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 macro_event_market_impact_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/macro_event_market_impact_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/macro_event_market_impact_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/macro_event_market_impact_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/macro_event_market_impact_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/macro_event_market_impact_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 | $0.00063 | $0.01361 |
| Opus 5.5 | $0.00025 | $0.00544 |
| Sonnet 5.5 | $0.00013 | $0.00272 |
| Haiku 4.5 | $0.00006 | $0.00136 |
Grade A, and why
macro_event_market_impact_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.
What it actually says
Macro Event Market Impact 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
本技能用于解读宏观事件对市场的影响，回答“这次数据或会议到底超了什么、影响先落在哪些资产和板块、短线市场为什么这样反应、中期该如何重新理解环境”。输出重点不是复述新闻标题，而是拆解预期差、传导路径与对股票市场的真实含义。
执行流程
Step 1：确认宏观事件、预期基线与时点背景
先明确本次要解读的事件是什么，并建立事件前的基准背景：
- 是通胀、就业、增长、利率还是政策会议
- 事件公布前市场的主流预期是什么
- 当前市场最敏感的变量是通胀、增长、流动性还是政策路径
- 这次事件发生前，市场处于风险偏好上升还是防御状态
若没有预期基线，宏观解读容易流于“数据好或坏”的表面判断。
Step 2：识别本次事件的真正预期差
宏观事件的重点不在数据本身，而在于与市场预期相比改变了什么，重点包括：
- 哪个核心数据点超预期或低于预期
- 市场原本最担心的变量是否被缓解或强化
- 数据内部结构是否比 headline 更重要
- 这次事件是否改变后续政策、流动性或增长叙事
Step 3：拆解影响传导链条
围绕股票市场，说明影响通常如何传导：
- 先影响利率、汇率、商品或风险偏好
- 再影响成长与价值、周期与防御等风格比较
- 进一步影响对特定行业的盈利预期和估值容忍度
- 最后影响市场对未来几周或几个月环境的定价
要把即时价格波动与更慢的叙事重定价分开处理。
Step 4：分析对板块与风格的差异化影响
不同板块对同一宏观事件的敏感度并不相同，需要说明：
- 哪些行业是直接受益方
- 哪些行业更容易承压
- 哪些板块的反应主要来自估值变化，哪些来自基本面变化
- 若市场出现与直觉相反的表现，原因可能是什么
Step 5：区分短线交易反应与中期环境含义
进一步判断：
- 今天的市场反应是合理定价还是情绪放大
- 事件是否足以改变中期市场主线
- 若只是一次扰动，后续哪些数据仍需确认
- 若环境确实切换，风格和仓位应如何调整
Step 6：形成宏观冲击解读成稿
最终输出应让使用者知道这次宏观事件影响了什么、影响有多深、该盯什么后续变量。正文只保留预期差、传导逻辑、差异化影响和跟踪点。
输出结构
{宏观事件名称}市场影响解读（{YYYY-MM-DD}）
一、30 秒结论
- 本次最大预期差：{一句话}
- 对股市的主影响方向：{偏利好 / 偏利空 / 中性偏复杂}
- 最敏感的风格或板块：{内容}
- 中期含义判断：{环境改善 / 环境承压 / 仍待确认}
二、事件事实与预期对比
| 项目 | 公布结果 | 事前预期 | 变化含义 |
|---|---|---|---|
| 核心指标 1 | {内容} | {内容} | {判断} |
| 核心指标 2 | {内容} | {内容} | {判断} |
| 核心指标 3 | {内容} | {内容} | {判断} |
三、影响传导路径
- 第一层传导：{内容}
- 第二层传导：{内容}
- 第三层传导：{内容}
四、板块与风格影响
- 受益方向：{内容}
- 承压方向：{内容}
- 最值得留意的错位反应：{内容}
五、短线与中期判断
- 短线交易反应解读：{内容}
- 中期环境含义：{内容}
- 仍需继续验证的变量：{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
- 宏观解读必须围绕“预期差”展开，而不是只写数据好坏。
- 要把即时市场反应与中期环境重定价分开分析。
- 需体现风格和行业的差异化敏感度，避免给出平均结论。
- 若市场反应与直觉相反，应优先解释原因，而不是忽略。
- 输出要帮助使用者判断事件影响的深浅与持续性，而不是停留在新闻复述。
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 · 129 lines · 63 tokens per session scan A 1b71c593837e
macro_event_market_impact_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 1,361 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-04.
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