用于基于 Tushare 的 A 股市场监测,重点支持:
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstock-sector-monitoringExecute the skills CLI command in your project's root directory to begin installation:
Fetches stock-sector-monitoring from zuoa/aj-skills 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-sector-monitoring. Access via /stock-sector-monitoring 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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用于基于 Tushare 的 A 股市场监测,重点支持:
sector)lhb,top_list / doc_id=106)当用户出现以下意图时使用本 skill:
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
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:
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。
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):
领涨板块输出要求(必须):
今日领涨板块原因分析 章节。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:
reports/lhb-YYYYMMDD.mdreports/lhb-YYYYMMDD-TS_CODE.mdreports/sector-YYYYMMDD.mdMarkdown 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。
每次响应都应:
.md<path>”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:
执行规则:
post-to-wechat skill(baoyu-post-to-wechat)发布。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。免责声明 章节,且位于最后一节。post-to-wechat skill。上榜理由 已压缩为移动端短句.mdpost-to-wechat skill 发布top_list 通常需积分权限)。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
ailabs-393/ai-labs-claude-skills
stock-sector-monitoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
stock-sector-monitoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: stock-sector-monitoring is focused, and the summary matches what you get after install.
Keeps context tight: stock-sector-monitoring is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added stock-sector-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added stock-sector-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in stock-sector-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend stock-sector-monitoring for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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