> 生成 A 股医药行业日报:从新浪财经抓取 20 只核心医药股实时行情,分析 7 大细分赛道排名、涨跌榜 TOP 3、资金流向,然后通过飞书发送富文本日报。 覆盖恒瑞医药、百济神州、智飞生物、药明康德、迈瑞医疗、片仔癀、云南白药等 20 只核心标的。 当用户需要 医药日报、医药行业日报、pharma daily report、 新浪财经医药数据、A 股医药板块分析、飞书推送医药数据、 制药行业快报 时触发。也适用于"帮我看一下今天医药板块"、 "给飞书发一份今天的医药数据"等口语化请求。
npx skills add https://github.com/daymade/claude-code-skills --skill pharma-daily-report
从新浪财经免费公开接口抓取 A 股医药板块实时行情,分析后通过飞书发送日报。
hq.sinajs.cn,GBK 编码,免费无需认证)能做的:
不能做的:
urllib,无需 pip 装任何包)hq.sinajs.cn如果用户没装 lark-cli:
npm install -g @larksuite/cli
lark-cli auth login --recommend
注意:飞书是国内服务,lark-cli 命令行前必须加 LARK_CLI_NO_PROXY=1。
问用户确认:
lark-cli profile list 查看(详见 references/feishu_profiles.md) LARK_CLI_NO_PROXY=1 lark-cli im +chat-search --query " " --page-size 20
发给自己就用 Feishu Assistant 对话。发给群就搜群名。
references/stock_pool.md)。用户可以说"只看创新药"或"加上某某股票"来定制。直接执行 scripts/daily_pipeline.py:
python3 scripts/daily_pipeline.py \
--feishu-profile <你的飞书profile名> \
--feishu-chat-id <你的飞书chat_id>
脚本一气呵成:抓数据 → 分析 → 生成日报 → 发飞书 → 存快照。
告诉用户关键数据:
http://hq.sinajs.cn/list=sh600276,sz300122,...
返回 GBK 编码文本,每行格式:
var hq_str_sh600276="股票名称,今开,昨收,现价,最高,最低,...,成交金额,...,日期,时间"
32 个逗号分隔字段。关键字段(从 0 开始数):
| 位置 | 含义 | 示例 |
|------|------|------|
| 0 | 股票名称 | 恒瑞医药 |
| 2 | 昨收 | 45.60 |
| 3 | 现价 | 46.10 |
| 4 | 今日最高 | 46.50 |
| 5 | 今日最低 | 45.30 |
| 9 | 成交金额(元) | 850000000 |
代码前缀规则:6 开头 = sh,其他 = sz。
完整字段速查见 references/sina_api.md。
20 只股票分为 7 个赛道(见 references/stock_pool.md),分析时按赛道聚合涨跌幅和成交额。
新浪免费接口不提供买卖方向数据。脚本用"涨的股票的成交额 - 跌的股票的成交额"粗略估算净流向,报告中标注为"估算"。
新浪返回的个别名称带空格(如"新 和 成"),脚本内置归一化。
飞书 markdown 消息有约 4096 字节限制。超长自动截断并标注。
交易时间 9:30-15:00 外,新浪返回上一个收盘价,涨跌幅接近 0。报告仍会显示,但数据时间可以判断是否实时。
新浪不可达时脚本报错退出,不使用缓存(金融数据宁可没有也不能是旧的)。
日报做好后,用户可能想每天自动发送。建议:
> 日报测试发送成功!要不要设置每天早上 9:03 自动发到飞书?
>
> A) 设工作日 9:03 — 每个交易日开盘前收到(推荐)
> B) 设每天 9:03 — 周末也发(数据不动但能看到)
> C) 暂时不设,手动跑就行
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take daymade/pharma-daily-report from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm.
Without those the skill loads but fails at the first command.