comp-analysis▌
anthropics/knowledge-work-plugins · updated Apr 8, 2026
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/comp-analysis
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.
Usage
/comp-analysis $ARGUMENTS
What I Need From You
Option A: Single role analysis "What should we pay a Senior Software Engineer in SF?"
Option B: Upload comp data Upload a CSV or paste your comp bands. I'll analyze placement, identify outliers, and compare to market.
Option C: Equity modeling "Model a refresh grant of 10K shares over 4 years at a $50 stock price."
Compensation Framework
Components of Total Compensation
- Base salary: Cash compensation
- Equity: RSUs, stock options, or other equity
- Bonus: Annual target bonus, signing bonus
- Benefits: Health, retirement, perks (harder to quantify)
Key Variables
- Role: Function and specialization
- Level: IC levels, management levels
- Location: Geographic pay adjustments
- Company stage: Startup vs. growth vs. public
- Industry: Tech vs. finance vs. healthcare
Data Sources
- With ~~compensation data: Pull verified benchmarks
- Without: Use web research, public salary data, and user-provided context
- Always note data freshness and source limitations
Output
Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
## Compensation Analysis: [Role/Scope]
### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |
**Sources:** [Web research, compensation data tools, or user-provided data]
### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |
### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]
If Connectors Available
If ~~compensation data is connected:
- Pull verified market benchmarks by role, level, and location
- Compare your bands against real-time market data
If ~~HRIS is connected:
- Pull current employee comp data for band analysis
- Identify outliers and retention risks automatically
Tips
- Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
- Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
- Keep data confidential — Comp data is sensitive. Results stay in your conversation.
How to use comp-analysis 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 comp-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches comp-analysis from GitHub repository anthropics/knowledge-work-plugins 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 comp-analysis. Access the skill through slash commands (e.g., /comp-analysis) 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
4.5★★★★★38 reviews- ★★★★★Ganesh Mohane· Dec 16, 2024
comp-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Omar Bhatia· Dec 12, 2024
comp-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Noor Rao· Dec 4, 2024
We added comp-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mateo Thomas· Nov 27, 2024
comp-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kwame Sethi· Nov 23, 2024
Keeps context tight: comp-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Rahul Santra· Nov 7, 2024
I recommend comp-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Olivia Jain· Nov 3, 2024
comp-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Oct 26, 2024
Useful defaults in comp-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Liam Ghosh· Oct 22, 2024
Keeps context tight: comp-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Omar Huang· Oct 18, 2024
Solid pick for teams standardizing on skills: comp-analysis is focused, and the summary matches what you get after install.
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