
Fundamentals & Valuation
SKILL.md
Find Profitable Stocks (自由现金流掘金) is a screening and analysis Skill that identifies companies generating real cash profit using Free Cash Flow (FCF = Operating Cash Flow − Capital Expenditure) alongside standard fundamental metrics.
概览
Find Profitable Stocks (自由现金流掘金) is a screening and analysis Skill that identifies companies generating real cash profit using Free Cash Flow (FCF = Operating Cash Flow − Capital Expenditure) alongside standard fundamental metrics.
Find Profitable Stocks (自由现金流掘金) is a screening and analysis Skill that identifies companies generating real cash profit using Free Cash Flow (FCF = Operating Cash Flow − Capital Expenditure) alongside standard fundamental metrics. It retrieves financial data from East Money / Sina / Tencent APIs, computes a Health Score (0–100) and letter Grade (A–D), and ranks companies by combining valuation (PE, PB), profitability (ROE, profit margin), growth (revenue and profit trends), and technical signals (price action, volume). Outputs include key metrics, ranked results, and concise analysis summaries. Typical uses: screen for high-FCF stocks, analyze a single ticker’s cash-flow quality, compare multiple companies, and build shortlists. Requires Python 3.x, requests, and internet; falls back to demo/mock data when offline. Core advantages are cash-focused quality filtering, transparent scoring, and API-driven real-time screening.
Skill.md
这个 Skill 如何工作
Find Profitable Stocks (自由现金流掘金) is a screening and analysis Skill that identifies companies generating real cash profit using Free Cash Flow (FCF = Operating Cash Flow − Capital Expenditure) alongside standard fundamental metrics.
SKILL.md - Find Profitable Stocks (自由现金流掘金)
Description
Find truly profitable companies using Free Cash Flow (FCF) and fundamental analysis strategy.
Free Cash Flow = Operating Cash Flow - Capital Expenditure
This skill screens for companies that generate real cash profit, not just accounting earnings.
When to Use
- User asks "find profitable stocks"
- User asks "free cash flow analysis"
- User asks "真正的赚钱公司" / "现金流好的股票"
- User asks "筛选优质股票"
- User wants to analyze a specific stock's cash flow quality
How It Works
- Fetches financial data from public APIs (East Money / Sina / Tencent)
- Calculates health scores based on:
- Valuation (PE/PB)
- Profitability (ROE, profit margin)
- Growth (revenue/profit growth)
- Technical (price action, volume)
- Ranks and outputs results
Usage
Analyze a single stock
分析 600938 的基本面
筛选 股票 159201
Screen for profitable stocks
筛选自由现金流好的股票
找真正赚钱的公司
帮我选几只优质股票
Compare multiple stocks
对比 600938 和 600519
Output Format
Returns:
- Health Score (0-100)
- Grade (A/B/C/D)
- Key metrics (PE, ROE, growth, etc.)
- Analysis summary
Data Sources
- Primary: East Money API (push2.eastmoney.com)
- Fallback: Demo/Mock data (when network unavailable)
- Note: Requires internet access for real-time data
Requirements
- Python 3.x
- requests package
- Internet connection
Status
Ready for use - Network dependent (will show demo data if offline)
最适合用于
何时使用
Find Profitable Stocks (自由现金流掘金) is a screening and analysis Skill that identifies companies generating real cash profit using Free Cash Flow (FCF = Operating Cash Flow − Capital Expenditure) alongside standard fundamental metrics.

01 · 会前准备
准备决策简报
在投委会开会前,把零散证据整理成结构化的论据。

02 · 团队协作
统一交接标准
让分析师、组合经理与 Agent 产出一致的研究结果。

03 · 实时更新
更新投资逻辑
出现新催化剂、KPI 发布或财报结果后,更新情景假设。
社区反馈
越用越好用。
随着 Skill 被使用与评审,反馈将展示在这里。
发现更多
相关 Skills
查看全部Industry Primer
Analyze an industry's structure, profit pools, key players, supply chain, and regime drivers. Use when starting research on an industry, mapping market structure, or framing sector-level opportunity…
Bear Case Builder
Stress test consensus with bearish counter-narratives, failure modes, and probability-severity analysis. Use when building downside cases, short theses, or risk scenarios against a bullish narrative.
Business Model Comp Matching
Match a company to the right peer set by business model, revenue mix, margin structure, and economics. Use when standard comp sets are misleading or when the user asks for more precise peer selection.