Agent skill

Q-EDA Exploratory Analysis

Runs exploratory data analysis on tabular data after you confirm each column's measurement level, then writes CSV tables and a narrative summary.

MITAuto-check passedData & Analytics

Install Q-EDA Exploratory Analysis

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a claude-code

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

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda --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/TyrealQ/q-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/q-scholar/q-eda .claude/skills/q-eda && 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/

Paste into Claude Code

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .claude/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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

$skill-installer install https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda

Type 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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/q-scholar/q-eda .agents/skills/q-eda && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .agents/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/q-scholar/q-eda .cursor/skills/q-eda && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .cursor/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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

$ gemini skills install https://github.com/TyrealQ/q-skills.git --path skills/q-scholar/q-eda

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/q-scholar/q-eda .gemini/skills/q-eda && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .gemini/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda

Installs 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).

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/q-scholar/q-eda .github/skills/q-eda && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .github/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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.

skills CLI

$ npx skills add TyrealQ/q-skills --skill q-eda -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TyrealQ/q-skills q-eda --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TyrealQ/q-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/q-scholar/q-eda .opencode/skills/q-eda && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "q-eda" agent skill from https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-eda into .opencode/skills/q-eda/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "q-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: q-eda
GitHub stars: 108
Token cost: ~1.1k tokens
SKILL.md length: 407 words
Files: 6 (incl. scripts, references)
Skills in repo: 16
Repo updated: Sep 23, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Runs exploratory data analysis on tabular data after you confirm each column's measurement level, then writes CSV tables and a narrative summary.

Works in 3 steps: Determine this SKILL.md file's directory… → Script path =… → Reference path = ${SKILL_DIR}/references/.
Running a first descriptive pass over a survey or experiment dataset
SKILL.md covers Script Directory, Dependencies, References and Core Principles, plus 2 more sections
Runs Python scripts from its folder; calls pip

What it does

Before any statistics run, the skill interviews you in two stages about the dataset and proposes a measurement level for each column. Nothing is analyzed until you confirm the classification, and corrections lead to a full updated table for approval. It then executes the pre-built scripts/run_eda.py rather than writing a new script.

Methods follow the confirmed level: median and IQR for ordinal data, Pearson correlations for continuous variables, Spearman for ordinal pairs, and cross-tabulations for nominal ones. Results are exploratory only, with no confirmatory tests, and include mean, median, SD, SE, 95% confidence intervals, skewness and kurtosis in an APA-compatible form.

Output comes in two forms: structured CSV files for validation and import, and an EXPLORATORY_SUMMARY.md that flags patterns and warnings, written from the generated CSVs using the summary template and instructions in references/. If the agent is in plan mode it only writes a brief plan and exits before running anything.

When your agent uses it

Running a first descriptive pass over a survey or experiment dataset
Preparing descriptive statistics tables for a report or manuscript
Checking distributions and relationships before choosing a formal test

Example prompts

“Run EDA on data/survey_responses.csv and help me classify the columns.”
“Give me descriptive statistics for my study dataset in a form I can cite in APA style.”
“Explore this spreadsheet before I decide which tests to run.”

Requirements

Python with pandas, numpy, scipy and openpyxl
A tabular dataset file

Workflow steps

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

Determine this SKILL.md file's directory path as SKILL_DIR.
Script path = ${SKILL_DIR}/scripts/run_eda.py.
Reference path = ${SKILL_DIR}/references/.

What it can do on your machine

Read from SKILL.md and the folder at commit d8aaee7. 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 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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

Q-EDA Exploratory Analysis loads about 1.1k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 407 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~52
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~6.3k

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 TyrealQ/q-skills at commit d8aaee7, republished under its MIT licence (© TyrealQ). 407 words, ~1,054 tokens.

Download SKILL.mdSave it as .claude/skills/q-eda/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: q-eda
description: Run exploratory data analysis on tabular datasets with measurement-appropriate statistics. Use for EDA, descriptive statistics, data exploration, or preparing data summaries for reports and manuscripts.

Q-EDA

Universal exploratory data analysis for tabular datasets. Interviews the user to confirm column measurement levels, runs statistically appropriate analysis per variable type, and produces structured CSVs with a narrative summary.

IMPORTANT: This skill requires Bash execution. Use the pre-built scripts/run_eda.py from ${SKILL_DIR}/scripts/ — do NOT write a new script or inline Python.

If in plan mode: write a brief plan — "Run q-eda skill: interview user for context and column types, execute run_eda.py, write EXPLORATORY_SUMMARY.md from generated CSVs." — then exit plan mode immediately. Do NOT attempt interview stages, script execution, or any analysis while plan mode is active.

Script Directory

Agent execution instructions:

Determine this SKILL.md file's directory path as SKILL_DIR.
Script path = ${SKILL_DIR}/scripts/run_eda.py.
Reference path = ${SKILL_DIR}/references/<ref-name>.

Dependencies

pandas
numpy
scipy
openpyxl   # required for .xlsx input and Phase 6 Excel report

Install: pip install pandas numpy scipy openpyxl

References

references/interview_protocol.md — two-stage interview, column classification table, and detection rules
references/invocation_guide.md — script arguments, examples, and behavioral defaults
references/summary_instructions.md — post-script summary instructions, table formatting rules, flagging thresholds
references/summary_template.md — structural blueprint for EXPLORATORY_SUMMARY.md
../references/apa_style_guide.md — APA formatting, numbers, notation

Core Principles

Exploratory-first: no confirmatory statistics; build the picture before hypothesis testing
User-confirmed classification: suggest measurement levels; user confirms before analysis runs
Whole-artifact re-presentation on revision: when the user requests corrections, re-present the complete updated classification table alongside the confirmed Stage A answers as a single consolidated proof, and obtain explicit final approval before invoking run_eda.py
Measurement-appropriate methods: median/IQR for ordinal, Pearson for continuous, Spearman for ordinal, cross-tabs for nominal
Insight-flagging: report patterns and warnings, not just numbers
Dual output: CSVs for validation and import; markdown for interpretation
APA-compatible statistics: full metric set (M, Mdn, SD, SE, 95% CI, skewness, kurtosis) ready for reporting

Show full SKILL.md (140 more words)Show less

Workflow

Step	Action	Reference
1	Interview: context questions, then column classification with user confirmation	references/interview_protocol.md
2	Execute: run `run_eda.py` with confirmed types (see Pipeline below)	references/invocation_guide.md
3	Summarize: write `tables-eda/EXPLORATORY_SUMMARY.md` from generated CSVs	references/summary_template.md, references/summary_instructions.md

Pipeline (Step 2 output)

Phase	Output	Content
0	(console)	Data loading, column classification, schema summary
1	`01_dataset_profile.csv`	Shape, column types, missing%, uniqueness
2	`02_data_quality.csv`	Missing counts/%, duplicates, constant columns, outliers (IQR)
3	`03`-`08_*.csv`	Univariate: nominal frequencies, binary summary, ordinal/discrete/continuous descriptives
4	`09`-`12_*.csv`	Bivariate: Pearson/Spearman correlations, grouped descriptives, cross-tabs
5	`13`-`14_*.csv`	Specialized: text analysis, temporal trends
6	`EXPLORATORY_REPORT.xlsx`	APA-7th formatted workbook (B&W, one sheet per CSV)

Files are omitted when no columns of that type exist. Output directory: tables-eda/.

Scope

Include: Any .xlsx/.csv dataset — academic, business, or general. Outputs feed directly into q-methods and q-results.

Exclude: Confirmatory statistics, visualization, hypothesis testing, data cleaning beyond script internals.

Files

SKILL.md and 5 other files (scripts, references) in skills/q-scholar/q-eda of TyrealQ/q-skills.

SKILL.md
references/interview_protocol.md
references/invocation_guide.md
references/summary_instructions.md
references/summary_template.md
scripts/run_eda.py

Open the folder on GitHubat commit d8aaee7

Compare with similar skills

Q-EDA Exploratory Analysis 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.

Q-EDA Exploratory Analysis compared with similar skills
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PyMC Bayesian Modelingdavila7/claude-code-templates	32k	11 repos	~3.9k	Automated safety check: Pass	MIT	today
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Data AnalysisEXboys/skilllite	170	—	~176	Automated safety check: Pass	MIT	14 days ago
Tooluniverse Epigenomicswu-yc/LabClaw	1.1k	2 repos	~14k	Automated safety check: Pass	None	6 mo ago

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Works with

Questions about Q-EDA Exploratory Analysis

What does Q-EDA Exploratory Analysis do?

Runs exploratory data analysis on tabular data after you confirm each column's measurement level, then writes CSV tables and a narrative summary. Before any statistics run, the skill interviews you in two stages about the dataset and proposes a measurement level for each column. Nothing is analyzed until you confirm the classification, and corrections lead to a full updated table for approval.

When should I use Q-EDA Exploratory Analysis?

Q-EDA Exploratory Analysis fits situations like: running a first descriptive pass over a survey or experiment dataset; preparing descriptive statistics tables for a report or manuscript; checking distributions and relationships before choosing a formal test.

How do I install Q-EDA Exploratory Analysis in Claude Code?

Run `npx skills add TyrealQ/q-skills --skill q-eda -a claude-code`. Or copy the skill folder (skills/q-scholar/q-eda in TyrealQ/q-skills) into .claude/skills/q-eda in your project. Claude Code loads it when a task matches its description.

How do I install Q-EDA Exploratory Analysis in Codex?

Run `npx skills add TyrealQ/q-skills --skill q-eda -a codex`. Or copy the skill folder (skills/q-scholar/q-eda in TyrealQ/q-skills) into .agents/skills/q-eda in your project. Codex loads it when a task matches its description.

Can I use Q-EDA Exploratory Analysis 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 TyrealQ/q-skills --skill q-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/q-eda, .gemini/skills/q-eda, .github/skills/q-eda and .opencode/skills/q-eda in your project.

What does Q-EDA Exploratory Analysis need to run?

Going by SKILL.md and its folder, Q-EDA Exploratory Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python with pandas, numpy, scipy and openpyxl; A tabular dataset file.

Does Q-EDA Exploratory Analysis access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Q-EDA Exploratory Analysis 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 Q-EDA Exploratory Analysis use?

Q-EDA Exploratory Analysis 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 Q-EDA Exploratory Analysis use?

About 1.1k tokens (SKILL.md is roughly 4.2k 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 5.3k tokens, read only when the agent opens those files.

What are the alternatives to Q-EDA Exploratory Analysis?

Skills that share tags, products or a category with Q-EDA Exploratory Analysis: Statistical Data Analysis (lingzhi227/agent-research-skills, 386 stars), PyMC Bayesian Modeling (davila7/claude-code-templates, 32k stars), Python Executor (cortega26/chile-hub, 113 stars) and Data Analysis (EXboys/skilllite, 170 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Q-EDA Exploratory Analysis?

TyrealQ (a GitHub user) maintains it in TyrealQ/q-skills, which has 108 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on September 23, 2026.

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

Install Q-EDA Exploratory Analysis

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Q-EDA

Script Directory

Dependencies

References

Core Principles

Workflow

Pipeline (Step 2 output)

Scope

Files

Compare with similar skills

Similar skills

More from TyrealQ/q-skills

Related

Questions about Q-EDA Exploratory Analysis