Systematic trading research with backtesting, alpha validation, and statistical rigor to separate real edges from overfit signals.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionquantitative-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches quantitative-research from omer-metin/skills-for-antigravity 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 quantitative-research. Access via /quantitative-research in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
4
total installs
4
this week
50
GitHub stars
0
upvotes
Run in your terminal
4
installs
4
this week
50
stars
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:
Battle Scars:
Contrarian Opinions:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.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.
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.
omer-metin/skills-for-antigravity
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: quantitative-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for quantitative-research matched our evaluation — installs cleanly and behaves as described in the markdown.
quantitative-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
quantitative-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in quantitative-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend quantitative-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added quantitative-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added quantitative-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend quantitative-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in quantitative-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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