sec_filing_question_answer_skill

sec_filing_question_answer_skill is a skill for Claude Code, Codex from chuancyzhang/deepseek-cowork. It costs 53 tokens per session (1,038 once invoked), scanned A, original, Apache-2.0.

A question-answering tool for finding evidence in regulatory filings such as annual reports, quarterly reports, and IPO documents. It uses those long official documents to answer specific questions about a company.

In plain words
What is it for?
It is for quickly reading filings, checking terms, finding operating details, and answering company-research questions.
Why use it?
It saves time when checking details buried in lengthy filings. It helps connect an answer to the relevant source material for verification.

Skill for Claude CodeCodex

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

Good fit It is for quickly reading filings, checking terms, finding operating details, and answering company-research questions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_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 sec_filing_question_answer_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 sec_filing_question_answer_skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_skill/github.svg)](https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/sec_filing_question_answer_skill)
Your own site
<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.

agentmods 80×15 button for sec_filing_question_answer_skill

Your own site · 80×15
<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>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,038 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 $0.00053 $0.01038
Opus 5.5 $0.00021 $0.00415
Sonnet 5.5 $0.00011 $0.00208
Haiku 4.5 $0.00005 $0.00104

Measured 1mo ago against content hash 021bf5a0ea5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

ai_skills/wind-aifinmarket/skills/sec_filing_question_answer_skill/SKILL.md · 112 lines

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

  1. 必须围绕用户问题作答,不能把答疑写成整份文件综述。
  2. 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。
  3. 要严格区分文件明确写明的内容与基于文件做出的推断。
  4. 文件没有覆盖到的信息必须明确说明,不可脑补。
  5. 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。
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 · 112 lines · 53 tokens per session scan A 021bf5a0ea5d

Subscribe to this mod's changes

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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