stock_research_memo_writer_skill

stock_research_memo_writer_skill is a skill for Claude Code, Codex from chuancyzhang/deepseek-cowork. It costs 66 tokens per session (1,200 once invoked), scanned A, original, Apache-2.0.

A structured writer for company research memos. It organizes an investment thesis, disagreements, valuation view, risks, and items to monitor into a shareable record.

In plain words
What is it for?
It is for writing stock research summaries, recording investment conclusions, and maintaining follow-up checklists.
Why use it?
It removes the need to turn scattered research notes into a consistent document by hand. It also makes the reasoning easier to save and share with a team.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for writing stock research summaries, recording investment conclusions, and maintaining follow-up checklists.

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Install with agentmods
npx agentmods add skills/chuancyzhang/deepseek-cowork/stock_research_memo_writer_skill
Install

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.

Any agent
npx skills add chuancyzhang/deepseek-cowork --skill stock_research_memo_writer_skill
Clone the repo
git clone --depth 1 https://github.com/chuancyzhang/deepseek-cowork

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for stock_research_memo_writer_skill

README.md
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Your own site
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<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/stock_research_memo_writer_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/stock_research_memo_writer_skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,200 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00066 $0.01200
Opus 5.5 $0.00026 $0.00480
Sonnet 5.5 $0.00013 $0.00240
Haiku 4.5 $0.00007 $0.00120

Measured 1mo ago against content hash fdd7f2905637, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

stock_research_memo_writer_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.

ai_skills/wind-aifinmarket/skills/stock_research_memo_writer_skill/SKILL.md · 143 lines

What it actually says

Stock Research Memo Writer 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

本技能用于把分散的个股研究结果整理成一份结构清晰、便于分享和复用的研究备忘录。输出重点不是单纯罗列资料,而是沉淀“结论、依据、分歧、风险、后续跟踪”这五个最关键部分,让团队成员能快速接手和继续推进研究。

执行流程

Step 1:确认备忘录用途与深度

先明确这份备忘录是用于:

  • 首次研究后的阶段性总结
  • 晨会、周会或内部分享
  • 为后续深挖建立统一底稿

若用户未指定,默认按“供团队内部快速理解并继续跟踪”的标准撰写。

Step 2:提炼核心投资结论

先把最想表达的结论压缩成几句清楚的话:

  • 为什么值得关注
  • 当前最大机会来自哪里
  • 当前最大风险是什么
  • 现阶段更适合行动、观察还是继续研究

结论部分应先于材料堆叠出现。

Step 3:整理支撑结论的事实与逻辑

围绕结论抽取真正重要的依据,包括:

  • 业务模式与核心变量
  • 经营与财务表现
  • 行业与竞争位置
  • 市场预期与估值位置

不要把所有搜到的内容全部塞进备忘录,只保留能支撑结论的关键材料。

Step 4:把核心分歧写清楚

一份有用的研究备忘录,必须说明:

  • 市场为什么会同意这个观点
  • 市场为什么可能不同意
  • 哪个变量最可能让结论被证伪

这一步能让备忘录真正具备讨论价值,而不是单向陈述。

Step 5:补齐风险与跟踪清单

除了结论本身,还要列出:

  • 当前结论最依赖的假设
  • 需要持续跟踪的事件与指标
  • 哪些信号一旦出现,应当更新或撤销观点

这样备忘录才能从“静态总结”升级为“动态研究底稿”。

Step 6:整理成标准化成稿

最终以统一结构输出,保证不同个股之间的备忘录可以横向比较、持续更新、团队复用。正文只呈现成稿,不展示内部整理过程。

输出结构


{股票名称}研究备忘录({YYYY-MM-DD})

一、结论摘要

  • 当前结论:{看多 / 中性 / 谨慎 / 继续观察}
  • 一句话理由:{说明}
  • 最重要的验证变量:{变量}

二、公司与逻辑概览

  • 公司在做什么:{一句话}
  • 当前值得看的逻辑:{一句话}
  • 最主要的争议点:{一句话}

三、支撑依据

维度 关键内容 对结论的意义
业务 {描述} {说明}
财务 {描述} {说明}
行业/竞争 {描述} {说明}
估值 {描述} {说明}

四、多空分歧

看多理由

  • {理由 1}
  • {理由 2}

看空理由

  • {理由 1}
  • {理由 2}

五、风险与失效条件

  • {风险 1}
  • {风险 2}
  • {结论失效条件}

六、后续跟踪清单

  • {跟踪项 1}
  • {跟踪项 2}
  • {跟踪项 3}

七、当前动作建议

  • 建议动作:{继续跟踪 / 深入研究 / 等待验证 / 暂不行动}
  • 原因:{说明}

质量要求

  1. 备忘录必须先给结论,再给依据,不能让读者自己从材料堆里猜结论。
  2. 所有内容都应服务后续复用,避免写成一次性聊天记录。
  3. 多空分歧与风险不能省略,否则备忘录缺乏讨论价值。
  4. 若数据或信息有缺口,应在跟踪清单中明确补齐方向。
  5. 文本风格应压缩、清晰、可传递,适合内部分享与持续更新。
Changes

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.

  1. 1mo ago First seen · 143 lines · 66 tokens per session scan A fdd7f2905637

Subscribe to this mod's changes

stock_research_memo_writer_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 1,200 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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