表单小助手 · Form Assistant

SkillDocs & knowledge

Fill in Word document form templates (.docx) with user-provided data. Reads a template containing tables with label→value cell pairs, detects all fillable fields, and outputs a completed document. Handles CJK/Latin mixed text with proper font switching. Use this skill when the user wants to fill in a form template, complete an application form, populate a Word table form, or automate document filling. Also trigger when the user mentions "填表", "填写表格", "fill form", "fill template", "表格填写", "申请表", "登记表", or has a .docx template with blank fields to fill.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the 表单小助手 · Form Assistant skill

What this skill tells your AI

The instructions your AI receives, as published by lovstudio/skills in skills/fill-form/SKILL.md and read by ahel’s review.

This skill fills in Word document form templates (.docx) with user-provided data. It detects table-based form fields (label in one cell, value in the adjacent cell) and populates them automatically.

When to Use

  • User has a .docx form template with blank fields to fill
  • User wants to fill in an application form, registration form, etc.
  • Document uses Word tables for form layout (label | value cell pairs)
  • User mentions 填表, 申请表, 登记表, or wants to automate form filling

Workflow (MANDATORY)

You MUST follow these steps in order:

Step 1: Scan the template

Discover all fillable fields:

python lov-fill-form/scripts/fill_form.py --template <path> --scan

Step 2: Pre-fill from known context

Before asking the user, try to fill as many fields as possible from:

  1. User memory — name, title, organization, etc.
  2. Context files — if the user provides reference documents (e.g. STARTER-PROMPT.md, project docs), extract relevant info to fill content-heavy fields
  3. Conversation context — anything already mentioned

For content-heavy fields (e.g. "主要内容/简介/摘要"), actively compose the content by synthesizing from context files, user's known expertise, and the topic/title.

Step 3: Ask only what you don't know

Use AskUserQuestion to collect ONLY the fields you cannot fill from context.

  • Group fields into a single question
  • If ALL fields are unknown, list them all
  • If the user says some fields can be left blank (e.g. "其他朋友会帮我填"), respect that and leave those empty
  • Do NOT force the user to provide every field

Step 4: Fill and save

Write a JSON data file (avoids shell escaping issues with long text), then run:

python lov-fill-form/scripts/fill_form.py \
  --template <path> \
  --data-file /tmp/form_data.json

Output path rules:

  • Default: <template_dir>/<name>_filled.docx (same directory as the template)
  • If the template is in a temp directory or system path, save to user's document directory or ask the user where to save
  • Use --output to override explicitly

CLI Reference

ArgumentDefaultDescription
--template(required)Path to template .doc/.docx file
--output<template_dir>/<name>_filled.docxOutput .docx path
--scanfalseList all detected form fields
--data""JSON string with field→value mapping
--data-file""Path to JSON file with field→value mapping
--fontPlatform CJK serifFont name for filled text
--font-size11Font size in points

How Field Detection Works

  1. Table-based (primary): Scans all tables for rows with label→value cell pairs. A label cell contains short text (CJK or Latin); the adjacent cell is the value field.
  2. Merged rows: Detects full-width merged cells with "Label:" pattern as large text areas.
  3. Paragraph fallback: If no tables found, detects "Label:value" patterns in paragraphs.

Limitations

  • .doc files are auto-converted to .docx via macOS textutil, which loses table structure. For best results, use .docx templates directly. If you only have .doc, convert with LibreOffice first: libreoffice --headless --convert-to docx file.doc
  • Fields are matched by normalized label text (whitespace removed). If a label contains unusual formatting, the match may fail — use --scan to verify detection.

Dependencies

python3 -m pip install python-docx

Runtime context (shared)

运行前读取本 Skill 包的 skill.yaml,由宿主提供 skill-runtime/v1 上下文。字段解析顺序为:当前请求、项目上下文、个人 Preferences、品牌 Profile、通用默认值。

  • 只使用 Manifest 声明的字段;Profile 保存公开品牌事实,Preferences 保存个人工作偏好。
  • required: true 字段缺失时,按 Manifest 的问题配置向用户提出一个聚焦问题;用户明确同意后再保存回答。
  • 报错提供可复制的 context_id、字段路径与来源,诊断内容避开秘密、完整私人路径和原始配置。

通用反馈闭环

用户在 Skill 驱动任务中提出修改意见时,继续当前产物前必须执行:

  1. 先判断意见是 task-specific(仅本次)还是 reusable(可跨任务复用)。
  2. task-specific 只修改当前任务,不改 Skill。
  3. reusable 先确定作用域:领域规则先更新对应 canonical Skill;适用于所有 Skill 的规则先更新共享规范。
  4. 完成规则更新、版本、lint 与分发核验后,再把修改应用到当前任务。
  5. reusable 修改会使此前的“确认”“继续”“发吧”失效;完成当前产物修改和回读后必须停下,等待用户下一步指示,不自动进入发布、提交或其他外部写入。

Signals

GitHub stars
66
Forks
17
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
lov-fill-form
Source
github.com/lovstudio/skills