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 policy_headline_interpreter_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/policy_headline_interpreter_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/policy_headline_interpreter_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/policy_headline_interpreter_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/policy_headline_interpreter_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/policy_headline_interpreter_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 | $0.01319 |
| Opus 5.5 | $0.00025 | $0.00528 |
| Sonnet 5.5 | $0.00013 | $0.00264 |
| Haiku 4.5 | $0.00006 | $0.00132 |
Grade A, and why
policy_headline_interpreter_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
88% identical to moat_strength_review_skill — 157 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
Policy Headline Interpreter 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
本技能用于解读政策头条对市场的影响,回答“这条政策新闻是真的增量信号,还是旧闻重提;影响会先落在哪些行业和公司;从新闻到落地之间还有哪些不确定性”。输出重点不是把政策原文转述一遍,而是帮助使用者在最短时间内完成政策定性、受益映射和风险判断。
执行流程
Step 1:确认政策来源、级别与状态
先把政策消息本身界定清楚:
- 来源是正式文件、会议表述、权威媒体还是传闻型信息
- 是顶层方向性表态,还是具体执行规则
- 当前处于征求意见、正式发布、执行推进还是口径澄清阶段
- 信息是否为新增,还是市场已经交易过的内容
若来源和状态不清晰,必须先把可信度和正式程度说明白。
Step 2:拆解政策目标与关键约束
政策解读不能只看表面提法,还要回答:
- 政策试图解决什么问题
- 受益方向是需求刺激、供给约束、融资改善还是监管放松
- 执行中最重要的门槛、节奏和约束条件是什么
- 哪些表述是原则性信号,哪些才是实质性变化
Step 3:映射受影响的行业、主题与公司
在目标明确后,分层映射影响范围:
- 直接受益行业
- 间接受益或边际受益行业
- 可能受约束或受损的行业
- 核心受益公司与边缘映射公司
映射时要区分“政策直接触达”和“市场情绪外溢”,不能混为一谈。
Step 4:分析影响路径与兑现节奏
进一步说明这条政策为何影响市场,以及影响如何逐步兑现:
- 是先影响预期,再影响订单、投资、价格或盈利
- 哪些影响可以短期体现,哪些需要更长时间验证
- 市场当前更可能交易政策想象,还是现实兑现
- 若后续执行力度不足,哪些方向最容易回吐
Step 5:评估交易价值与误判风险
政策消息容易被过度放大,因此要重点指出:
- 哪部分影响已经被市场提前反映
- 哪些受益逻辑证据较强,哪些只是概念扩散
- 最容易出现的误判是把口号当结果、把边缘映射当核心受益
- 后续最关键的验证节点是什么
Step 6:形成政策头条解读成稿
最终输出应帮助使用者快速完成“政策定性 + 影响映射 + 节奏判断 + 风险控制”。正文只保留事实、影响链条、受益排序和后续验证点。
输出结构
{政策主题}头条解读({YYYY-MM-DD})
一、30 秒结论
- 政策性质:{方向性信号 / 实质性增量 / 旧闻重提 / 噪音扰动}
- 直接受益方向:{内容}
- 最大不确定性:{内容}
- 当前建议:{重点跟踪 / 谨慎乐观 / 等待细则 / 避免泛化}
二、政策事实卡片
| 项目 | 内容 |
|---|---|
| 来源 | {内容} |
| 级别 | {内容} |
| 状态 | {内容} |
| 关键表述 | {内容} |
| 是否新增 | {是 / 否 + 说明} |
三、影响映射
- 直接受益行业/主题:{内容}
- 间接受益方向:{内容}
- 可能受压制方向:{内容}
- 核心受益公司 vs 边缘映射公司:{内容}
四、影响路径与兑现节奏
- 短期影响:{内容}
- 中期影响:{内容}
- 兑现所需条件:{内容}
五、误判风险与验证节点
- 最容易被误判的点:{内容}
- 后续关键验证:{内容}
- 可能推翻当前判断的因素:{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
- 政策解读必须先确认来源、级别和状态,不能把所有消息同等对待。
- 要区分正式增量政策、方向性表态与市场噪音。
- 影响映射必须区分核心受益和边缘映射,避免概念扩散失控。
- 需要明确执行约束和兑现节奏,防止把政策口号直接写成业绩结果。
- 输出要帮助使用者完成快速政策定性与后续验证,而不是改写新闻稿。
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 · 132 lines · 63 tokens per session scan A e4353c60fbff
policy_headline_interpreter_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 1,319 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to moat_strength_review_skill, differing in 157 lines, and is treated as a copy.
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