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 theme_leader_identification_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/theme_leader_identification_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/theme_leader_identification_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/theme_leader_identification_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/theme_leader_identification_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/theme_leader_identification_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 | $0.00070 | $0.01821 |
| Opus 5.5 | $0.00028 | $0.00728 |
| Sonnet 5.5 | $0.00014 | $0.00364 |
| Haiku 4.5 | $0.00007 | $0.00182 |
Grade A, and why
theme_leader_identification_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 — 197 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.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theme Leader Identification 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
本技能用于在热门题材或主题行情中识别谁是真正的龙头、谁是中军、谁只是跟风，以及题材当前处于启动、强化、扩散还是退潮阶段。输出重点不是简单按照涨幅排序，而是判断题材内部地位、带动能力、持续性和交易拥挤度，帮助使用者把注意力聚焦到最有代表性的标的上。
功能简介
本技能的核心功能是：识别热门题材中的龙头、中军和跟随股，判断谁最值得跟踪。
它强调把题材驱动、个股辨识度、成交结构、产业逻辑和资金偏好放在一起判断，帮助使用者更清楚地理解题材内部的角色分层。
使用场景
- 热点题材快速升温后，需要判断谁是最核心的标的。
- 用户希望在题材轮动中避免把跟风股当龙头追逐。
- 团队做盘后复盘，需要整理题材内部地位变化。
- 新题材爆发时，需要快速建立主线与梯队认知。
工作原则
- 龙头识别不能只按短期涨幅排序，必须结合辨识度、带动性与承接质量。
- 要区分龙头、中军、跟随股和掉队股四个层次。
- 题材分析必须说明其所处阶段，避免脱离周期谈地位。
- 若题材逻辑薄弱或已明显退潮，应降低龙头讨论价值。
- 正文中不显式描述内部调用过程。
执行流程
Step 1：确认题材边界与观察窗口
先定义本次要分析的题材范围：
- 是单一明确主题，还是多个相近概念的集合
- 是观察当日热点，还是近一周的持续主线
- 用户想判断的是交易龙头，还是中期主线核心资产
题材边界不清，会直接导致后续角色判断失真。
Step 2：梳理题材驱动与核心叙事
先回答“市场为什么在交易这个题材”。重点拆解：
- 驱动来自政策、产业趋势、景气数据、事件催化还是纯情绪扩散
- 题材是否有真实产业映射，还是概念泛化严重
- 市场目前最关心的是哪一条主叙事
- 这条叙事是否足以支撑题材持续发酵
Step 3：建立题材内股票清单与角色分层
围绕题材内主要股票，先做初步分层：
- 龙头：辨识度最高、带动最强、资金承接最集中
- 中军：容量大、稳定度高、对题材方向有确认作用
- 跟随股：受题材情绪带动，但自主地位较弱
- 掉队股：题材内存在感下降或表现明显落后
这一步的目标是建立“结构图”，而不是立即给出交易建议。
Step 4：检查龙头候选的辨识度与带动性
真正的龙头通常具备几个共同特征：
- 价格表现领先且具有持续性
- 成交活跃，市场讨论度高
- 当其走强时，题材内其他股票会被带动
- 回调时也常成为资金优先回流对象
如果某只股票只是单日涨幅大，但缺乏带动性，应谨慎定义为龙头。
Step 5：结合产业映射与基本面支撑
题材龙头不一定都是基本面最强，但也不能完全脱离业务相关性。要判断：
- 公司与题材主线的相关度是否真实、直接
- 业务、订单、产品或行业位置是否支持其龙头地位
- 题材炒作是否严重超前于基本面
- 若情绪退潮，哪些标的更可能保留溢价
Step 6：判断题材所处阶段与内部演化
龙头识别必须放在题材周期中看。要区分：
- 启动期：谁率先点火、谁提供辨识度
- 强化期：谁最能吸引增量资金
- 扩散期：谁带动中军和跟随股补涨
- 退潮期：谁还能抗跌、谁最先掉队
Step 7：输出优先跟踪名单与风险提醒
根据“题材地位 + 资金承接 + 产业映射 + 阶段位置”综合排序，形成：
- 最值得优先跟踪的龙头
- 值得观察的中军
- 不宜误判为龙头的跟随对象
同时写明：
- 题材继续强化的条件
- 龙头地位可能被替代的情形
- 题材退潮时的风险信号
Step 8：整合成题材龙头成稿
最终输出应帮助使用者知道“这个题材的核心是谁、为什么是它、目前处于什么阶段、后面还要看什么”。正文只保留结构化结论与跟踪框架。
输出结构
题材龙头识别（{YYYY-MM-DD}）
一、30 秒结论
- 当前题材龙头：{名称}
- 当前中军：{名称}
- 题材阶段：{启动 / 强化 / 扩散 / 退潮}
- 最值得跟踪的原因：{一句话}
二、题材结构总览
| 股票 | 题材角色 | 辨识度 | 带动性 | 产业映射 | 当前判断 |
|---|---|---|---|---|---|
| {标的1} | {龙头/中军/跟随/掉队} | {高/中/低} | {高/中/低} | {强/中/弱} | {说明} |
| {标的2} | {角色} | {等级} | {等级} | {等级} | {说明} |
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 · 171 lines · 70 tokens per session scan A 4543f938e9b7
theme_leader_identification_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 1,821 once invoked, about $0.0003 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 197 lines, and is treated as a copy.
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