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 sec_filing_question_answer_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/sec_filing_question_answer_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_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/sec_filing_question_answer_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_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 | $0.00053 | $0.01038 |
| Opus 5.5 | $0.00021 | $0.00415 |
| Sonnet 5.5 | $0.00011 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
sec_filing_question_answer_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
SEC Filing Question Answer 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
本技能用于针对监管文件中的具体问题做精准答疑,重点回答“文件里到底怎么写、哪一段最相关、直接结论是什么、哪些属于明确表述、哪些只是合理推断”。输出重点不是写整份文件摘要,而是针对用户问题给出可追溯、可核验、边界清晰的回答。
执行流程
Step 1:明确问题、对象与文件范围
先确认用户到底想问什么,并锁定对应公司、文件类型和时间范围。优先判断:
- 问题是关于业务、风险、会计处理、治理结构还是资本安排
- 需要查看的是年报、季报、招股书、公告附件还是其他正式文件
- 用户需要的是一句话答案,还是带背景的完整解释
若问题范围过大,应先收缩到一个清晰、可回答的问题。
Step 2:定位与问题最相关的文件段落
围绕用户问题优先定位最可能出现答案的章节,例如:
- 业务概述、风险因素、管理层讨论与分析
- 财务附注、会计政策、客户集中度、合同负债
- 股本结构、锁定期、关联交易、诉讼或监管事项
不要把整份文件平均浏览,而应沿着问题导向查找最相关部分。
Step 3:区分直接答案与支持证据
在找到相关内容后,先判断是否存在明确表述:
- 若文件有直接回答,应优先给出直接结论
- 若文件只提供部分事实,应据此做有限推断
- 若文件没有足够信息,应明确说无法从当前文件确认
任何推断都必须建立在文件中的具体信息之上,不能把猜测写成事实。
Step 4:补充必要上下文
监管文件中的文字往往高度法律化或会计化。为帮助使用者真正理解,应把关键内容翻译成研究语言:
- 这段表述在商业上意味着什么
- 对经营、财务或股东利益的影响是什么
- 与市场通常关心的问题如何对应
- 哪些细节容易被误读
Step 5:标明边界与仍待确认事项
如果问题仍然存在不确定性,需要明确指出:
- 当前文件没有覆盖到的部分
- 需要结合其他文件或后续公告才能确认的点
- 当前只能得出的保守结论是什么
Step 6:整合成精准答疑成稿
最终输出应围绕用户问题直接作答,不展示大段检索过程,也不把整份文件改写成摘要。
输出结构
{股票名称}监管文件答疑({YYYY-MM-DD})
一、问题
{用户问题}
二、直接答案
{先用 2-4 句话直接回答问题,明确结论边界。}
三、依据要点
- 依据 1:{对应内容与意义}
- 依据 2:{对应内容与意义}
- 依据 3:{对应内容与意义}
四、如何理解
- {商业或财务层面的解释 1}
- {商业或财务层面的解释 2}
五、仍待确认的部分
- {未覆盖点 1}
- {未覆盖点 2}
质量要求
- 必须围绕用户问题作答,不能把答疑写成整份文件综述。
- 先给直接答案,再补依据和解释,避免读者在长文本里找结论。
- 要严格区分文件明确写明的内容与基于文件做出的推断。
- 文件没有覆盖到的信息必须明确说明,不可脑补。
- 输出应帮助使用者更快定位关键条款与研究含义,而不是停留在法律化原文复述。
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 · 112 lines · 53 tokens per session scan A 021bf5a0ea5d
sec_filing_question_answer_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 1,038 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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