Clean messy data in the active sheet or a specified range.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionclean-data-xlsExecute the skills CLI command in your project's root directory to begin installation:
Fetches clean-data-xls from anthropics/financial-services-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate clean-data-xls. Access via /clean-data-xls in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Clean messy data in the active sheet or a specified range.
Excel.run(async (context) => {...})). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies.A1:F200), use it| Issue | What to look for |
|---|---|
| Whitespace | leading/trailing spaces, double spaces |
| Casing | inconsistent casing in categorical columns (usa / USA / Usa) |
| Number-as-text | numeric values stored as text; stray $, ,, % in number cells |
| Dates | mixed formats in the same column (3/8/26, 2026-03-08, March 8 2026) |
| Duplicates | exact-duplicate rows and near-duplicates (case/whitespace differences) |
| Blanks | empty cells in otherwise-populated columns |
| Mixed types | a column that's 98% numbers but has 3 text entries |
| Encoding | mojibake (é, ’), non-printing characters |
| Errors | #REF!, #N/A, #VALUE!, #DIV/0! |
Show a summary table before changing anything:
| Column | Issue | Count | Proposed Fix |
|---|
=TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
asyrafhussin/agent-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: clean-data-xls is focused, and the summary matches what you get after install.
clean-data-xls has been reliable in day-to-day use. Documentation quality is above average for community skills.
clean-data-xls reduced setup friction for our internal harness; good balance of opinion and flexibility.
clean-data-xls is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend clean-data-xls for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
clean-data-xls fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in clean-data-xls — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: clean-data-xls is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for clean-data-xls matched our evaluation — installs cleanly and behaves as described in the markdown.
clean-data-xls fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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