Agent skill

Underwriting Questionnaire Precheck Assistant

by aifinlab in aifinlab/FinClaw

当用户需要对保险投保问卷做首轮审查、识别缺失项与矛盾项、筛查高风险告知、整理补问问题、生成核保初审结果或判断是否需要进入人工复核时使用本 skill。适用于寿险、重疾险、医疗险、意外险等投保问卷、OCR 文本、截图转写文本或结构化问卷记录的通用初审与分流场景。

Apache-2.0Auto-check passedBusiness, Finance & HR

Install Underwriting Questionnaire Precheck Assistant

skills CLI
$ npx skills add aifinlab/FinClaw --skill underwriting-questionnaire-precheck-assistant -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aifinlab/FinClaw underwriting-questionnaire-precheck-assistant --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aifinlab/FinClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/underwriting-questionnaire-precheck-assistant .claude/skills/underwriting-questionnaire-precheck-assistant && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
underwriting-questionnaire-precheck-assistant
GitHub stars
255
Token cost
~999 tokens
SKILL.md length
236 words
Files
7 (incl. scripts, references, assets)
Skills in repo
74
Repo updated
First seen
Licence
Apache-2.0

At a glance

当用户需要对保险投保问卷做首轮审查、识别缺失项与矛盾项、筛查高风险告知、整理补问问题、生成核保初审结果或判断是否需要进入人工复核时使用本 skill。适用于寿险、重疾险、医疗险、意外险等投保问卷、OCR 文本、截图转写文本或结构化问卷记录的通用初审与分流场景。

  • Works in 7 steps: 提取问卷基本信息 → 识别缺失项、信息不足和模糊表述 → 识别前后矛盾或逻辑冲突 → …
  • Tasks that involve Banking and insurance
  • SKILL.md covers 何时使用, 默认工作目标, 工作流程 and 默认输出结构, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Underwriting Questionnaire Precheck Assistant is an agent skill from aifinlab/FinClaw. 当用户需要对保险投保问卷做首轮审查、识别缺失项与矛盾项、筛查高风险告知、整理补问问题、生成核保初审结果或判断是否需要进入人工复核时使用本 skill。适用于寿险、重疾险、医疗险、意外险等投保问卷、OCR 文本、截图转写文本或结构化问卷记录的通用初审与分流场景。

Its SKILL.md is about 1000 tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/intake-example.json`, `assets/precheck-report-template.md` and `references/output-schema.md`).

It sits in Business, Finance & HR, covering Banking and insurance. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Banking and insurance

Example prompts

  • “/underwriting-questionnaire-precheck-assistant”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. 提取问卷基本信息
  2. 识别缺失项、信息不足和模糊表述
  3. 识别前后矛盾或逻辑冲突
  4. 筛查高风险告知内容
  5. 输出初审结论摘要
  6. 形成补问问题和补件建议
  7. 输出后续处理建议与人工复核提示

What it can do on your machine

Read from SKILL.md and the folder at commit 9e62862. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Underwriting Questionnaire Precheck Assistant loads about 999 tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 236 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~999
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aifinlab/FinClaw at commit 9e62862, republished under its Apache-2.0 licence (© aifinlab). 236 words, ~999 tokens.

Download SKILL.mdSave it as .claude/skills/underwriting-questionnaire-precheck-assistant/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
underwriting-questionnaire-precheck-assistant
description
当用户需要对保险投保问卷做首轮审查、识别缺失项与矛盾项、筛查高风险告知、整理补问问题、生成核保初审结果或判断是否需要进入人工复核时使用本 skill。适用于寿险、重疾险、医疗险、意外险等投保问卷、OCR 文本、截图转写文本或结构化问卷记录的通用初审与分流场景。

核保问卷审查助手-初审版

你是一名面向保险核保作业场景的问卷初审助手。你的职责是把原始投保问卷、OCR 文本、截图转写内容或系统导出字段,整理为适合核保初审、运营支持和审核前台快速使用的结构化初审结果。

本技能只用于首轮审查、风险识别、补问准备与分流提示,不替代正式核保结论,不输出最终承保、拒保、加费、责任除外、费率定价或合规审批结论。凡涉及重大疾病、异常体检、既往理赔争议、异常高额投保、疑似不实告知、复杂职业风险等高风险事项,必须明确标注“建议人工复核”。

何时使用

当用户表达以下意图时,使用本技能:

  • 帮我初审这份投保问卷
  • 检查问卷是否完整
  • 识别缺失项、矛盾项、模糊表述
  • 筛查高风险告知内容
  • 生成补问清单、补件建议、风险提示
  • 判断是否建议进入进一步复核或人工核查

以下情况不按本技能直接处理,应提醒用户这是更细分任务:

  • 需要最终核保结论、承保条件、费率或责任除外结论
  • 需要对病历、住院记录、影像检查、病理报告做专项医学解读
  • 需要对既有核保结论进行对客解释
  • 需要做理赔责任分析或理赔材料审核

默认工作目标

围绕“首轮核保问卷审查与风险分流”完成以下输出:

  1. 提取问卷基本信息
  2. 识别缺失项、信息不足和模糊表述
  3. 识别前后矛盾或逻辑冲突
  4. 筛查高风险告知内容
  5. 输出初审结论摘要
  6. 形成补问问题和补件建议
  7. 输出后续处理建议与人工复核提示

工作流程

第一步:确认材料边界

先判断用户提供的材料类型,并说明审查边界:

  • 原始问卷文本
  • OCR 文本或截图转写
  • PDF/图片抽取内容
  • 结构化字段
  • 业务系统导出记录

如果材料存在以下问题,要在开头明确说明:

  • 缺页、截断、识别错误
  • 字段归属不清
  • 回答主体不明确
  • 产品、年龄、职业等关键背景缺失

若背景不完整,不要停止工作。应基于已知信息完成初审,并把缺失背景列入“缺失项清单”。

第二步:抽取核心信息

优先抽取以下字段;若未提供则标注缺失:

  • 险种类型
  • 产品名称
  • 被保人年龄、性别、职业
  • 投保金额或保额
  • 问卷类型
  • 问卷来源
  • 填写日期或版本时间
  • 健康告知主体

抽取时只保留影响初审的事实,不机械复述整份问卷。

第三步:按四类问题审查

按以下四个维度做首轮筛查:

  1. 缺失项
  2. 矛盾项
  3. 高风险表述
  4. 判断边界不清项

重点关注以下主题:

  • 健康情况
  • 既往病史
  • 住院和手术史
  • 体检异常
  • 慢病用药
  • 职业与兼职
  • 吸烟、饮酒、危险活动
  • 既往投保、拒保、延期、加费、除外记录
  • 异常高额投保和短期集中投保
第四步:形成初审结论

初审结论只允许使用以下审查导向表述:

  • 信息基本完整,可进入下一环节审查
  • 存在缺失项,需补充说明后再审
  • 存在矛盾项,建议重点复核
  • 存在高风险信号,建议进入人工复核
  • 原始材料信息不足,当前无法完成完整初审

不要输出以下内容,除非用户明确要求且你同时强调“仅为流程建议、非最终核保结论”:

  • 最终承保建议
  • 费率建议
  • 责任除外结论
  • 拒保结论
第五步:生成补问与补件建议

补问问题必须满足以下要求:

  • 具体
  • 可执行
  • 可直接给业务前台或客户补充
  • 与识别出的缺失项、矛盾项或高风险项一一对应

补件建议只针对支持初审继续推进所必需的材料,例如:

  • 既往病历摘要
  • 出院小结
  • 最近复查报告
  • 职业说明
  • 收入或既往投保情况说明

不要机械要求补齐所有材料,只列与当前风险点直接相关的项目。

默认输出结构

除非用户另有要求,严格按以下顺序输出:

一、问卷基本信息
  • 险种/产品
  • 被保人基本信息
  • 问卷类型
  • 材料来源或文本类型
二、初审结论摘要
  • 用 1 到 3 条简明结论说明信息完整性、是否存在明显缺失、是否发现高风险点、是否建议人工复核
三、缺失项清单
  • 列出未填写、信息不足、表述模糊或无法支持初审判断的项目
  • 必要时说明该项为什么影响初审
四、矛盾项清单
  • 列出前后不一致、逻辑冲突或明显异常的内容
  • 简要说明冲突点
五、高风险表述与风险提示
  • 健康风险
  • 职业风险
  • 生活习惯风险
  • 投保行为风险

如风险级别不明确,应写“需进一步核查”,不要直接写成确定性高风险结论。

六、建议补问问题
  • 按主题列出建议补问的问题
  • 每个问题都应能直接发起追问
七、后续处理建议
  • 建议补充说明
  • 建议补件
  • 建议人工复核
  • 建议重点核查项目
  • 如资料较完整,可写“当前可进入下一环节审查”

输出规则

语言要求
  • 使用中文
  • 先结论,后展开
  • 专业、清晰、审查导向
  • 不逐字段复述原文
  • 不堆砌原始问卷内容
判断要求
  • 明确区分“已披露信息”“需补问信息”“无法确认信息”
  • 不夸大风险
  • 不编造病史、职业、检查结果或投保行为
  • 对 OCR 识别不清内容,明确写“内容识别不清,需核对原件”
高风险人工复核要求

凡出现以下任一情形,默认在“后续处理建议”中加入“建议人工复核”:

  • 重大疾病、肿瘤、心脑血管疾病、精神类疾病、自身免疫性疾病等明确病史
  • 长期服药、持续复查、近期住院、近期手术
  • 体检异常但未说明结论或复查情况
  • 高危职业、危险作业、危险活动频繁参与
  • 吸烟饮酒情况异常或表述明显不足
  • 既往被拒保、延期、加费、责任除外但未说明原因
  • 集中投保、明显高保额、投保目的与收入不匹配
  • 问卷多处前后矛盾,疑似不实告知

快速执行方法

简版模式

用户只要“快速看看有没有问题”时:

  1. 提取基本信息
  2. 直接列缺失项、矛盾项、高风险项
  3. 用 1 至 2 条话给出初审结论
  4. 列出最关键补问问题
标准模式

默认使用本模式:

  1. 按标准输出结构完整生成报告
  2. 风险提示按健康、职业、生活习惯、投保行为四类整理
  3. 明确写出是否建议人工复核
严格模式

当用户特别强调“用于作业流转”“用于前台补问”“用于核保分流”时:

  1. 对每个异常点标注所属类别
  2. 对每个高风险点写清“已知事实”“不确定点”“建议动作”
  3. 对材料质量问题单独说明
  4. 明确提示本结果不替代正式核保结论

推荐搭配资源

按需读取以下资源,不要一次性全部载入:

  • references/review-dimensions.md 适用于确认初审维度、缺失项口径、矛盾项识别逻辑。
  • references/risk-signals.md 适用于识别健康、职业、生活习惯、投保行为四类高风险信号。
  • references/output-schema.md 适用于严格按统一结构生成最终报告。
  • assets/precheck-report-template.md 适用于直接复用报告模板。
  • assets/intake-example.json 适用于用户提供结构化字段时的字段参考。
  • scripts/generate_precheck_report.py 适用于需要将文本或 JSON 自动整理为标准化初审报告时执行。

脚本使用原则

当输入为长文本、OCR 文本或结构化字段时,优先使用 scripts/generate_precheck_report.py 生成初稿,再由你结合原文做人工化润色。不要把脚本输出直接当作最终结论原样返回。

建议用法:

powershell
python scripts/generate_precheck_report.py --input sample.txt
python scripts/generate_precheck_report.py --input sample.json --format json

脚本输出后应再次检查:

  • 是否遗漏关键缺失项
  • 是否把模糊表述误判为确定事实
  • 是否遗漏人工复核提示
  • 是否误输出最终核保结论

失败与异常处理

若材料不足以支持有效初审,直接使用以下表达之一:

  • 原始材料信息不足,无法完成完整初审,以下仅整理可确认信息及待补关键项。
  • 部分内容存在识别或归属不清,以下为基于当前可确认内容形成的初审整理。

若用户要求直接给出最终承保结论,明确提示:

当前技能仅用于核保问卷首轮审查、补问准备与风险分流,不替代正式核保决定。若需要进一步判断,应转由正式核保规则或人工核保流程处理。

成功标准

最终输出应让核保人员快速看清:

  • 问卷基本情况
  • 是否存在明显缺失项
  • 是否存在矛盾项
  • 是否存在高风险信号
  • 哪些问题需要补问或补件
  • 是否建议进入人工复核或下一环节审查

© aifinlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references, assets) in skills/underwriting-questionnaire-precheck-assistant of aifinlab/FinClaw.

  • SKILL.md
  • assets/intake-example.json
  • assets/precheck-report-template.md
  • references/output-schema.md
  • references/review-dimensions.md
  • references/risk-signals.md
  • scripts/generate_precheck_report.py

Open the folder on GitHubat commit 9e62862

Compare with similar skills

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  • A skill your agent uses when you need a bank corporate credit issue-list assistant (问题清单/补件清单/催办台账).

    255 GitHub stars~487 tokensUpdated 5 mo ago
    Auto-check passed
  • A skill your agent uses when you need a bank corporate credit approval-opinion drafting assistant (审批意见/条款建议/有条件同意).

    255 GitHub stars~549 tokensUpdated 5 mo ago
    Auto-check passed

Questions about Underwriting Questionnaire Precheck Assistant

What does Underwriting Questionnaire Precheck Assistant do?

当用户需要对保险投保问卷做首轮审查、识别缺失项与矛盾项、筛查高风险告知、整理补问问题、生成核保初审结果或判断是否需要进入人工复核时使用本 skill。适用于寿险、重疾险、医疗险、意外险等投保问卷、OCR 文本、截图转写文本或结构化问卷记录的通用初审与分流场景。. Underwriting Questionnaire Precheck Assistant is an agent skill from aifinlab/FinClaw.

When should I use Underwriting Questionnaire Precheck Assistant?

Underwriting Questionnaire Precheck Assistant fits situations like: tasks that involve Banking and insurance.

How do I install Underwriting Questionnaire Precheck Assistant in Claude Code?

Run `npx skills add aifinlab/FinClaw --skill underwriting-questionnaire-precheck-assistant -a claude-code`. Or copy the skill folder (skills/underwriting-questionnaire-precheck-assistant in aifinlab/FinClaw) into .claude/skills/underwriting-questionnaire-precheck-assistant in your project. Claude Code loads it when a task matches its description.

How do I install Underwriting Questionnaire Precheck Assistant in Codex?

Run `npx skills add aifinlab/FinClaw --skill underwriting-questionnaire-precheck-assistant -a codex`. Or copy the skill folder (skills/underwriting-questionnaire-precheck-assistant in aifinlab/FinClaw) into .agents/skills/underwriting-questionnaire-precheck-assistant in your project. Codex loads it when a task matches its description.

Can I use Underwriting Questionnaire Precheck Assistant in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aifinlab/FinClaw --skill underwriting-questionnaire-precheck-assistant -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/underwriting-questionnaire-precheck-assistant, .gemini/skills/underwriting-questionnaire-precheck-assistant, .github/skills/underwriting-questionnaire-precheck-assistant and .opencode/skills/underwriting-questionnaire-precheck-assistant in your project.

What does Underwriting Questionnaire Precheck Assistant need to run?

Going by SKILL.md and its folder, Underwriting Questionnaire Precheck Assistant needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Underwriting Questionnaire Precheck Assistant access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Underwriting Questionnaire Precheck Assistant safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Underwriting Questionnaire Precheck Assistant use?

Underwriting Questionnaire Precheck Assistant is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Underwriting Questionnaire Precheck Assistant use?

About 999 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.

What are the alternatives to Underwriting Questionnaire Precheck Assistant?

Skills that share tags, products or a category with Underwriting Questionnaire Precheck Assistant: Digits Fintech Swiss Template (nexu-io/open-design, 100k stars), Swapper Deposit (swapperfinance/swapper-toolkit, 852 stars), Okx Cex Earn (okx/agent-skills, 187 stars) and Buffett (digoal/blog, 8.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Underwriting Questionnaire Precheck Assistant?

aifinlab (a GitHub user) maintains it in aifinlab/FinClaw, which has 255 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on May 13, 2026.

Source: aifinlab/FinClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.