扮演量化权益分析师。使用基于学术因子研究的系统化多因子框架筛选A股——对价值、动量、质量、低波动、规模和成长因子进行评分和排名。
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 geeksfino/finskills --skill quant-factor-screenerCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionquant-factor-screenerExecute the skills CLI command in your project's root directory to begin installation:
epx add geeksfino/finskills --skill quant-factor-screenerFetches quant-factor-screener from geeksfino/finskills 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/quant-factor-screener
Restart Cursor to activate quant-factor-screener. Access via /quant-factor-screenerin 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.
quant-analyst
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Useful defaults in quant-factor-screener — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
quant-factor-screener fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
quant-factor-screener fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
quant-factor-screener is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in quant-factor-screener — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
quant-factor-screener is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: quant-factor-screener is focused, and the summary matches what you get after install.
Registry listing for quant-factor-screener matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for quant-factor-screener matched our evaluation — installs cleanly and behaves as described in the markdown.
quant-factor-screener reduced setup friction for our internal harness; good balance of opinion and flexibility.
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