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。
Works with
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:
Fetches 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:
Restart Cursor to activate quant-factor-screener. Access via /quant-factor-screener 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
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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。
与用户确认：
| 输入 | 选项 | 默认 |
|---|---|---|
| 选股池 | 沪深300 / 中证500 / 中证1000 / 全A / 自定义 | 中证800 |
| 因子 | 全部6个或特定因子 | 全部 |
| 因子权重 | 等权或自定义 | 等权 |
| 行业约束 | 行业中性或不约束 | 行业中性 |
| 结果数量 | 前N只 | 前20只 |
| 宏观研判 | 当前因子择时评估 | 自动判断 |
| 排除项 | 行业、概念、特定个股 | 无 |
对选股池中每只股票计算各因子得分。详细定义参见 references/factor-methodology.md。
| 因子 | 主要指标 | 默认权重 |
|---|---|---|
| 价值 | 盈利收益率、PB倒数、FCF收益率、EV/EBITDA | 1/6 |
| 动量 | 12-1月价格动量、盈利预期修正动量 | 1/6 |
| 质量 | ROE、盈利稳定性、低杠杆、应计质量 | 1/6 |
| 低波动 | 已实现波动率（1年）、Beta、下行偏差 | 1/6 |
| 规模 | 市值（越小得分越高） | 1/6 |
| 成长 | 营收增速、盈利增速、利润率扩张 | 1/6 |
对每个因子：
综合得分 = Σ (因子权重 × 因子得分)
按综合得分从高到低排列所有股票。
评估当前宏观环境及其对因子表现的影响。参见 references/factor-methodology.md。
| 宏观环境 | 利好因子 | 不利因子 |
|---|---|---|
| 经济复苏初期 | 规模、动量 | 低波动 |
| 经济扩张中期 | 动量、成长 | 价值 |
| 经济扩张末期 | 质量、价值 | 规模 |
| 经济下行 | 低波动、质量 | 动量、规模 |
| 经济触底 | 价值、规模、动量 | 低波动 |
基于当前研判，提供因子择时叠加以调整权重。
评估热门因子是否过度拥挤：
| 信号 | 拥挤 | 不拥挤 |
|---|---|---|
| 估值价差 | 因子内高低分组估值差收窄 | 估值差扩大 |
| 因子收益相关性 | 高（许多人跟随相同信号） | 低 |
| ETF/基金资金流入 | 因子相关产品大量净申购 | 净赎回 |
| 媒体/分析师关注 | 被广泛讨论 | 被忽视 |
标记拥挤的因子——收益可能被压缩。
格式参见 references/output-template.md：
如需实时市场数据支撑分析，请使用金融数据工具包技能（findata-toolkit-cn）。该工具包提供A股实时行情、财务指标、董监高增减持、北向资金、宏观数据等功能，所有数据源免费，无需API密钥。
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
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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