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 minguncle/a-share-agent-skills --skill a-share-market-sweepgit clone --depth 1 https://github.com/minguncle/a-share-agent-skillsWrote 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/minguncle/a-share-agent-skills/a-share-market-sweep)<a href="https://agentmods.dev/skills/minguncle/a-share-agent-skills/a-share-market-sweep"><img src="https://agentmods.dev/badge/skills/minguncle/a-share-agent-skills/a-share-market-sweep/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/minguncle/a-share-agent-skills/a-share-market-sweep"><img src="https://agentmods.dev/badge/skills/minguncle/a-share-agent-skills/a-share-market-sweep.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00073 | $0.01388 |
| Opus 5 | $0.00036 | $0.00694 |
| Sonnet 5 | $0.00015 | $0.00278 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
a-share-market-sweep 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 11d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A Share Market Sweep
Overview
这个 skill 负责把“今天市场到底在交易什么”重建出来，并形成当次会话专用的全市场临时库。它不依赖旧状态，不把上次扫描结果直接沿用到这次。
Hard Rules
- 先看指数，再看广度，再看板块，最后看个股。
- 用户说“今天”“现在”“刚刚”时，必须抓当日最新数据。
- 任何主线判断都必须至少有三个证据支撑，例如指数结构、板块强度、成交或领涨股联动。
- 允许建“临时全市场库”，但仅限当前响应内使用，不写成长期状态依赖。
- 若指数与情绪不一致，必须单列说明“指数强弱”和“情绪强弱”是分裂还是共振。
When To Use
- 用户问“今天 A 股怎么样”
- 要做盘中复核、收盘复盘、主线扫描
- 要给选股或调仓建议，但还没完成市场底稿
- 要判断某个板块是不是主线或只是脉冲
- 要把宏观或事件观点落到今天盘面上
Workflow
Step 1: 时间与市场状态
- 锚定日期、时间、是否午盘前、午盘后或收盘后
- 明确本次扫描是“盘中结构跟踪”还是“收盘定稿”
Step 2: 指数层
至少覆盖：
- 上证、深成指、创业板、科创或中证核心宽基
- 涨跌幅、成交额、分时强弱
- 指数之间的风格差异
Step 3: 广度与量能层
至少覆盖：
- 上涨家数、下跌家数、涨停、跌停
- 成交额与相对前值变化
- 是放量普涨、缩量轮动、抱团防守还是退潮杀跌
Step 4: 板块层
输出强弱分层：
- 第一梯队主线
- 第二梯队轮动方向
- 明显承压方向
对每个主线至少说明：
- 领涨逻辑
- 资金承接是否稳定
- 是趋势主线、事件脉冲还是超跌反抽
Step 5: 个股层
从板块里抽取：
- 龙头
- 跟风
- 分歧转强候选
- 高位风险样本
Step 6: 建立临时全市场库
把当前会话内的可交易信息归档成临时表：
- 主线板块
- 领涨标的
- 次强板块
- 观察名单
- 明显回避名单
顺序模板见 sweep-order.md。
Step 7: 输出交易框架
给出而不是泛泛评论：
- 今日最该做什么
- 今日最不该做什么
- 适合追涨、低吸、等分歧还是等确认
- 下一步应该衔接哪个 skill
Tool Rules
- 大盘页、板块资金、东财数据中心：优先网页抓取
- 个股与板块定向数据：优先任意可靠的单票或板块行情 API、MCP 或网页数据源
- 网页信息必须落到具体数字、日期和栏目，不要只复述标题
- 若工具之间数据冲突，优先标记时间差异，再说明采用哪一个口径
Output Contract
默认输出：
今日结论关键证据临时全市场库交易框架风险清单观察清单
Practical Notes
- 若市场高度混沌，不强行制造单一主线，直接承认“多脉冲、低持续”
- 若资源、科技、红利同时有表现，要拆成不同风格篮子，不要混写
- 扫描结束后，若用户要处理持仓，优先交给组合校准或动作路由
Inputs & Preconditions
- 至少有一个可访问的当日市场级数据源或网页页签
- 用户若要求“今天/现在/刚刚”，必须按同日数据处理
- 若用户同时要求选股或调仓，应先完成本 skill，再进入后续路由
Dependency Profile
- 必需依赖：一个支持 skill 的 agent 运行环境
- 建议依赖：可访问的大盘页、板块页、资金流页或等效市场数据源
- 可选依赖：单票行情 API/MCP、新闻快讯页、龙虎榜或研报页
- 降级策略：若仅能拿到指数和部分板块数据，则输出部分市场工作包并显式标记
数据缺口
Data Gap Handling
- 缺广度、成交或资金流时，不补猜，用
数据缺口单列说明 - 若不同来源时间戳不一致，先写清采样时间，再给采用口径
- 若只有指数、没有情绪层证据，不把判断升级成“明确主线”
- 不伪造涨跌家数、成交额或板块排名
Risk Language
- 不把单日脉冲描述成长期确定性主线
- 不用收益承诺口径替代证据
- 所有主线判断都应附带不成立条件或持续性风险
Short Disclaimer
本 skill 仅用于投资研究流程、信息整理与教育演示，不构成投资建议、个性化荐股、投顾服务或自动交易指令。
This skill is for research workflow, information organization, and educational use only. It is not investment advice, not personalized recommendation, and not for automated trading.
What ships with it
2 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.
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.
- 11d ago First seen · 150 lines · 73 tokens per session scan A cfcad2d33838
a-share-market-sweep is a skill published in the GitHub repository minguncle/a-share-agent-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 73 tokens to every session and 1,388 once invoked, about $0.0004 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 2026-08-31.
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