building Automl Pipelines use?

About 702 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 895 tokens, read only when the agent opens those files.

What are the alternatives to Building Automl Pipelines?

Skills that share tags, products or a category with Building Automl Pipelines: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 171 stars) and Estimate Online Covariance (microprediction/precise, 337 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Building Automl Pipelines?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.