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_heat_tracker_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_heat_tracker_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/theme_heat_tracker_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/theme_heat_tracker_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_heat_tracker_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/theme_heat_tracker_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 | $0.01485 |
| Opus 5.5 | $0.00024 | $0.00594 |
| Sonnet 5.5 | $0.00012 | $0.00297 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
theme_heat_tracker_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
78% identical to breakout_candidate_finder_skill — 203 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theme Heat Tracker 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
本技能用于跟踪某一主题题材的热度变化，回答“这条题材现在热不热、热度来自哪里、是龙头驱动还是全面扩散、是否已经开始拥挤或退潮”。输出重点不是简单罗列相关新闻，而是帮助使用者判断题材所处阶段，以及应继续跟踪、择机参与还是降低关注。
执行流程
Step 1：界定题材边界与观察周期
先明确本次要跟踪的主题是什么，并把边界定义清楚：
- 题材名称对应的核心叙事是什么
- 哪些公司属于核心受益，哪些只是边缘映射
- 是观察单一题材，还是比较多个相近题材
- 是看日内热度、周度热度，还是阶段性热度演化
若题材边界模糊，应先做核心名单与边缘名单的区分，避免把无关标的混入热度判断。
Step 2：建立题材热度的多维观察框架
热度不应只看价格涨跌，需要从多个维度综合判断：
- 题材核心标的与跟随标的的表现强弱
- 题材内部上涨家数、持续性和梯队完整度
- 成交活跃度是否同步上升
- 新闻、政策、产业动态是否持续提供新催化
- 市场讨论热度是否集中且不断强化
这一阶段的目标是确认热度是否真实存在，而不是仅凭少数龙头表现下结论。
Step 3：判断题材所处阶段
结合市场表现和信息流变化，判断题材当前更接近哪一阶段：
- 启动阶段：少数先行标的率先异动，市场开始形成关注
- 强化阶段：核心标的持续走强，逻辑得到重复验证
- 扩散阶段：中军、二线、边缘映射标的普遍受益
- 拥挤阶段：交易过热、分支泛化、兑现压力升高
- 退潮阶段：题材热度减弱，仅剩个别强股维持表现
阶段判断必须有清晰依据，不能只使用情绪化表述。
Step 4：识别热度来源与延续条件
进一步说明题材之所以热，是因为什么，以及要继续热下去还需要什么条件：
- 是政策刺激、产业进展、业绩兑现还是市场风格配合
- 当前催化是一次性事件，还是可连续验证的主线
- 热度是否已经从叙事走向业绩支持
- 若后续没有新增催化，热度是否容易快速回落
Step 5：判断拥挤度与风险释放可能
热题材常见问题不是“有没有逻辑”，而是“是不是已经太拥挤”。重点说明：
- 核心标的是否已有明显抢跑
- 边缘标的是否被过度外延
- 题材涨幅和成交是否出现脱离基本逻辑的扩张
- 一旦情绪回落，最先承压的环节会在哪里
Step 6：形成题材热度跟踪成稿
最终输出应帮助使用者快速判断题材是值得继续跟踪、等待分歧、做高低切换，还是暂时回避。正文只保留阶段判断、驱动解释、拥挤风险与后续观察点。
输出结构
{题材名称}热度追踪（{YYYY-MM-DD}）
一、30 秒结论
- 当前热度水平：{高 / 中 / 低}
- 所处阶段：{启动 / 强化 / 扩散 / 拥挤 / 退潮}
- 热度来源：{一句话}
- 当前建议：{继续跟踪 / 优先看核心 / 防拥挤 / 暂不追高}
二、题材范围界定
- 核心逻辑：{内容}
- 核心标的：{内容}
- 边缘映射标的：{内容}
三、热度观察面板
| 维度 | 当前状态 | 判断 |
|---|---|---|
| 核心标的强度 | {内容} | {判断} |
| 跟随标的扩散 | {内容} | {判断} |
| 成交活跃度 | {内容} | {判断} |
| 新催化供给 | {内容} | {判断} |
| 市场讨论度 | {内容} | {判断} |
四、热度驱动与延续条件
- 当前最强驱动：{内容}
- 还能继续强化的条件：{内容}
- 最容易失速的原因：{内容}
五、拥挤度与风险提示
- 拥挤迹象：{内容}
- 最先可能退潮的环节：{内容}
- 需要回避的误判：{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
- 题材热度分析必须把“价格表现、扩散层级、催化供给、拥挤程度”放在同一框架下。
- 必须区分核心受益和边缘映射，避免题材外延无限扩大。
- 阶段判断应基于证据，不可仅凭情绪化词语。
- 需要同时写出延续条件和退潮风险，避免只给单边结论。
- 输出要能服务热点跟踪和轮动判断，而不是简单热点摘要。
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 · 135 lines · 61 tokens per session scan A f07c220f0caf
theme_heat_tracker_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 1,485 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 78% identical to breakout_candidate_finder_skill, differing in 203 lines, and is treated as a copy.
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