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Ejecuta cualquier Skill en Manus con un clic

eda

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Actualizado5 de mayo de 2026 a las 11:46

Use this skill when the user wants exploratory analysis of one tabular dataset (Parquet, CSV, JSON, JSONL) — column distributions, data quality, class balance, missing patterns, high-cardinality detection, or summary statistics. Trigger on prompts like "analyze this column", "check the distribution", "look for missing values", "is this target imbalanced", or "run EDA on this file". If the dataset path or column is missing, ask for it after routing. Do NOT use it just to verify a file is readable, list its schema, or count rows; use `dataset-inspector` first for basic access checks. Do NOT use it for model training, feature engineering, non-tabular data (images, text, audio), or remote warehouse exploration.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

argythana

argythana/python-ml-skills

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name

eda

description

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.

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code-review-loop

argythana/python-ml-skills

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code-review

argythana/python-ml-skills

Use this skill when the user wants a production-grade review of Python code, a Python pull request, or a Python-focused diff. Check architecture, security, code quality, tests, documentation, deployment risk, and code-documentation consistency. Trigger on requests like "review this Python PR", "audit this diff", "check code quality", or "give me a fix plan". Do NOT use it for non-Python code review, writing new features, dependency-only changes, pytest-suite-specific audits where `pytest-suite-review` is a better fit, or iterative review-fix-rerun loops where `code-review-loop` is a better fit.

2026-06-040

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argythana/python-ml-skills

Use when the user wants to import a local Markdown file as a new note into a personal Obsidian vault — prompts like "import this .md into Obsidian", "add this Markdown doc to my vault", or "create a note from this file". The vault root is discovered at runtime: `OBSIDIAN_VAULT` env var if set, otherwise the single directory under `$HOME/Documents` (depth 3) containing `.obsidian/`. If the source Markdown path is missing, ask for it. Do NOT use for: editing, renaming, moving, or deleting existing notes; vault-wide search; daily notes (`daily/`); Literature notes (`Literature/`); ephemeral scratch (`ephemeral/`); Excalidraw drawings; fetching from a URL (use `url-reader` first); or authoring note content from scratch. Do NOT run `git` — the `obsidian-git` plugin owns commits. Assumes the layout in `references/vault-conventions.md` (`all_notes/`, `tags/`, etc.).

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argythana/python-ml-skills

Use when expanding, annotating, rewriting, or drafting a section or whole notebook of an official tutorial for a Python data/ML library (e.g. scikit-learn, PyTorch, pandas, matplotlib, NumPy, SciPy, Plotly) so it is friendlier for beginner data scientists and CS students. Trigger on prompts like "expand this scikit-learn section", "explain this pandas concept better", "make this PyTorch notebook beginner-friendly", or "why does this feature exist". If the target notebook or cell is unclear, ask after routing. Do NOT use for general Python tutorial writing unrelated to data/ML libraries (Django, Flask, application code), editing the upstream library itself, non-tutorial docs (`README.md`, `CLAUDE.md`), or non-teaching notebook changes (file moves, dependency bumps, formatting, kernel metadata). If a library-specific tutorial-expand skill is installed (e.g. `mlflow-tutorial-expand`), prefer that. For deps use `uv-deps`; for staging use `git-staging`; for messages use `commit-message`.

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argythana/python-ml-skills

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2026-05-200

uv-deps

argythana/python-ml-skills

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2026-05-200

name

eda

description

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.