stock-sector-monitoring▌
zuoa/aj-skills · updated Apr 8, 2026
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用于基于 Tushare 的 A 股市场监测,重点支持:
Stock Sector Monitoring Skill
用于基于 Tushare 的 A 股市场监测,重点支持:
- 概念板块监测(
sector) - 龙虎榜每日明细(
lhb,top_list/ doc_id=106) - 将结果输出并保存为 Markdown 文件
When to Use
当用户出现以下意图时使用本 skill:
- “监控概念板块/行业热度/涨幅榜”
- “查询龙虎榜数据”
- “过滤 ST 股票后输出结果”
- “把监控结果保存成 markdown/md 文件”
Prerequisites
在 skill 根目录执行:
cd /Users/yujian/Code/py/aj-skills/skills/stock-sector-monitoring
依赖准备:
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -U pip
python3 -m pip install tushare pandas
Tushare token 约定从 ~/.aj-skills/.env 读取:
# ~/.aj-skills/.env
TUSHARE_TOKEN=your_token
Core Workflow
1) 采集龙虎榜数据(默认推荐)
source .venv/bin/activate
python3 scripts/sector-monitoring.py \
--once \
--data-source lhb \
--trade-date 20260213 \
--top 20 \
--format json
说明:
--data-source lhb使用龙虎榜接口(top_list)--trade-date YYYYMMDD指定交易日,默认是当天- 当前脚本已内置过滤 ST / ST / SST / SST 股票
2) 板块异动+新闻联动分析(必须)
每次执行都必须完成该分析,不需要用户额外提问;完成分析后才可生成对应报告。
Step 1: 识别异动板块
从板块监测数据中提取涨幅前 N 的板块。
Step 2: 搜索相关新闻
针对异动板块,搜索近期新闻:
- 行业政策变化
- 价格/供需变化
- 龙头公司动态
- 国际市场影响
Step 3: 形成异动原因结论
整合板块数据 + 新闻,产出可写入报告的异动原因分析结论。
3) 生成 Markdown 报告
在完成第 2 步分析后,将 JSON 输出与异动原因结论整理成 Markdown,并保存到 reports/ 目录。
报告标题要求(必须):
- 先生成一个吸引人的标题,适合社交媒体分享。
- 标题需体现“异动板块 + 关键驱动/新闻线索”,避免仅使用“日报/监测”这类平铺命名。
- 标题控制在 18-32 字,信息密度高,可读性强。
领涨板块输出要求(必须):
- 必须输出
今日领涨板块原因分析章节。 - 至少覆盖涨幅前 1-3 个板块(默认 3 个,可按数据完整性调整)。
- 每个板块必须包含:板块名+涨幅、核心逻辑、驱动因素表、趋势结论。
涨幅榜中的上榜理由必须为移动端短句,单条建议不超过 16 个汉字(或 24 个字符),仅保留关键信息。
文件命名规范:
- 龙虎榜日报:
reports/lhb-YYYYMMDD.md - 指定股票:
reports/lhb-YYYYMMDD-TS_CODE.md - 概念板块:
reports/sector-YYYYMMDD.md
Markdown 最低结构:
# 【板块异动】算力板块领涨:政策催化+龙头放量(YYYY-MM-DD)
## 今日领涨板块原因分析
### 1. 有色金属(铅锌 +5.76%、小金属 +4.65%、铜 +2.96%)
核心逻辑:需求爆发 + 供给收缩
| 驱动因素 | 具体内容 |
|---|---|
| 国际金属价格普涨 | LME 铜涨超 1.7%,锡涨超 4.6%,海外定价中枢上移 |
| AI 产业需求 | 算力与数据中心建设拉动铜等导电金属需求 |
| 新能源需求 | 锑、钨、稀土、锡等小金属受新能源产业链拉动 |
| 供给管控 | 国内供给管控政策持续,供需缺口支撑价格 |
| 货币环境 | 国际货币环境宽松,风险偏好提升带动商品走强 |
趋势结论:有色金属正从周期品向战略资源属性演进,阶段性主线仍看供需错配与产业升级共振。
## 涨幅榜 Top N
| 排名 | 代码 | 名称 | 涨跌幅 | 龙虎榜净买入额(亿) | 净买额占比(%) | 上榜理由(短句) |
|---|---|---|---:|---:|---:|---|
## 综合评分 Top N
| 排名 | 代码 | 名称 | 综合评分 | 涨跌幅 | 龙虎榜净买入额(亿) |
|---|---|---|---:|---:|---:|
## 预警
- 列出超过阈值的标的(若无则写“无”)
## 免责声明
本文为市场复盘与信息整理,不构成任何投资建议。市场有风险,决策需结合自身风险承受能力并独立判断。
4) 保存要求(必须)
每次响应都应:
- 明确输出文件路径
- 将最终结果落盘为
.md - 在回复中给出“已保存到:
<path>”
5) 发布询问与公众号发布(必须)
报告生成并保存后,必须追加询问用户是否需要发布到公众号。
标准询问:
- “报告已生成,是否需要我直接发布到公众号?”
执行规则:
- 若用户确认“需要”,调用
post-to-wechatskill(baoyu-post-to-wechat)发布。 - 若用户未确认或拒绝,不进行发布操作,仅保留本地报告文件。
Output Rules
- 默认输出 Markdown,不仅在对话中展示。
- 仅输出分析报告正文,不输出数据来源说明、接口信息或过滤规则说明。
- 报告主标题必须为“吸引人的社交媒体风格标题”,不能使用通用占位标题。
- 报告必须包含
今日领涨板块原因分析,且每个领涨板块都要给出“核心逻辑 + 驱动因素表 + 趋势结论”。 涨幅榜的上榜理由必须压缩为移动端短句,不得出现过长原文搬运。- 报告结尾必须追加
免责声明章节,且位于最后一节。 - 如用户未指定文件名,按命名规范自动生成。
- 如用户指定日期或 TS 代码,文件名必须反映该条件。
- 若当日无数据,仍生成 Markdown,并在正文写明“无可用数据(非交易日/无上榜)”。
- 报告保存完成后,必须询问用户是否发布到公众号;仅在用户明确同意后调用
post-to-wechatskill。
Quality Checklist
- 已完成板块异动+新闻联动分析,并写入报告
- 报告标题已按社交媒体分享场景优化
- 领涨板块输出已按模板包含:板块涨幅、核心逻辑、驱动因素表、趋势结论
- 涨幅榜
上榜理由已压缩为移动端短句 - 报告末尾已追加免责声明
- 报告正文未包含数据来源说明与过滤规则说明
- 表格列名完整、单位清晰(金额统一“亿元”)
- 报告已保存为
.md - 回复中包含保存路径
- 已询问用户是否发布到公众号;如用户确认,已调用
post-to-wechatskill 发布
Notes
- 龙虎榜接口权限要求较高(
top_list通常需积分权限)。 - 若接口返回为空,优先提示交易日与权限问题,再输出空报告模板。
How to use stock-sector-monitoring on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add stock-sector-monitoring
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches stock-sector-monitoring from GitHub repository zuoa/aj-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate stock-sector-monitoring. Access the skill through slash commands (e.g., /stock-sector-monitoring) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
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
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★67 reviews- ★★★★★Sofia Smith· Dec 28, 2024
stock-sector-monitoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Li Thomas· Dec 28, 2024
stock-sector-monitoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mateo Malhotra· Dec 28, 2024
Solid pick for teams standardizing on skills: stock-sector-monitoring is focused, and the summary matches what you get after install.
- ★★★★★Mateo Liu· Dec 24, 2024
Keeps context tight: stock-sector-monitoring is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kwame Huang· Dec 20, 2024
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Shikha Mishra· Dec 16, 2024
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Evelyn Ghosh· Dec 16, 2024
We added stock-sector-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mei Wang· Dec 4, 2024
We added stock-sector-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mei Garcia· Nov 19, 2024
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Evelyn Li· Nov 19, 2024
I recommend stock-sector-monitoring for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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