Visual Skills
npc-live/clawfirm
A skill your agent uses whenever the user provides data (CSV, JSON, table, pasted numbers, or any structured dataset) and expects a visual output — even if they don't say 'chart' or 'visualize'.
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and…
Install Dataset Quality Audit
$ npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zebbern/claude-code-guide dataset-quality-audit --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/zebbern/claude-code-guide.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dataset-quality-audit .claude/skills/dataset-quality-audit && 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 "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .claude/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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/zebbern/claude-code-guide/tree/main/skills/dataset-quality-auditType 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 zebbern/claude-code-guide --skill dataset-quality-audit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zebbern/claude-code-guide dataset-quality-audit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dataset-quality-audit .agents/skills/dataset-quality-audit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .agents/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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 zebbern/claude-code-guide --skill dataset-quality-audit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zebbern/claude-code-guide dataset-quality-audit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dataset-quality-audit .cursor/skills/dataset-quality-audit && 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 "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .cursor/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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/zebbern/claude-code-guide.git --path skills/dataset-quality-audit--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 zebbern/claude-code-guide --skill dataset-quality-audit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zebbern/claude-code-guide dataset-quality-audit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dataset-quality-audit .gemini/skills/dataset-quality-audit && 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 "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .gemini/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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 zebbern/claude-code-guide dataset-quality-auditInstalls 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 zebbern/claude-code-guide --skill dataset-quality-audit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dataset-quality-audit .github/skills/dataset-quality-audit && 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 "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .github/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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 zebbern/claude-code-guide --skill dataset-quality-audit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zebbern/claude-code-guide dataset-quality-audit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dataset-quality-audit .opencode/skills/dataset-quality-audit && 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 "dataset-quality-audit" agent skill from https://github.com/zebbern/claude-code-guide/tree/main/skills/dataset-quality-audit into .opencode/skills/dataset-quality-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataset-quality-audit", 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
dataset-quality-audit- GitHub stars
- 4.7k
- Token cost
- ~996 tokens
- SKILL.md length
- 280 words
- Files
- 3 (incl. scripts)
- Skills in repo
- 46
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and…
- Tasks that involve Data cleaning
- SKILL.md covers Capabilities, Quick Start, Detailed Usage and Output Format (JSON), plus 2 more sections
- Runs Python scripts from its folder; calls python3 and pip
- Tasks that involve CSV and tabular files
What it does
Dataset Quality Audit is an agent skill from zebbern/claude-code-guide. Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.
Its SKILL.md is about 1000 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/data_quality_checker.py`).
It sits in Data & Analytics, covering Data cleaning, CSV and tabular files and Excel spreadsheets. It works with Microsoft Excel. The repository describes itself as: Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks from beginner to power user! The licence is MIT.
When your agent uses it
- Tasks that involve Data cleaning
- Tasks that involve CSV and tabular files
- Tasks that involve Excel spreadsheets
Example prompts
- “/dataset-quality-audit”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9cde898. 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:
python3pip
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
Dataset Quality Audit loads about 996 tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 280 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 zebbern/claude-code-guide at commit 9cde898, republished under its MIT licence (© zebbern). 280 words, ~996 tokens.
Download SKILL.mdSave it as
.claude/skills/dataset-quality-audit/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- dataset-quality-audit
- description
- Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.
- license
- MIT
dataset-quality-audit
A data quality auditing tool that runs 12-dimension quality checks on tabular data, producing per-dimension scores (0–100), an overall grade, and actionable fix suggestions.
Capabilities
| Dimension | Description |
|---|---|
| Missing Values | Count and percentage of null/NaN values per column |
| Duplicate Rows | Number and percentage of fully duplicated rows |
| Type Consistency | Mixed types within a single column (e.g., numbers mixed with text) |
| Value Range / Outliers | Outlier detection using the IQR method |
| Format Compliance | Consistency of date, email, phone number, and other formatted fields |
| Uniqueness Constraints | Whether ID-type columns contain duplicates |
| Whitespace Issues | Leading/trailing spaces, empty strings, whitespace-only values |
| Constant Columns | Columns with only a single unique value (zero information) |
| Distribution Skewness | Whether numeric columns have excessive skewness |
| Column Naming | Spaces, special characters, or inconsistent casing in column names |
| Cardinality Anomalies | Unusually high or low number of unique values |
| Cross-Column Consistency | Logical checks across columns (e.g., start date before end date) |
Quick Start
# Basic quality check
python3 scripts/data_quality_checker.py data.csv
# Save report as JSON
python3 scripts/data_quality_checker.py data.csv --output report.json
# Specify ID columns (for uniqueness checks)
python3 scripts/data_quality_checker.py users.csv --id-columns "user_id,email"
# Specify date columns (for format checks)
python3 scripts/data_quality_checker.py orders.csv --date-columns "created_at,updated_at"Detailed Usage
Basic Invocation
python3 scripts/data_quality_checker.py <data-file> [options]Parameters
| Parameter | Short | Required | Default | Description |
|---|---|---|---|---|
input | — | Yes | — | Path to input file (CSV/TSV/Excel/JSON) |
--output | -o | No | stdout | Path for the JSON report output |
--id-columns | -id | No | Auto-detect | Comma-separated column names that should be unique |
--date-columns | -dc | No | Auto-detect | Comma-separated column names containing dates |
--sample | -s | No | All rows | Number of rows to sample (useful for large files) |
--encoding | -e | No | utf-8 | File encoding |
Output Format (JSON)
{
"file": "data.csv",
"rows": 10000,
"columns": 15,
"overall_score": 78.5,
"grade": "B",
"dimensions": {
"missing_values": {
"score": 85.0,
"issues": [
{"column": "age", "missing_count": 150, "missing_pct": 1.5, "suggestion": "Fill with median or mode"}
]
},
"duplicates": {
"score": 95.0,
"issues": [...]
}
},
"top_suggestions": [
"Column 'age' has 1.5% missing values — consider filling with the median",
"Found 200 fully duplicated rows — consider deduplication"
]
}Grading Scale
| Grade | Score Range | Meaning |
|---|---|---|
| A+ | 95–100 | Excellent quality — ready for use as-is |
| A | 90–95 | Good quality — minor issues only |
| B | 80–90 | Moderate quality — recommended to fix before use |
| C | 60–80 | Poor quality — significant cleaning required |
| D | 40–60 | Very poor quality — many issues need attention |
| F | 0–40 | Essentially unusable — requires re-collection or major cleanup |
Dependencies
- Python 3.8+
- pandas
- numpy
pip install pandas numpy© zebbern, 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 2 other files (scripts) in skills/dataset-quality-audit of zebbern/claude-code-guide.
- SKILL.md
- LICENSE
- scripts/data_quality_checker.py
Open the folder on GitHubat commit 9cde898
Compare with similar skills
Dataset Quality Audit 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 |
|---|---|---|---|---|---|---|
| Dataset Quality Audit this skillzebbern/claude-code-guide | 4.7k | — | ~996 | Automated safety check: Pass | MIT | |
| Visual Skillsnpc-live/clawfirm | 156 | — | ~7.4k | Automated safety check: Pass | None | |
| XLSX Spreadsheet ToolkitXiaomiMiMo/MiMo-Code | 14k | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| CSV and Excel MergerOneWave-AI/claude-skills | 328 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Excel Spreadsheet Creation and Editinganthropics/skills | 180k | 4 repos | ~2.1k | Automated safety check: Pass | Proprietary | |
| Excel and CSV Data Analysisbytedance/deer-flow | 84k | 4 repos | ~2.2k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Dataset Quality Audit
What does Dataset Quality Audit do?
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and…. Dataset Quality Audit is an agent skill from zebbern/claude-code-guide. Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions.
When should I use Dataset Quality Audit?
Dataset Quality Audit fits situations like: tasks that involve Data cleaning; tasks that involve CSV and tabular files; tasks that involve Excel spreadsheets.
How do I install Dataset Quality Audit in Claude Code?
Run `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -a claude-code`. Or copy the skill folder (skills/dataset-quality-audit in zebbern/claude-code-guide) into .claude/skills/dataset-quality-audit in your project. Claude Code loads it when a task matches its description.
How do I install Dataset Quality Audit in Codex?
Run `npx skills add zebbern/claude-code-guide --skill dataset-quality-audit -a codex`. Or copy the skill folder (skills/dataset-quality-audit in zebbern/claude-code-guide) into .agents/skills/dataset-quality-audit in your project. Codex loads it when a task matches its description.
Can I use Dataset Quality Audit 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 zebbern/claude-code-guide --skill dataset-quality-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dataset-quality-audit, .gemini/skills/dataset-quality-audit, .github/skills/dataset-quality-audit and .opencode/skills/dataset-quality-audit in your project.
What does Dataset Quality Audit need to run?
Going by SKILL.md and its folder, Dataset Quality Audit needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.
Does Dataset Quality Audit 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 Dataset Quality Audit 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 Dataset Quality Audit use?
Dataset Quality Audit 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 Dataset Quality Audit use?
About 996 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Dataset Quality Audit?
Skills that share tags, products or a category with Dataset Quality Audit: Visual Skills (npc-live/clawfirm, 156 stars), XLSX Spreadsheet Toolkit (XiaomiMiMo/MiMo-Code, 14k stars), CSV and Excel Merger (OneWave-AI/claude-skills, 328 stars) and Excel Spreadsheet Creation and Editing (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dataset Quality Audit?
zebbern (a GitHub user) maintains it in zebbern/claude-code-guide, which has 4,652 GitHub stars. The repository holds 46 skills in this directory. The repository was last updated on October 9, 2026.
Source: zebbern/claude-code-guide on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.