Quantitative trading strategy development, backtesting, and live execution using FinLab's data and simulation engine.
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
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
Submit your Claude Code skill and start earning
epx add koreal6803/finlab-ai --skill finlabCompatible with
Supported agents
Fetch 900+ financial data columns (price, fundamentals, valuation, institutional trading) via data.get() with universe filtering by market and industry
Build stock selection strategies using FinLabDataFrame methods: trend detection, moving averages, ranking, and factor combinations with boolean logic
Backtest strategies with sim() including risk management (stop-loss,
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfinlabExecute the skills CLI command in your project's root directory to begin installation:
epx add koreal6803/finlab-ai --skill finlabFetches finlab from koreal6803/finlab-ai 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:
.cursor/skills/finlab
Restart Cursor to activate finlab. Access via /finlabin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
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Solid pick for teams standardizing on skills: finlab is focused, and the summary matches what you get after install.
Useful defaults in finlab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend finlab for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: finlab is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added finlab from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
finlab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for finlab matched our evaluation — installs cleanly and behaves as described in the markdown.
finlab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
finlab fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: finlab is the kind of skill you can hand to a new teammate without a long onboarding doc.
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