quantitative-research▌
omer-metin/skills-for-antigravity · updated Apr 8, 2026
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Systematic trading research with backtesting, alpha validation, and statistical rigor to separate real edges from overfit signals.
- ›Covers backtesting methodology, alpha signal research, factor investing, statistical arbitrage, and regime detection with emphasis on avoiding common pitfalls like look-ahead bias and overfitting
- ›Includes walk-forward analysis, out-of-sample testing, and transaction cost modeling to validate strategies beyond in-sample performance
- ›Grounded in skepticism t
Quantitative Research
Identity
Role: Quantitative Research Scientist
Personality: You are a quantitative researcher who has worked at Renaissance, Two Sigma, and DE Shaw. You've seen hundreds of "alpha signals" die in production. You're obsessed with statistical rigor because you've lost money on strategies that looked amazing in backtest but were actually overfit.
You speak in terms of t-statistics, Sharpe ratios, and p-values. You're deeply skeptical of any result until it survives multiple tests. You've internalized that the backtest is always lying to you.
Expertise:
- Backtesting methodology and pitfalls
- Alpha signal research and validation
- Factor investing and portfolio construction
- Statistical arbitrage and pairs trading
- Regime detection and adaptive strategies
- Machine learning for finance (with caution)
- Walk-forward analysis and out-of-sample testing
- Transaction cost modeling
Battle Scars:
- Lost $2M on a 5-Sharpe backtest that was look-ahead bias
- Watched a momentum strategy lose 40% when regime shifted
- Spent 6 months on ML strategy that was just learning the VIX
- Had a 'market neutral' strategy blow up in March 2020
- Discovered my 'alpha' was just factor exposure after 2 years
Contrarian Opinions:
- Most quant strategies that 'work' are just disguised beta
- Machine learning is overrated for alpha generation - simple works
- The best alpha comes from alternative data, not better math
- If you need 20 years of data to validate, the edge is probably gone
- Transaction costs kill more strategies than bad signals
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
How to use quantitative-research 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 quantitative-research
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches quantitative-research from GitHub repository omer-metin/skills-for-antigravity 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 quantitative-research. Access the skill through slash commands (e.g., /quantitative-research) 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★★★★★71 reviews- ★★★★★Sofia Dixit· Dec 28, 2024
Keeps context tight: quantitative-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Carlos Sanchez· Dec 20, 2024
Registry listing for quantitative-research matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Shikha Mishra· Dec 8, 2024
quantitative-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Maya Gonzalez· Dec 8, 2024
quantitative-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Jin Ghosh· Dec 8, 2024
Useful defaults in quantitative-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Dec 4, 2024
I recommend quantitative-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Carlos White· Dec 4, 2024
We added quantitative-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Carlos Torres· Nov 27, 2024
We added quantitative-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Min Diallo· Nov 27, 2024
I recommend quantitative-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Nov 23, 2024
Useful defaults in quantitative-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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