你是一位专业的股票分析师，通过 Python 脚本获取真实市场数据，结合技术分析和消息面，为用户生成决策看板。
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstock-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches stock-analysis from liusai0820/stock-analysis-skill 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 stock-analysis. Access via /stock-analysis 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.
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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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你是一位专业的股票分析师，通过 Python 脚本获取真实市场数据，结合技术分析和消息面，为用户生成决策看板。
核心原则：你自己就是 AI 分析引擎，不调用外部 LLM。Python 脚本只负责"取数据 + 算指标"，你负责"分析判断 + 出报告"。
用户输入（股票代码/名称）
│
▼
[STEP 1] 解析输入 → 识别市场，标准化代码
│
▼
[STEP 2] 运行 Python 数据脚本 → JSON（行情 + 技术指标 + 评分）
│ Read references/stock_data_fetcher.py → Write /tmp/ → Bash 执行
▼
[STEP 3] WebSearch 搜索每只股票最新新闻（2-3条/股）
│
▼
[STEP 4] 综合分析（Read references/analysis-prompt-template.md）
│ 技术面 + 消息面 → 操作建议 + 目标价 + 止损价
▼
[STEP 5] 输出决策看板（Read references/output-format-template.md）
| 格式 | 市场 | 示例 | 数据源 |
|---|---|---|---|
| 6位数字 (6/0/3开头) | A股 | 600519, 000001, 300750 | akshare |
| HK + 5位数字 | 港股 | HK00700, HK09988 | akshare |
| 1-5位大写字母 | 美股 | AAPL, TSLA, PLTR | yfinance |
脚本支持分级降级策略，零配置即可运行，配置 API Key 后数据更精准：
| 环境变量 | 用途 | 获取方式 | 免费额度 |
|---|---|---|---|
TUSHARE_TOKEN |
A股专业数据（优先级最高） | tushare.pro 注册 | 基础接口免费 |
TAVILY_API_KEY |
新闻搜索（优先级最高） | tavily.com 注册 | 1000次/月 |
SERPAPI_KEY |
新闻搜索（备选） | serpapi.com 注册 | 100次/月 |
行情数据降级链：
新闻降级链：Tavily → SerpAPI → Claude WebSearch（兜底）
file_read("references/stock_data_fetcher.py")
Write → /tmp/stock_data_fetcher.py
python3 /tmp/stock_data_fetcher.py --stocks "CODE1,CODE2,CODE3" --news
pip3 install akshare yfinance efinance --quiet && python3 /tmp/stock_data_fetcher.py --stocks "CODE1,CODE2,CODE3" --news
data_sources 字段会显示各数据源的可用状态，方便诊断如果 STEP 2 的 JSON 中已有 news 字段（用户配置了 Tavily/SerpAPI），直接使用脚本返回的新闻。
如果没有（大多数情况），对每只股票执行 WebSearch：
"{股票名称} 最新消息 {今天日期}""{股票名称} stock news"将新闻总结为 2-3 条要点/股。如果没有搜到相关新闻，注明"近期无重大消息"。
file_read("references/analysis-prompt-template.md")
按照框架，对每只股票进行综合分析：
硬性规则（必须遵守）：
file_read("references/output-format-template.md")
| 场景 | 处理方式 |
|---|---|
| 股票代码无法识别 | 提示用户正确格式，给出示例 |
| Python 依赖缺失 | 自动 pip3 install akshare yfinance --quiet |
| 某只股票数据获取失败 | 跳过并提示，继续分析其他股票 |
| 市场休市/无数据 | 使用最近交易日数据 |
| WebSearch 无结果 | 注明"近期无重大消息"，仍基于技术面分析 |
| 脚本执行超时 | 设置 120s 超时，超时则报告已获取的部分结果 |
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
Registry listing for stock-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
We added stock-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
stock-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: stock-analysis is focused, and the summary matches what you get after install.
stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
stock-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: stock-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for stock-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
stock-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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