Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shouldnotappearcalm/a-share-skill --skill a-share-datagit clone --depth 1 https://github.com/shouldnotappearcalm/a-share-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shouldnotappearcalm/a-share-skill/a-share-data)<a href="https://agentmods.dev/skills/shouldnotappearcalm/a-share-skill/a-share-data"><img src="https://agentmods.dev/badge/skills/shouldnotappearcalm/a-share-skill/a-share-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shouldnotappearcalm/a-share-skill/a-share-data"><img src="https://agentmods.dev/badge/skills/shouldnotappearcalm/a-share-skill/a-share-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.02349 |
| Opus 5.5 | $0.00037 | $0.00940 |
| Sonnet 5.5 | $0.00019 | $0.00470 |
| Haiku 4.5 | $0.00009 | $0.00235 |
Grade A, and why
a-share-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A股数据查询
使用方式
从本技能的 scripts/ 调用取数程序，先按下表选择入口，再按需读取对应参考。代码、依赖和参考文件均随本技能目录安装。
SKILL_DIR="<本技能安装目录的绝对路径>"
cd "$SKILL_DIR"
python3 -m pip install akshare MyTT pandas numpy requests beautifulsoup4
Python 3.9+；仅查询公司资讯时只需 requests；新增概念/公司事件/池详情/席位只需 requests，概念的同花顺解析另需 beautifulsoup4。以下示例均从技能根目录执行。
功能与脚本路由
| 用户要查什么 | 入口 | 何时阅读参考 |
|---|---|---|
| 单票/批量报价、分钟线、大盘指数、龙虎榜、涨跌停、资金流 | scripts/fetch_realtime.py |
参数见 API参考 |
| 历史K线、复权选择、分红、交易日历、指数成分、业绩预告/快报、宏观 | scripts/fetch_history.py |
普通参数见 API参考；复权与缓存见 数据口径 |
| 综合或单项财务指标、某日之前已公告的指标 | scripts/fetch_history.py |
先读 财务日期口径 |
| MA、MACD、KDJ、RSI、BOLL等技术指标 | scripts/fetch_technical.py |
API参考 |
| 某公司最近新闻/公告、近7天发生什么 | scripts/fetch_company_news.py |
公司资讯；优先于全市场业绩或舆情入口 |
| 公司有哪些概念、为何入选、同花顺常规/新兴/其他概念 | scripts/fetch_stock_concepts.py |
先读 概念与重点数据说明；保留供应商依据原文 |
| 个股/日期窗口解禁、回购计划及进度、已披露股东增减持 | scripts/fetch_corporate_actions.py |
先读 公司事件说明；按公告/解禁日期分别查询 |
| 涨停/炸板的封板时间、炸板次数、封单、连板及原始详情；龙虎榜买卖席位 | scripts/fetch_market_details.py |
先读 池详情与龙虎榜席位；核对真实交易日及沪深/全A覆盖范围 |
| 行业目录、申万1/2/3级分类、指定行业真实成分、行业指数历史 | scripts/fetch_industry.py |
先读 行业目录与成分 |
| 单只/多只股票所属行业与证券名称 | scripts/fetch_sector_info.py |
API参考；当前文档化路径加 --no-concepts |
| 热门行业/概念、涨跌幅榜、热力图、指定板块groupKey成员、市场快讯 | scripts/fetch_danginvest.py |
先读 板块与快讯；检查stale |
| 个股业绩预告/快报和旧事件聚合入口 | scripts/fetch_stock_events.py |
API参考；解禁/回购/增减持优先用新的公司事件入口，未分类新闻不能填成合同/监管事件 |
| A+H公司清单、H股上市日期范围 | scripts/fetch_ah_stocks.py |
API参考 |
| A股赴港递表、聆讯、备案、招股、定价、配售、上市节点 | scripts/fetch_ah_ipo_timeline.py |
API参考 |
What ships with it
27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api-reference.md 12 KB
- references/company-news.md 4.5 KB
- references/danginvest-api-reference.md 6.7 KB
- references/data-contracts.md 5.2 KB
- references/reference-data.md 13 KB
- scripts/Ashare.py 5.5 KB runs code
- scripts/check_data_health.py 10 KB runs code
- scripts/data_runtime.py 4.5 KB runs code
- scripts/fetch_ah_ipo_timeline.py 19 KB runs code
- scripts/fetch_ah_stocks.py 9.4 KB runs code
- scripts/fetch_company_news.py 18 KB runs code
- scripts/fetch_corporate_actions.py 6.7 KB runs code
- scripts/fetch_danginvest.py 14 KB runs code
- scripts/fetch_history_fallback.py 53 KB runs code
- scripts/fetch_history.py 2.2 KB runs code
- scripts/fetch_industry.py 4.2 KB runs code
- scripts/fetch_market_details.py 16 KB runs code
- scripts/fetch_realtime.py 52 KB runs code
- scripts/fetch_sector_info.py 13 KB runs code
- scripts/fetch_stock_concepts.py 9.2 KB runs code
- scripts/fetch_stock_events.py 24 KB runs code
- scripts/fetch_technical.py 8.6 KB runs code
- scripts/reference_common.py 4.3 KB runs code
- scripts/test_data_contracts.py 3.7 KB runs code
- scripts/test_data_regressions.py 12 KB runs code
- scripts/test_fetch_realtime_intraday.py 1.5 KB runs code
- scripts/test_reference_data.py 14 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago Changed · -62 lines · -4 tokens per session c17dbcd2e085
- 1mo ago First seen · 157 lines · 97 tokens per session scan A 0645411d14e8
a-share-data is a skill published in the GitHub repository shouldnotappearcalm/a-share-skill (249 stars, last pushed 11d ago), licensed MIT. It adds 93 tokens to every session and 2,349 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
china-dcf-model
A documented discounted-cash-flow model for valuing non-financial A-share companies. A discounted-cash-flow model estimates today’s value from expected future cash flows and makes its assumptions, valuation date, and adjustments explicit.
pptx-author
Produce a .pptx file on disk for headless Chinese A-share market research, using python-pptx with Chinese font support. Use when no controllable Office session is available.
industry-etf-research
A research workflow for studying Chinese A-share industry ETFs and the indexes they track. It compares index rules, holdings, company fundamentals, industry conditions, valuations and market evidence.
china-market-data
A routing and quality-check process for obtaining Chinese stock-market and company-financial data from sources such as Tushare Pro and AKShare.
a-share-factor-validation
A research checklist and toolkit for testing stock-selection factors and backtests in China's A-share market, the mainland Chinese stock market. It checks whether results avoid future-data leakage and account for trading rules, costs, and statistical uncertainty.
china-market-researcher
A research workflow for analysing Chinese-listed stocks by industry or investment theme, including companies, competitors, ETFs, and market data. A-share means shares listed on mainland Chinese stock exchanges.