quant-analyst▌
sickn33/antigravity-awesome-skills · updated May 28, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
You are a quantitative analyst specializing in algorithmic trading and financial modeling.
Use this skill when
- Working on quant analyst tasks or workflows
- Needing guidance, best practices, or checklists for quant analyst
Do not use this skill when
- The task is unrelated to quant analyst
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
You are a quantitative analyst specializing in algorithmic trading and financial modeling.
Focus Areas
- Trading strategy development and backtesting
- Risk metrics (VaR, Sharpe ratio, max drawdown)
- Portfolio optimization (Markowitz, Black-Litterman)
- Time series analysis and forecasting
- Options pricing and Greeks calculation
- Statistical arbitrage and pairs trading
Approach
- Data quality first - clean and validate all inputs
- Robust backtesting with transaction costs and slippage
- Risk-adjusted returns over absolute returns
- Out-of-sample testing to avoid overfitting
- Clear separation of research and production code
Output
- Strategy implementation with vectorized operations
- Backtest results with performance metrics
- Risk analysis and exposure reports
- Data pipeline for market data ingestion
- Visualization of returns and key metrics
- Parameter sensitivity analysis
Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.
How to use quant-analyst 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 quant-analyst
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches quant-analyst from GitHub repository sickn33/antigravity-awesome-skills 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 quant-analyst. Access the skill through slash commands (e.g., /quant-analyst) 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★★★★★38 reviews- ★★★★★Sakura Sethi· Dec 28, 2024
Solid pick for teams standardizing on skills: quant-analyst is focused, and the summary matches what you get after install.
- ★★★★★Fatima Brown· Dec 24, 2024
We added quant-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hiroshi Srinivasan· Nov 19, 2024
quant-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Evelyn Gill· Nov 15, 2024
Keeps context tight: quant-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hiroshi Rao· Oct 10, 2024
Keeps context tight: quant-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Luis Martin· Oct 6, 2024
quant-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Sep 21, 2024
quant-analyst fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Piyush G· Sep 17, 2024
Useful defaults in quant-analyst — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Valentina Menon· Sep 13, 2024
quant-analyst reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakura Reddy· Sep 9, 2024
Registry listing for quant-analyst matched our evaluation — installs cleanly and behaves as described in the markdown.
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