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_sentiment_temperature_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_sentiment_temperature_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/market_sentiment_temperature_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/market_sentiment_temperature_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_sentiment_temperature_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/market_sentiment_temperature_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 | $0.00060 | $0.01380 |
| Opus 5.5 | $0.00024 | $0.00552 |
| Sonnet 5.5 | $0.00012 | $0.00276 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
market_sentiment_temperature_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
86% identical to buyback_program_reviewer_skill — 149 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 Sentiment Temperature 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
本技能用于量化市场情绪与风险偏好，回答“现在市场是热是冷、热在哪里、冷在哪里、风险偏好是否过度、接下来更应顺势还是降温”。输出重点不是把情绪形容成笼统的乐观或悲观，而是给出情绪温度、拥挤位置、风险释放路径和仓位层面的启发。
执行流程
Step 1：确认市场范围、观察窗口与使用目标
先确认本次情绪判断覆盖的对象和用途：
- 观察哪个市场或哪类股票池
- 是日内情绪判断、周度情绪复盘，还是阶段性温度评估
- 是为了决定总仓位，还是为了判断题材是否拥挤
- 用户更关注追涨风险还是抄底时机
若用户未说明，默认按“当前市场的短中期情绪温度”展开。
Step 2：建立情绪温度的多维指标框架
情绪判断不能只靠一句“市场很热”，需要从多个维度拆解：
- 风险偏好是否明显上升或下降
- 强势股、热门题材和高弹性标的表现是否突出
- 市场参与热情是否从核心扩散到边缘
- 分歧日是否仍有强承接，或情绪是否一碰就散
- 恐慌释放是否已经充分，还是仍在扩散
这一阶段的目标是确定情绪不只是“有没有”，而是“在哪些层面表现出来”。
Step 3：区分热度、拥挤度与脆弱度
市场热并不等于危险，冷也不等于安全，因此要把三个层次拆开：
- 热度：市场参与意愿是否增强
- 拥挤度：同一方向的交易是否过于集中
- 脆弱度：一旦出现负面扰动，是否容易出现快速回撤
只有把这三个层次区分开，才能避免用单一情绪词覆盖复杂状态。
Step 4：判断情绪所处阶段
结合多维信号，将市场情绪大致归为：
- 低温修复
- 中性均衡
- 高温强化
- 极热拥挤
- 快速降温
- 恐慌释放
并说明这个阶段为何成立，重点证据来自哪里。
Step 5：分析情绪与价格结构是否一致
进一步回答：
- 情绪升温是否伴随结构改善
- 情绪高涨是否已经超过基本逻辑承载能力
- 情绪很弱时，是否反而在孕育修复窗口
- 当前更适合顺势参与、等待分歧，还是优先控制暴露
Step 6：形成情绪温度成稿
最终输出应让使用者迅速把握市场温度、拥挤位置和节奏风险。正文只保留温度判断、阶段说明、脆弱点和行动提示。
输出结构
{市场范围}情绪温度计（{YYYY-MM-DD}）
一、30 秒结论
- 当前温度：{低温 / 中性 / 升温 / 高温 / 极热}
- 风险偏好状态：{回升 / 稳定 / 降低}
- 拥挤度：{低 / 中 / 高}
- 当前建议：{顺势跟踪 / 等待分歧 / 控制追高 / 防恐慌扩散}
二、情绪观察面板
| 维度 | 当前状态 | 判断 |
|---|---|---|
| 风险偏好 | {内容} | {判断} |
| 强势股表现 | {内容} | {判断} |
| 热点扩散 | {内容} | {判断} |
| 分歧承接 | {内容} | {判断} |
| 恐慌释放 | {内容} | {判断} |
三、情绪阶段判断
- 当前阶段：{内容}
- 阶段依据：{内容}
- 最容易被误判的点：{内容}
四、拥挤度与脆弱点
- 拥挤位置：{内容}
- 脆弱环节：{内容}
- 可能的风险触发器：{内容}
五、节奏含义
- 适合提高关注的方向：{内容}
- 适合降低暴露的方向：{内容}
- 总仓位层面的启发：{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
- 情绪分析必须区分热度、拥挤度和脆弱度，避免混成单一判断。
- 要说明情绪状态的证据来源，不能只给情绪形容词。
- 需把情绪与价格结构结合起来看，防止“情绪高”就简单等同风险。
- 若市场存在明显分化，应写出热在哪、冷在哪，而不是给整体平均结论。
- 输出要服务仓位节奏判断，而不是停留在气氛描述。
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 · 137 lines · 60 tokens per session scan A 15cd8e9875f4
market_sentiment_temperature_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 1,380 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to buyback_program_reviewer_skill, differing in 149 lines, and is treated as a copy.
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