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 chuancyzhang/deepseek-cowork --skill market_breadth_health_skillgit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/market_breadth_health_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/market_breadth_health_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/market_breadth_health_skill/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/chuancyzhang/deepseek-cowork/market_breadth_health_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/market_breadth_health_skill.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 | $0.00061 | $0.01400 |
| Opus 5.5 | $0.00024 | $0.00560 |
| Sonnet 5.5 | $0.00012 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
market_breadth_health_skill 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 1mo 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.
This is a copy
84% identical to breakout_candidate_finder_skill — 202 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Market Breadth Health Skill
数据依赖与执行前置条件
推荐使用万得 wind-mcp-skill 获取底层数据。执行前先检查本地是否已有 wind-mcp-skill；若没有，提示用户安装：
Cowork 适配：所需子 Skill 已随
wind-aifinmarket固定快照内置，不执行安装或在线更新命令。
技能定位
本技能用于判断指数表现背后的参与广度和上涨质量，回答“指数涨了是真强，还是少数权重硬拉；指数跌了是全面转弱，还是局部结构调整”。输出重点不是复述指数点位变化，而是评估行情健康度、识别抱团与分化，并帮助使用者校准风险暴露。
执行流程
Step 1：确认观察对象与周期
先明确本次广度判断围绕什么展开：
- 观察的是哪个指数或哪类市场
- 是看日内、近一周、近一月，还是更长周期
- 是要解释当前上涨质量，还是判断下跌杀伤范围
- 用户更关心短线参与度还是中期趋势稳健性
若用户未说明，默认以最近一周和最近一个月两个尺度做交叉判断。
Step 2：建立广度观察框架
市场广度不应只看涨跌家数，应从多个角度同步判断：
- 上涨与下跌标的数量分布
- 创新高与创新低的数量变化
- 不同市值、风格和行业的参与程度
- 龙头股、权重股与普通成分股是否同步
- 指数表现与赚钱效应是否一致
这一阶段的目标是确认“指数表现有没有得到更广泛市场的支持”。
Step 3：识别健康扩散、局部抱团或弱反弹
结合广度数据和结构特征，把行情大致归为以下类型之一：
- 健康扩散：多数行业和个股同步改善，指数上行有基础
- 结构抱团：少数权重或龙头推动指数，广度并不跟随
- 弱修复反弹：下跌后短暂修复，但参与范围有限
- 普遍转弱：广度恶化先于或同步于指数回落
分类时要同时看横截面分布与时间延续性，避免只看单日状态。
Step 4：解释广度变化背后的原因
进一步说明当前广度为何呈现这种状态，重点包括：
- 市场是在风险偏好提升下全面扩散，还是避险抱团
- 成长、价值、周期、防御等风格是否轮番承接
- 是否存在宏观事件、政策信号或财报窗口带来的结构性扰动
- 广度恶化是否源于高位拥挤释放、筹码切换或流动性问题
Step 5：判断对指数趋势与仓位的含义
广度分析最终要回到可执行层面，重点回答：
- 当前指数上行是否具有延续性
- 即使指数看似稳定，是否仍需要防范结构性回撤
- 若广度改善，应该提高对哪些方向的关注
- 若广度恶化，风险应优先控制在何处
Step 6：形成市场广度体检成稿
最终输出应帮助使用者快速判断大盘行情“强不强、稳不稳、有没有赚钱效应”。正文只保留广度结论、结构诊断、驱动解释与风险提示。
输出结构
{市场/指数名称}市场广度体检（{YYYY-MM-DD}）
一、30 秒结论
- 指数状态：{强势上行 / 震荡 / 转弱}
- 广度状态：{健康 / 一般 / 偏弱}
- 行情性质：{全面扩散 / 局部抱团 / 弱修复 / 普遍转弱}
- 当前提示：{可积极跟踪 / 保持筛选 / 谨慎对待指数表象}
二、广度观察面板
| 维度 | 当前表现 | 结论 |
|---|---|---|
| 涨跌分布 | {内容} | {判断} |
| 创新高/新低 | {内容} | {判断} |
| 行业参与度 | {内容} | {判断} |
| 权重与普通个股同步性 | {内容} | {判断} |
| 赚钱效应 | {内容} | {判断} |
三、行情结构诊断
- 是否存在抱团：{内容}
- 是否存在扩散：{内容}
- 最明显的分化点：{内容}
四、驱动解释
- 主驱动：{内容}
- 辅助因素：{内容}
- 潜在扰动：{内容}
五、对趋势与仓位的含义
- 趋势延续性判断：{内容}
- 当前主要风险：{内容}
- 操作层面的关注点：{内容}
六、后续观察清单
- {观察点 1}
- {观察点 2}
- {观察点 3}
质量要求
- 广度分析必须体现“指数表现”和“个股参与度”的关系，不能只写一边。
- 要明确判断行情属于健康扩散、局部抱团还是弱修复，提升可读性。
- 广度弱并不必然等于看空，但必须说明风险来自哪里。
- 若不同广度指标给出相反信号，应把冲突点明确写出。
- 输出要能帮助使用者识别大盘真实健康度，而不是停留在指数表面。
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.
- 1mo ago First seen · 134 lines · 61 tokens per session scan A 36d411c1e68d
market_breadth_health_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 1,400 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to breakout_candidate_finder_skill, differing in 202 lines, and is treated as a copy.
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