Data Table Manager
n8n-io/n8n
Load before calling data-tables or parse-file. An agent skill from n8n-io/n8n.
Clean up and analyze CSV/Excel data for non-analysts. An agent skill from prapaa-ai/agav.
Install Data Clean
$ npx skills add prapaa-ai/agav --skill data-clean -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install prapaa-ai/agav data-clean --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/prapaa-ai/agav.git skills-src && mkdir -p .claude/skills && cp -r skills-src/source/skills/bundled/data-clean .claude/skills/data-clean && 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 "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .claude/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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/prapaa-ai/agav/tree/main/source/skills/bundled/data-cleanType 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 prapaa-ai/agav --skill data-clean -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install prapaa-ai/agav data-clean --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prapaa-ai/agav.git skills-src && mkdir -p .agents/skills && cp -r skills-src/source/skills/bundled/data-clean .agents/skills/data-clean && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .agents/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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 prapaa-ai/agav --skill data-clean -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install prapaa-ai/agav data-clean --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prapaa-ai/agav.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/source/skills/bundled/data-clean .cursor/skills/data-clean && 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 "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .cursor/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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/prapaa-ai/agav.git --path source/skills/bundled/data-clean--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 prapaa-ai/agav --skill data-clean -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install prapaa-ai/agav data-clean --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prapaa-ai/agav.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/source/skills/bundled/data-clean .gemini/skills/data-clean && 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 "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .gemini/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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 prapaa-ai/agav data-cleanInstalls 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 prapaa-ai/agav --skill data-clean -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/prapaa-ai/agav.git skills-src && mkdir -p .github/skills && cp -r skills-src/source/skills/bundled/data-clean .github/skills/data-clean && 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 "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .github/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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 prapaa-ai/agav --skill data-clean -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install prapaa-ai/agav data-clean --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prapaa-ai/agav.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/source/skills/bundled/data-clean .opencode/skills/data-clean && 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 "data-clean" agent skill from https://github.com/prapaa-ai/agav/tree/main/source/skills/bundled/data-clean into .opencode/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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
data-clean- GitHub stars
- 147
- Token cost
- ~479 tokens
- SKILL.md length
- 242 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Clean up and analyze CSV/Excel data for non-analysts. An agent skill from prapaa-ai/agav.
- Works in 7 steps: Read the file first (head and tail,… → Detect and report data problems → Propose the cleanup plan and get… → …
- Tasks that involve Excel spreadsheets
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Tasks that involve CSV and tabular files
What it does
Data Clean is an agent skill from prapaa-ai/agav. Clean up and analyze CSV/Excel data for non-analysts
Its SKILL.md is about 480 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Documents & Office, covering Excel spreadsheets and CSV and tabular files. It works with Microsoft Excel. The repository describes itself as: Terminal-native AI coding assistant for real repositories. Inspect code, edit files, run tests, and verify changes from the CLI. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Excel spreadsheets
- Tasks that involve CSV and tabular files
Example prompts
- “/data-clean”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): read_file, write_file, run_command, find_files, list_directory
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Read the file first (head and tail, row/column counts) and describe what you found before changing anything: column names, apparent types…
- Detect and report data problems
- Propose the cleanup plan and get approval: one bullet per transformation, in the order applied. Typical fixes — parse types, normalize…
- Apply transformations with Python (csv module or openpyxl/pandas if available). Work on a copy: write the cleaned data to a new file…
- After cleaning, produce a before/after summary: rows in/out, values changed per fix, columns added or renamed.
- For questions ("which month had the highest sales?"), answer in one plain sentence, then show the small supporting table or aggregate…
- Flag anything the data cannot answer honestly. Correlation is not causation; say so when the user asks "why".
What it can do on your machine
Read from SKILL.md and the folder at commit 9e9582a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
read_filewrite_filerun_commandfind_fileslist_directory
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
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
Data Clean loads about 479 tokens when it runs. Until then it costs about 16 tokens; SKILL.md has 242 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from prapaa-ai/agav at commit 9e9582a, republished under its Apache-2.0 licence (© prapaa-ai). 242 words, ~479 tokens.
Download SKILL.mdSave it as
.claude/skills/data-clean/SKILL.md (or your agent's skills folder).- name
- data-clean
- description
- Clean up and analyze CSV/Excel data for non-analysts
- allowed-tools
- read_file, write_file, run_command, find_files, list_directory
- version
- 1.0.0
- invocation
- both
- tags
- data, spreadsheets
Data Clean
Clean up messy CSV/Excel data and answer simple questions about it, explained in plain language.
Instructions
- Read the file first (head and tail, row/column counts) and describe what you found before changing anything: column names, apparent types, row count, and obvious problems.
- Detect and report data problems:
- Types stored as text: numbers with currency symbols or thousands separators, inconsistent date formats.
- Inconsistent categories: "USA" vs "United States", trailing whitespace, case mismatches.
- Missing values: count them per column and show where they cluster.
- Duplicates: exact-duplicate rows and suspicious near-duplicates.
- Structural issues: merged cells, multi-row headers, totals embedded in the data.
- Propose the cleanup plan and get approval: one bullet per transformation, in the order applied. Typical fixes — parse types, normalize categories to one spelling, trim whitespace, ISO-format dates, mark (not drop) missing values, flag duplicates.
- Apply transformations with Python (csv module or openpyxl/pandas if available). Work on a copy: write the cleaned data to a new file (
-cleanedsuffix), never over the original. - After cleaning, produce a before/after summary: rows in/out, values changed per fix, columns added or renamed.
- For questions ("which month had the highest sales?"), answer in one plain sentence, then show the small supporting table or aggregate behind it. State the denominator and any filtering you applied ("excluding 12 rows with no date").
- Flag anything the data cannot answer honestly. Correlation is not causation; say so when the user asks "why".
© prapaa-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in source/skills/bundled/data-clean of prapaa-ai/agav.
Open the folder on GitHubat commit 9e9582a
Compare with similar skills
Data Clean 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 |
|---|---|---|---|---|---|---|
| Data Clean this skillprapaa-ai/agav | 147 | — | ~479 | Automated safety check: Pass | Apache-2.0 | |
| Data Table Managern8n-io/n8n | 207k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 2 repos | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Markitshift-labs-ai/markit | 1.3k | — | ~299 | Automated safety check: Pass | MIT | |
| Convert Fileduckdb/duckdb-skills | 600 | 1 repos | ~720 | Automated safety check: Notes | MIT | |
| Research Integrity Auditxuzhougeng/wisp-science | 1k | — | ~2.6k | Automated safety check: Pass | AGPL-3.0 |
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Questions about Data Clean
What does Data Clean do?
Clean up and analyze CSV/Excel data for non-analysts. An agent skill from prapaa-ai/agav. Data Clean is an agent skill from prapaa-ai/agav.
When should I use Data Clean?
Data Clean fits situations like: tasks that involve Excel spreadsheets; tasks that involve CSV and tabular files.
How do I install Data Clean in Claude Code?
Run `npx skills add prapaa-ai/agav --skill data-clean -a claude-code`. Or copy the skill folder (source/skills/bundled/data-clean in prapaa-ai/agav) into .claude/skills/data-clean in your project. Claude Code loads it when a task matches its description.
How do I install Data Clean in Codex?
Run `npx skills add prapaa-ai/agav --skill data-clean -a codex`. Or copy the skill folder (source/skills/bundled/data-clean in prapaa-ai/agav) into .agents/skills/data-clean in your project. Codex loads it when a task matches its description.
Can I use Data Clean 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 prapaa-ai/agav --skill data-clean -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-clean, .gemini/skills/data-clean, .github/skills/data-clean and .opencode/skills/data-clean in your project.
What does Data Clean need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Clean is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: read_file, write_file, run_command, find_files, list_directory.
Does Data Clean 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 Data Clean 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. Review the folder before installing.
What licence does Data Clean use?
Data Clean is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data Clean use?
About 479 tokens (SKILL.md is roughly 1.9k 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 Data Clean?
Skills that share tags, products or a category with Data Clean: Data Table Manager (n8n-io/n8n, 207k stars), Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Markit (shift-labs-ai/markit, 1.3k stars) and Convert File (duckdb/duckdb-skills, 600 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Clean?
prapaa-ai (a GitHub organization) maintains it in prapaa-ai/agav, which has 147 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 8, 2026.
Source: prapaa-ai/agav on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.