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 stock_first_look_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/stock_first_look_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/stock_first_look_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/stock_first_look_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/stock_first_look_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/stock_first_look_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 | $0.00059 | $0.01515 |
| Opus 5.5 | $0.00024 | $0.00606 |
| Sonnet 5.5 | $0.00012 | $0.00303 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
stock_first_look_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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock First Look 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
本技能用于在尽可能短的时间内,帮助使用者对一只陌生股票建立“够用且可讨论”的第一层认知。输出重点不是全面尽调,而是先回答五个问题:公司做什么、为什么值得看、市场在争论什么、当前大致贵不贵、继续研究前该先补哪几块信息。
执行流程
Step 1:确认分析对象与研究上下文
先确认股票名称、代码、上市地与所属行业,并识别用户此时的真实目的:
- 是第一次接触,想快速建立基础认知
- 还是已有关注,只想判断是否值得深入
- 是用于自用判断,还是用于向团队口头汇报
若用户没有给出明确目标,默认按“第一次研究一只股票并判断是否值得继续跟进”的场景展开。
Step 2:提炼公司最核心的基础事实
先把最基础且最必要的信息压缩成短摘要,重点包括:
- 公司主要卖什么产品或服务
- 主要客户是谁
- 主要收入和利润来自哪些业务模块
- 所处产业链位置与行业属性
- 近阶段最重要的经营或市场变化
这一阶段不追求完整历史沿革,只保留理解当前股票所需的最小信息集合。
Step 3:建立“为什么现在值得看”的判断框架
围绕当前时点提炼这只股票的关注理由,常见来源包括:
- 财报、公告、政策、产品、订单、资本运作等事件催化
- 行业景气、主题热度、板块轮动带来的关注提升
- 业绩、估值、市场预期之间出现明显预期差
- 股价位置、交易活跃度或市场讨论度突然变化
若没有明显催化,也要明确说明“目前没有强催化,主要属于观察名单而非优先研究对象”。
Step 4:提炼关键经营变量与基础财务快照
从业务模式出发,抓 3-5 个最重要的经营变量,并用有限的关键指标说明公司当前状态。优先关注:
- 收入、利润、利润率、现金流等基础质量
- 增长速度与增长结构
- 业务分部或核心产品线表现
- 对估值和股价最敏感的单一变量
若部分数据缺失,需明确标注,不用模糊描述替代。
Step 5:给出估值位置与市场分歧的初步判断
结合当前估值水平、历史区间、行业对比和市场情绪,判断公司大致处于:
- 明显被低估、合理定价或偏高定价
- 市场预期保守、均衡或过热
- 估值弹性主要来自业绩兑现、逻辑重估还是情绪扩散
同时简要列出最重要的看多与看空变量,帮助使用者避免一上来就陷入单边叙事。
Step 6:形成“是否继续研究”的落地建议
在基础认知之上,明确回答三个问题:
- 这只股票现阶段值不值得继续看
- 若继续研究,下一步最该补哪几个问题
- 若暂时不继续,原因究竟是信息不足、逻辑不清,还是赔率不佳
最终输出只呈现成稿,不展示中间检索路径或内部草稿。
输出结构
{股票名称}初识个股({YYYY-MM-DD})
一、30 秒结论
- 这家公司做什么:{一句话}
- 现在为什么值得看:{一句话}
- 当前最大争议:{一句话}
- 是否建议继续研究:{继续 / 暂缓 / 仅观察}
二、公司基础认知
| 维度 | 内容 |
|---|---|
| 主营业务 | {一句话概述} |
| 主要客户/市场 | {一句话概述} |
| 产业链位置 | {一句话概述} |
| 核心赚钱方式 | {一句话概述} |
三、当前最重要的 3 个关注点
- 关注点 1:{内容}
- 关注点 2:{内容}
- 关注点 3:{内容}
四、关键经营与财务快照
| 指标 | 当前情况 | 解读 |
|---|---|---|
| {指标1} | {数值或描述} | {说明} |
| {指标2} | {数值或描述} | {说明} |
| {指标3} | {数值或描述} | {说明} |
五、估值与预期差判断
- 估值位置:{偏低 / 合理 / 偏高 + 简述}
- 市场预期:{保守 / 中性 / 偏热 + 简述}
- 可能的重估触发:{事件或变量}
六、多空要点
看多逻辑
- {要点 1}
- {要点 2}
看空逻辑
- {要点 1}
- {要点 2}
七、下一步研究清单
- {最该补的研究问题 1}
- {最该补的研究问题 2}
- {最该补的研究问题 3}
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 · 155 lines · 59 tokens per session scan A be1750341377
stock_first_look_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 1,515 once invoked, about $0.0002 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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