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 agentmods add skills/shouldnotappearcalm/a-share-skill/a-share-datanpx 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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00097 | $0.02470 |
| Opus 5 | $0.00048 | $0.01235 |
| Sonnet 5 | $0.00019 | $0.00494 |
| Haiku 4.5 | $0.00010 | $0.00247 |
Grade A, and why
a-share-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A股数据综合分析
目标
使用本技能时,优先调用本目录下脚本获取结构化数据,不依赖网页抓取。
支持能力:
- 实时行情与市场维度
- 历史数据与财务维度
- 技术指标
- 个股事件
- A+H 双重上市公司列表(支持按 H 股上市日期筛选)
- A股赴港上市关键事件时间节点(递表/聆讯/备案/招股/定价/配售/上市)
- 热门行业、热门概念、行业/概念涨跌幅、板块热力图、板块成分股、7×24 市场快讯(
fetch_danginvest.py;先读references/danginvest-api-reference.md) - 个股行业信息(
fetch_sector_info.py,数据源东方财富;个股概念不稳定,见下)
环境与路径
pip install akshare MyTT pandas numpy requests
SKILL_DIR="<本skill绝对路径>"
python3 "$SKILL_DIR/scripts/fetch_realtime.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_history.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_technical.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_stock_events.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_ah_stocks.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_ah_ipo_timeline.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_danginvest.py" [参数]
python3 "$SKILL_DIR/scripts/fetch_sector_info.py" [参数]
说明:fetch_sector_info.py 虽可能带概念参数,但东方财富个股概念接口不稳定、常为空,使用时固定加 --no-concepts,只查行业与证券简称。市场级概念板块(涨跌幅、热力图、成分股)走 fetch_danginvest.py,不要与前者混用。
代码格式约定
优先使用以下股票代码格式:
- 纯数字:
600519 - 市场前缀:
sh600519/sz000001 - JoinQuant:
600519.XSHG
脚本路由规则
按问题类型选脚本:
fetch_danginvest.py:热门概念、热门行业、行业涨跌幅、概念涨跌幅、板块热力图、板块成分股、7×24 市场快讯;参数与查询惯例见references/danginvest-api-reference.mdfetch_realtime.py:实时价格、分钟线、指数、北向、龙虎榜、涨跌停、资金流、全市场行情、成交明细(--boards-*仅兼容旧用法)fetch_history.py:历史K线、财务、业绩、分红、行业、指数成分、交易日历、宏观fetch_technical.py:MA/MACD/KDJ/RSI/BOLL等技术指标fetch_stock_events.py:业绩、增减持/回购、监管、重大合同、舆情方向fetch_ah_stocks.py:A+H 双重上市公司清单、H 股上市日期区间筛选fetch_ah_ipo_timeline.py:A股赴港上市关键事件节点(递表/聆讯/备案/招股/定价/配售/上市);支持--code/--name点查fetch_sector_info.py:单只或多只股票的行业与名称(东方财富);批量时并行,默认--workers;仅文档化行业路径,不加概念
执行流程
- 先识别用户意图:实时、历史、技术、事件、A股赴港上市时间节点、热门行业/热门概念/行业或概念涨跌幅、板块热力图、7×24 快讯,或「个股所属行业」。
- 命中下列任一表述时,先读
references/danginvest-api-reference.md,再用fetch_danginvest.py(勿用fetch_realtime.py --boards-*):- 今天/今日热门概念、什么概念涨得多、概念领涨/领跌
- 今天/今日热门行业、什么行业涨得多、行业领涨/领跌(含大类行业、细分行业)
- 行业涨跌幅、概念涨跌幅、板块热力图、某板块成分股
- 7×24 市场快讯
- 选择对应脚本并优先加
--json。 - 参数不足时补齐默认值后执行,不先空谈。
- 返回时给出关键字段结论,并附可复现命令。
What ships with it
13 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 9.3 KB
- references/danginvest-api-reference.md 6.0 KB
- scripts/Ashare.py 5.5 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_danginvest.py 12 KB runs code
- scripts/fetch_history_fallback.py 42 KB runs code
- scripts/fetch_history.py 2.1 KB runs code
- scripts/fetch_realtime.py 50 KB runs code
- scripts/fetch_sector_info.py 13 KB runs code
- scripts/fetch_stock_events.py 26 KB runs code
- scripts/fetch_technical.py 8.4 KB runs code
- scripts/test_fetch_realtime_intraday.py 1.5 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.
- 6d 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 (235 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 2,470 once invoked, about $0.0005 per session on Opus 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.
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pptx-author
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china-market-data
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china-market-researcher
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