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eda

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Aktualisiert5. Mai 2026 um 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.

Installation

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

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Quelle

argythana

argythana/python-ml-skills

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

DatenwissenschaftlerInformatik- und Mathematikberufe·SOC 15-2051

Datei-Explorer

3 Dateien

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

Use this skill when the user wants an iterative `code-review` loop over Python changes: review a Python diff, PR, branch, or commit range with `code-review`, fix the actionable Critical, High, and Moderate findings, run the relevant checks and tests, and repeat until 5 consecutive clean PASS reviews, `insufficient evidence`, or max cycles. Trigger on "run code-review until clean", "keep reviewing and fixing this Python PR until it passes", or "fix the code-review findings and rerun review until acceptable". If the target repo, diff, branch, commit, or commit range is referenced but unavailable, ask after routing. Do NOT use for a single review-only pass; use `code-review`. Do NOT use for pytest-suite-specific audit loops; use `pytest-suite-review-loop`. Do NOT use for implementing a task against Acceptance Criteria; use `task-implementation-loop`. Do NOT use for non-Python code, dependency-only changes, or writing new features from scratch.

2026-06-040

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

obsidian-create-note

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.).

2026-05-250

tutorial-expand

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`.

2026-05-250

lazy-imports

argythana/python-ml-skills

Use this skill when the user wants to lazily import Python modules, defer module loading until first use, or reduce Python or CLI startup cost by changing import behavior. Trigger on requests like "import X lazily", "don't load pandas until it's used", "use PEP 810", "use `wrapt.lazy_import()`", or "make this CLI's imports lazy". If the target code or supported Python version is missing and cannot be inferred from the repo, ask for it after routing. Do NOT use it just because a Python file has heavy dependencies, or for general import cleanup, generic startup tuning that is not about imports, or dependency-management requests where the main task is adding or updating packages; use `uv-deps` for that work.

2026-05-200

uv-deps

argythana/python-ml-skills

Use when the task is to manage dependencies in a `uv` project — either a single-package project (one `pyproject.toml`) or a `uv` workspace monorepo (multiple per-package `pyproject.toml` files plus a root `uv.lock`). Covers add, remove, update, sync, lock, build, export, or reorganize packages, dependency groups, extras, and workspace sources via `uv` commands rather than hand-editing `pyproject.toml`. Trigger for requests like 'add a package', 'uv sync', 'move this to dev dependencies', 'update uv deps', 'remove X from package Y', 'regenerate constraints.txt', or 'build a wheel'. Do NOT use for Poetry/pip/conda/pip-tools workflows, manual virtualenv setup or activation, import sorting, or unrelated `pyproject.toml` edits such as project metadata or build config.

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.