spreadsheet▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
IMPORTANT: System and user instructions always take precedence.
Spreadsheet Skill (Create, Edit, Analyze, Visualize)
When to use
- Build new workbooks with formulas, formatting, and structured layouts.
- Read or analyze tabular data (filter, aggregate, pivot, compute metrics).
- Modify existing workbooks without breaking formulas or references.
- Visualize data with charts/tables and sensible formatting.
IMPORTANT: System and user instructions always take precedence.
Workflow
- Confirm the file type and goals (create, edit, analyze, visualize).
- Use
openpyxlfor.xlsxedits andpandasfor analysis and CSV/TSV workflows. - If layout matters, render for visual review (see Rendering and visual checks).
- Validate formulas and references; note that openpyxl does not evaluate formulas.
- Save outputs and clean up intermediate files.
Temp and output conventions
- Use
tmp/spreadsheets/for intermediate files; delete when done. - Write final artifacts under
output/spreadsheet/when working in this repo. - Keep filenames stable and descriptive.
Primary tooling
- Use
openpyxlfor creating/editing.xlsxfiles and preserving formatting. - Use
pandasfor analysis and CSV/TSV workflows, then write results back to.xlsxor.csv. - If you need charts, prefer
openpyxl.chartfor native Excel charts.
Rendering and visual checks
- If LibreOffice (
soffice) and Poppler (pdftoppm) are available, render sheets for visual review:soffice --headless --convert-to pdf --outdir $OUTDIR $INPUT_XLSXpdftoppm -png $OUTDIR/$BASENAME.pdf $OUTDIR/$BASENAME
- If rendering tools are unavailable, ask the user to review the output locally for layout accuracy.
Dependencies (install if missing)
Prefer uv for dependency management.
Python packages:
uv pip install openpyxl pandas
If uv is unavailable:
python3 -m pip install openpyxl pandas
Optional (chart-heavy or PDF review workflows):
uv pip install matplotlib
If uv is unavailable:
python3 -m pip install matplotlib
System tools (for rendering):
# macOS (Homebrew)
brew install libreoffice poppler
# Ubuntu/Debian
sudo apt-get install -y libreoffice poppler-utils
If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.
Environment
No required environment variables.
Examples
- Runnable Codex examples (openpyxl):
references/examples/openpyxl/
Formula requirements
- Use formulas for derived values rather than hardcoding results.
- Keep formulas simple and legible; use helper cells for complex logic.
- Avoid volatile functions like INDIRECT and OFFSET unless required.
- Prefer cell references over magic numbers (e.g.,
=H6*(1+$B$3)not=H6*1.04). - Guard against errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?) with validation and checks.
- openpyxl does not evaluate formulas; leave formulas intact and note that results will calculate in Excel/Sheets.
Citation requirements
- Cite sources inside the spreadsheet using plain text URLs.
- For financial models, cite sources of inputs in cell comments.
- For tabular data sourced from the web, include a Source column with URLs.
Formatting requirements (existing formatted spreadsheets)
- Render and inspect a provided spreadsheet before modifying it when possible.
- Preserve existing formatting and style exactly.
- Match styles for any newly filled cells that were previously blank.
Formatting requirements (new or unstyled spreadsheets)
- Use appropriate number and date formats (dates as dates, currency with symbols, percentages with sensible precision).
- Use a clean visual layout: headers distinct from data, consistent spacing, and readable column widths.
- Avoid borders around every cell; use whitespace and selective borders to structure sections.
- Ensure text does not spill into adjacent cells.
Color conventions (if no style guidance)
- Blue: user input
- Black: formulas/derived values
- Green: linked/imported values
- Gray: static constants
- Orange: review/caution
- Light red: error/flag
- Purple: control/logic
- Teal: visualization anchors (key KPIs or chart drivers)
Finance-specific requirements
- Format zeros as "-".
- Negative numbers should be red and in parentheses.
- Always specify units in headers (e.g., "Revenue ($mm)").
- Cite sources for all raw inputs in cell comments.
Investment banking layouts
If the spreadsheet is an IB-style model (LBO, DCF, 3-statement, valuation):
- Totals should sum the range directly above.
- Hide gridlines; use horizontal borders above totals across relevant columns.
- Section headers should be merged cells with dark fill and white text.
- Column labels for numeric data should be right-aligned; row labels left-aligned.
- Indent submetrics under their parent line items.
How to use spreadsheet on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add spreadsheet
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches spreadsheet from GitHub repository davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate spreadsheet. Access the skill through slash commands (e.g., /spreadsheet) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★37 reviews- ★★★★★Meera Torres· Dec 20, 2024
We added spreadsheet from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mia Reddy· Nov 11, 2024
Keeps context tight: spreadsheet is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Meera Khan· Oct 2, 2024
spreadsheet is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yash Thakker· Sep 25, 2024
We added spreadsheet from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Sakura Srinivasan· Sep 17, 2024
Keeps context tight: spreadsheet is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yuki Zhang· Sep 1, 2024
We added spreadsheet from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ava Mehta· Aug 20, 2024
spreadsheet fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dhruvi Jain· Aug 16, 2024
spreadsheet fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Luis Abebe· Aug 8, 2024
spreadsheet is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ava Gonzalez· Jul 27, 2024
spreadsheet reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 37