Agent skill · Business & Finance

longbridge-factor-screen

Fundamental factor stock screening — filter value or growth stocks using PE, PB, ROE, revenue growth, net-profit growth, and dividend yield across A-share, HK, and US markets. Outputs a candidate table ranked by composite factor score. Triggers: "基本面筛选", "因子选股", "价值选股", "成长选股", "低PE选股", "高ROE", "股息筛选", "PE筛选", "PB筛选", "多条件选股", "基本面因子", "量化选股", "基本面篩選", "因子選股", "價值選股", "成長選股", "低PE選股", "股息篩選", "factor screening", "value screen", "growth screen", "low PE filter", "high ROE screen", "dividend screen", "fundamental factor", "multi-factor stock screen".

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill longbridge-factor-screen --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-factor-screen/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# longbridge-factor-screen Fundamental multi-factor screener. Applies user-defined thresholds across PE, PB, ROE, revenue growth, profit growth, and dividend yield to filter a candidate list and rank survivors by composite score. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - *"帮我筛选 PE < 15 且 ROE > 15% 的 A 股"*, *"screen for low-PE high-ROE A-shares"* - *"成长股筛选:营收增速 > 20%"*, *"growth screen: revenue CAGR > 20%"* - *"高股息蓝筹股 港股"*, *"high-dividend HK blue chips"* - *"价值选股:PB < 1 且 ROE > 10%"*, *"value screen: PB < 1 and ROE > 10%"* - *"多条件选股"*, *"multi-factor stock screen"* For index/ETF constituent lists route to `longbridge-constituent`. For single-stock deep-dive route to `longbridge-fundamental` or `longbridge-valuation`. ## Supported factors | Factor | 简体 | 繁體 | Source CLI | |---|---|---|---| | PE (TTM) | 市盈率 | 市盈率 | `calc-index` or `valuation` | | PB | 市净率 | 市淨率 | `calc-index` or `valuation` | | PS | 市销率 | 市銷率 | `calc-index` or `valuation` | | ROE | 净资产收益率 | 淨資產收益率 | `operating` or `financial-report` | | Revenue YoY | 营收增速 | 營收增速 | `operating` or `financial-report` | | Net profit YoY | 净利润增速 | 淨利潤增速

What's inside
Steps it walks through
  1. When to use
  2. Supported factors
  3. Workflow
  4. CLI
  5. Output
  6. Limitations
  7. Error handling
  8. MCP fallback
  9. Related skills
  10. File layout
Ships with 1 file
  • metadata.json
Commands it runs
longbridge constituent 000300.SH --format json   # run --help for available flags
longbridge calc-index --help
longbridge operating --help
longbridge valuation --help
longbridge dividend --help
longbridge calc-index <SYMBOL> --format json
longbridge operating <SYMBOL> --format json
longbridge dividend <SYMBOL> --format json
Step 0: discover flags
Step 1: get universe (if using an index)
More from claude-skill-registry
All skills →
About this skill
What does the longbridge-factor-screen skill do?

Fundamental factor stock screening — filter value or growth stocks using PE, PB, ROE, revenue growth, net-profit growth, and dividend yield across A-share, HK, and US markets. Outputs a candidate table ranked by composite factor score. Triggers: "基本面筛选", "因子选股", "价值选股", "成长选股", "低PE选股", "高ROE", "股息筛选", "PE筛选", "PB筛选", "多条件选股", "基本面因子", "量化选股", "基本面篩選", "因子選股", "價值選股", "成長選股", "低PE選股", "股息篩選", "factor screening", "value screen", "growth screen", "low PE filter", "high ROE screen", "dividend screen", "fundamental factor", "multi-factor stock screen".

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-factor-screen --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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