Pandas Pro
Jeffallan/claude-skills
Handles pandas DataFrame work: cleaning, merging, groupby aggregation, pivots, time-series resampling and memory tuning, with checks on dtypes, shapes and nulls.
Systematic exploratory data analysis. An agent skill from nimrodfisher/data-analytics-skills.
Install Programmatic Eda
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-eda --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .claude/skills/programmatic-eda && rm -rf skills-srcUse ~/.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/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .claude/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-edaType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-eda --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .agents/skills/programmatic-eda && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .agents/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-eda --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .cursor/skills/programmatic-eda && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .cursor/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/nimrodfisher/data-analytics-skills.git --path 01-data-quality-validation/programmatic-eda--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-eda --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .gemini/skills/programmatic-eda && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .gemini/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-edaInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .github/skills/programmatic-eda && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .github/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nimrodfisher/data-analytics-skills programmatic-eda --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/01-data-quality-validation/programmatic-eda .opencode/skills/programmatic-eda && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "programmatic-eda" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda into .opencode/skills/programmatic-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "programmatic-eda", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
programmatic-eda- GitHub stars
- 465
- Token cost
- ~582 tokens
- SKILL.md length
- 258 words
- Files
- 11 (incl. scripts, references, assets)
- Skills in repo
- 31
- Repo updated
- First seen
- Licence
- MIT
At a glance
Systematic exploratory data analysis. An agent skill from nimrodfisher/data-analytics-skills.
- Works in 7 steps: Load and overview — run… → Null profile — run… → Outlier detection — run… → …
- Tasks that involve Data analysis
- Runs Python scripts from its folder
- Tasks that involve Data cleaning
What it does
Programmatic Eda is an agent skill from nimrodfisher/data-analytics-skills. Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.
Its SKILL.md is about 580 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/eda_report_template.md`, `assets/findings_summary.md` and `references/eda_checklist.md`).
It sits in Data & Analytics, covering Data analysis, Data cleaning and DataFrames. The repository describes itself as: A comprehensive list of Claude & Codex skills for a wide range of data analytics tasks. The licence is MIT.
When your agent uses it
- Tasks that involve Data analysis
- Tasks that involve Data cleaning
- Tasks that involve DataFrames
Example prompts
- “/programmatic-eda”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Load and overview — run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row…
- Null profile — run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above…
- Outlier detection — run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or…
- Distribution summary — run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.
- Correlation exploration — run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
- EDA checklist sign-off — work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.
- Write findings — fill assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 9449d36. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 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.
Context cost
Programmatic Eda loads about 582 tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 48 tokens; SKILL.md has 258 words of instructions outside code blocks.
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 nimrodfisher/data-analytics-skills at commit 9449d36, republished under its MIT licence (© nimrodfisher). 258 words, ~582 tokens.
Download SKILL.mdSave it as
.claude/skills/programmatic-eda/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.- name
- programmatic-eda
- description
- Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.
When to use
- You receive a new dataset and need to understand its shape and quality before analysis
- An analysis produces surprising numbers and you want to verify the underlying data first
- A stakeholder asks "is this data reliable?" or "what's in this table?"
- You're about to run a model or statistical test and need data-quality assurance
Process
- Load and overview — run
scripts/data_overview.pyto get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents). - Null profile — run
scripts/null_profiler.py; compare output against thresholds inreferences/quality_thresholds.mdand flag columns above limits. - Outlier detection — run
scripts/outlier_detector.py(IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error? - Distribution summary — run
scripts/distribution_summary.pyfor descriptive stats and univariate histograms on each numeric column. - Correlation exploration — run
scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy. - EDA checklist sign-off — work through
references/eda_checklist.mdand confirm each item before declaring the dataset profiled. - Write findings — fill
assets/eda_report_template.mdwith full profiling output; distil top issues intoassets/findings_summary.md.
For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.
Inputs the skill needs
- Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
- Required: business context — what does one row represent?
- Optional: quality threshold overrides (defaults in
references/quality_thresholds.md) - Optional: columns to skip (PII, binary blobs, high-cardinality IDs)
Output
assets/eda_report_template.md(filled) — full profiling report with per-column statsassets/findings_summary.md(filled) — top 3–5 quality issues and recommended next steps- Console output / plots from scripts for interactive inspection
© nimrodfisher, 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 01-data-quality-validation/programmatic-eda of nimrodfisher/data-analytics-skills.
- SKILL.md
- assets/eda_report_template.md
- assets/findings_summary.md
- references/eda_checklist.md
- references/pandas_polars_recipes.md
- references/quality_thresholds.md
- scripts/correlation_explorer.py
- scripts/data_overview.py
- scripts/distribution_summary.py
- scripts/null_profiler.py
- scripts/outlier_detector.py
Open the folder on GitHubat commit 9449d36
Compare with similar skills
Programmatic Eda 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Programmatic Eda this skillnimrodfisher/data-analytics-skills | 465 | — | ~582 | Automated safety check: Pass | MIT | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Verified Data Analysis with pandaspipeshub-ai/pipeshub-ai | 3.8k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Data Table AnalysisNVIDIA-AI-Blueprints/deep-researcher-agent | 883 | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Investigate Datawalkthru-earth/geocoding-playground | 153 | — | ~935 | Automated safety check: Pass | CC-BY-4.0 |
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Related
Categories
Questions about Programmatic Eda
What does Programmatic Eda do?
Systematic exploratory data analysis. An agent skill from nimrodfisher/data-analytics-skills. Programmatic Eda is an agent skill from nimrodfisher/data-analytics-skills. Systematic exploratory data analysis.
When should I use Programmatic Eda?
Programmatic Eda fits situations like: tasks that involve Data analysis; tasks that involve Data cleaning; tasks that involve DataFrames.
How do I install Programmatic Eda in Claude Code?
Run `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a claude-code`. Or copy the skill folder (01-data-quality-validation/programmatic-eda in nimrodfisher/data-analytics-skills) into .claude/skills/programmatic-eda in your project. Claude Code loads it when a task matches its description.
How do I install Programmatic Eda in Codex?
Run `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -a codex`. Or copy the skill folder (01-data-quality-validation/programmatic-eda in nimrodfisher/data-analytics-skills) into .agents/skills/programmatic-eda in your project. Codex loads it when a task matches its description.
Can I use Programmatic Eda 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 nimrodfisher/data-analytics-skills --skill programmatic-eda -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/programmatic-eda, .gemini/skills/programmatic-eda, .github/skills/programmatic-eda and .opencode/skills/programmatic-eda in your project.
What does Programmatic Eda need to run?
Going by SKILL.md and its folder, Programmatic Eda needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Programmatic Eda 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 Programmatic Eda 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 Programmatic Eda use?
Programmatic Eda is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Programmatic Eda use?
About 582 tokens (SKILL.md is roughly 2.3k 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 2.3k tokens, read only when the agent opens those files.
What are the alternatives to Programmatic Eda?
Skills that share tags, products or a category with Programmatic Eda: Pandas Pro (Jeffallan/claude-skills, 12k stars), Verified Data Analysis with pandas (pipeshub-ai/pipeshub-ai, 3.8k stars), Data Table Analysis (NVIDIA-AI-Blueprints/deep-researcher-agent, 883 stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Programmatic Eda?
nimrodfisher (a GitHub user) maintains it in nimrodfisher/data-analytics-skills, which has 465 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on September 25, 2026.
Source: nimrodfisher/data-analytics-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.