Question2report
refraction-ray/xalpha
Turn a natural-language financial question into a polished, self-contained HTML report.
Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and…
Install Data Cleaning Pass
$ npx skills add mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-cleaning-pass --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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-cleaning-pass .claude/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .claude/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-passType 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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-cleaning-pass --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-cleaning-pass .agents/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .agents/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-cleaning-pass --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-cleaning-pass .cursor/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .cursor/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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/mohitagw15856/pm-claude-skills.git --path skills/data-cleaning-pass--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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-cleaning-pass --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-cleaning-pass .gemini/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .gemini/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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 mohitagw15856/pm-claude-skills data-cleaning-passInstalls 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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-cleaning-pass .github/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .github/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-cleaning-pass --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-cleaning-pass .opencode/skills/data-cleaning-pass && 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-cleaning-pass" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-cleaning-pass into .opencode/skills/data-cleaning-pass/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-cleaning-pass", 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-cleaning-pass- GitHub stars
- 1.4k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 603 words
- Files
- 1
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and…
- Works in 5 steps: Profile before touching: per column —… → Fix in non-masking order: trim… → Dedupe with a definition: "duplicate"… → …
- Asked clean this export
- SKILL.md covers What This Skill Produces, Required Inputs, Framework: The Pass Rules and Output Format, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Cleaning Pass is an agent skill from mohitagw15856/pm-claude-skills. Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible. Use when asked clean this export, why is my pivot double-counting, these names don't match between sheets, or prep this data for analysis. Produces the profile of what's wrong, the ordered cleaning plan, the join-key repairs, and the cleaning log.
Its SKILL.md is about 1.2k 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 Data & Analytics, covering Data cleaning. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Asked clean this export
- Why is my pivot double-counting
- These names dont match between sheets
- Prep this data for analysis
Example prompts
- “/data-cleaning-pass”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Profile before touching: per column — count blanks, count types (text-that-looks-numeric is the classic), list distinct values on category…
- Fix in non-masking order: trim whitespace & normalize case → fix types (text-numbers to numbers, dates to real dates) → standardize…
- Dedupe with a definition: "duplicate" needs a key (same email? same name+date?) — stated before removing anything, with conflicting-field…
- Blanks are three different things: truly-empty (fine), should-have-a-value (flag for source follow-up, don't invent), and…
- The log is the deliverable's twin: each step: rule, scope, count affected ("trimmed 412 cells; merged 37 duplicate customers by email…
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. 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
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 Cleaning Pass loads about 1.2k tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 603 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 mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 603 words, ~1,234 tokens.
Download SKILL.mdSave it as
.claude/skills/data-cleaning-pass/SKILL.md (or your agent's skills folder).- name
- data-cleaning-pass
- description
- Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible. Use when asked clean this export, why is my pivot double-counting, these names don't match between sheets, or prep this data for analysis. Produces the profile of what's wrong, the ordered cleaning plan, the join-key repairs, and the cleaning log.
Data Cleaning Pass Skill
Dirty data doesn't announce itself — it double-counts in the pivot, drops rows in the join, and averages text as zero. Cleaning done ad hoc corrupts as it corrects (the dedupe that removed real records, the find-replace that hit the wrong column). The pass is methodical: profile first (what's actually wrong, counted), fix in an order where each step doesn't mask the next, keep the original untouched, and log every transformation — because "how did you get these numbers" deserves an answer.
What This Skill Produces
- The profile — per column: type consistency, blank/error counts, distinct-value sanity, the weirdest values surfaced
- The cleaning plan — ordered fixes with their methods, run on a copy
- The join-key repair — the match-rate before/after when sheets must link
- The cleaning log — what changed, how many rows/cells, by what rule — the defensibility artifact
Required Inputs
Ask for these if not provided:
- The data — the sheet/export, and where it came from (system exports have signature messes: leading zeros eaten, dates re-typed, thousands separators as text)
- The destination — a pivot, a join, a chart, an import; the destination defines "clean enough" (a join needs perfect keys; a chart needs consistent types)
- The authority questions — when duplicates conflict (two rows, same customer, different phone), which source wins? Cleaning makes merge decisions; someone must own the rule
Framework: The Pass Rules
- Profile before touching: per column — count blanks, count types (text-that-looks-numeric is the classic), list distinct values on category columns (finds
NY / N.Y. / New York), min/max on numerics (finds the 1899 dates and the 9999 placeholders). The profile converts "it's messy" into a numbered work list. - Fix in non-masking order: trim whitespace & normalize case → fix types (text-numbers to numbers, dates to real dates) → standardize categories (the NY problem) → then dedupe → then handle blanks. Deduping before normalization misses duplicates; deduping after catches them. Order is the craft.
- Dedupe with a definition: "duplicate" needs a key (same email? same name+date?) — stated before removing anything, with conflicting-field rules decided by the named authority ("keep most recent," "prefer CRM over export"). Removed rows go to a
_removedtab, not to oblivion. - Blanks are three different things: truly-empty (fine), should-have-a-value (flag for source follow-up, don't invent), and blank-meaning-zero (convert only when the source confirms the semantic). Filling blanks by assumption is fabrication with a keyboard.
- The log is the deliverable's twin: each step: rule, scope, count affected ("trimmed 412 cells; merged 37 duplicate customers by email, keep-most-recent; 9 unresolvable → flagged"). Original preserved untouched; the cleaned copy + log travel together.
Show full SKILL.md (183 more words)Show less
Output Format
Cleaning Pass: [dataset] → for [destination]
The Profile
| Column | Type issues | Blanks | Distinct/sanity findings |
|---|
The Plan (ordered)
[Trim/case → types → categories (the mapping table) → dedupe (key + conflict rule + authority) → blanks (three-way sort)]
Join Repair (if joining)
[Key match-rate before → after · the unmatched remainder, listed for follow-up]
The Log
[Step · rule · rows/cells affected — running, final counts at bottom · original untouched at (location)]
Quality Checks
- Profiling produced counts before any edit
- The order ran normalize-before-dedupe
- The duplicate key and conflict authority are stated
- No blank was filled without a confirmed semantic
- The log accounts for every transformation, and the original survives untouched
Anti-Patterns
- Do not clean in place — the original is the rollback and the audit
- Do not dedupe first — un-normalized duplicates hide from the dedupe
- Do not invent values for should-have-value blanks — flag them; fabrication compounds downstream
- Do not global find-replace without column scoping — the classic self-inflicted corruption
- Do not deliver cleaned data without the log — numbers whose provenance can't be stated get re-cleaned by the next skeptic
Example Trigger Phrases
- "Clean this export."
- "Why is my pivot double-counting?"
- "Prep this data for analysis."
© mohitagw15856, MIT. 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 skills/data-cleaning-pass of mohitagw15856/pm-claude-skills.
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Data Cleaning Pass 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 Cleaning Pass this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Question2reportrefraction-ray/xalpha | 2.7k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Dingo VerifyMigoXLab/dingo | 757 | — | ~741 | Automated safety check: Notes | Apache-2.0 | |
| Data Validationplatonai/Browser4 | 1.2k | — | ~896 | Automated safety check: Pass | Apache-2.0 | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Issues DeduplicationJetBrains/ideavim | 10k | — | ~1.3k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Data Cleaning Pass
What does Data Cleaning Pass do?
Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and…. Data Cleaning Pass is an agent skill from mohitagw15856/pm-claude-skills. Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible.
When should I use Data Cleaning Pass?
Data Cleaning Pass fits situations like: asked clean this export; why is my pivot double-counting; these names dont match between sheets; prep this data for analysis.
How do I install Data Cleaning Pass in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a claude-code`. Or copy the skill folder (skills/data-cleaning-pass in mohitagw15856/pm-claude-skills) into .claude/skills/data-cleaning-pass in your project. Claude Code loads it when a task matches its description.
How do I install Data Cleaning Pass in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-cleaning-pass -a codex`. Or copy the skill folder (skills/data-cleaning-pass in mohitagw15856/pm-claude-skills) into .agents/skills/data-cleaning-pass in your project. Codex loads it when a task matches its description.
Can I use Data Cleaning Pass 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 mohitagw15856/pm-claude-skills --skill data-cleaning-pass -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-cleaning-pass, .gemini/skills/data-cleaning-pass, .github/skills/data-cleaning-pass and .opencode/skills/data-cleaning-pass in your project.
What does Data Cleaning Pass need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Cleaning Pass is instructions for the agent only.
Does Data Cleaning Pass 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 Cleaning Pass 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 Cleaning Pass use?
Data Cleaning Pass 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 Data Cleaning Pass use?
About 1.2k tokens (SKILL.md is roughly 4.9k 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 Cleaning Pass?
Skills that share tags, products or a category with Data Cleaning Pass: Question2report (refraction-ray/xalpha, 2.7k stars), Dingo Verify (MigoXLab/dingo, 757 stars), Data Validation (platonai/Browser4, 1.2k stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Cleaning Pass?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,433 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 8, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.