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Widget Forms

CommunityPopular
0xsline
widget-forms

Use when the agent should ask the user for structured input with an in-chat form, including single-select, multi-select, text fields, style pickers, or voice audition choices.

Overview

Publisher0xsline
RepositoryOpenChatCut
Skill namewidget-forms
Stars
1.9K
Forks
277
Bundled files
Instructions only
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by 0xsline on GitHub. Read the source before you install it.

Installation

Install the Widget Forms AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/0xsline/OpenChatCut.git /tmp/OpenChatCut
mkdir -p .claude/skills
cp -r /tmp/OpenChatCut/src/agent/skills/widget-forms .claude/skills/widget-forms
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Widget Forms in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Widget Forms on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Widget Forms is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Widget Forms

Use the form tool instead of hand-writing markup: the editor chat renders the form as an interactive card and returns the user's structured answer.

Runtime Rule

Call ask_followup_questions. It serializes your fields into the editor's native form card; the user's submission comes back as their next message.

Plan the whole questionnaire before calling the tool. Send one final form, not a trial form followed by a corrected form. The tool supports at most 12 fields; if the user asks for more questions, merge related prompts into combined fields before the first call.

After calling ask_followup_questions, stop the turn and wait for the submitted answer to appear in chat. Do not apply a choice, create assets, or continue planning from a recommendation until the user's selection is present in the conversation.

Supported Field Mapping

Build a fields array. Write every visible string in the user's conversation language.

  • Native <form-single> -> { type: "single", variant: "default" }
  • Native <form-multi> -> { type: "multi", variant: "default" }
  • Native <form-text> / <form-textarea> -> { type: "text" }
  • Native <form-visual> -> { type: "single", variant: "visual" }
  • Voice audition cards -> { type: "single", variant: "voice" }
  • Native start-scenario cards -> { type: "single", variant: "scenario" }

Form Copy Tone

For form-level text (title, prompt, fields[].label, submitLabel, and messagePrefix), write like OpenChatCut is a capable video-making partner inviting the user to describe what they want, not like a rigid survey.

Aim for:

  • Warm, open-ended, and action-oriented. The copy should imply "choose the closest video need or just tell me your idea; we can figure it out together."
  • Short and scannable. Use one natural sentence for prompt and concise question labels.
  • Honest capability framing. OpenChatCut can help with many video workflows, but do not claim unsupported abilities or guarantee a result before inputs are known.
  • The user's language and local product terms. Keep "OpenChatCut", "B-roll", "Motion Graphics", "MG 动画", model/product names, and platform names in their established forms.
  • User-facing creative wording. For early planning or creative-intake forms, prefer natural terms such as idea, direction, plan, story, shot list, audience, mood, or video need over internal production-document language.

Avoid stiff labels such as "Select video type", "Please choose the video type for this project", or "What type of video do you want to make?" for scenario intake unless the host has no room for warmer copy.

For a scenario-intake form, prefer copy like:

json
{
  "title": "What do you want to make?",
  "prompt": "Choose the closest video scenario, or choose Something else and describe your idea.",
  "submitLabel": "Start creating",
  "messagePrefix": "I want to start with this video direction:",
  "fields": [
    {
      "id": "scenario",
      "label": "Which scenario fits your video best?",
      "type": "single",
      "variant": "scenario",
      "otherPlaceholder": "For example: turn my travel footage into an atmospheric vlog / make a launch video for a new product"
    }
  ]
}

Do not include file-upload questions in forms. If a task actually needs source media and the project/chat does not already have it, ask the user separately to upload files in the editor (drag & drop or the upload button, or paste into the chat composer). File upload is not a default prerequisite for every questionnaire; only ask for it when the next editing step depends on missing media.

For choice fields:

  • Use id for the internal value the next tool call needs.
  • Use label for what the user sees.
  • For ordinary single-select and multi-select cards (variant: "default"), keep options label-only. Do not add per-option description unless the user cannot distinguish the choices from labels alone.
  • Use description mainly for voice cards. Visual style cards should usually use only preview + label.
  • When an off-list answer is acceptable, add an explicit option with id: "__other__" and a label in the same language as the rest of the form, using the word the user would expect for an off-list answer. The widget will turn this option into a text entry when selected. Use otherPlaceholder if the text entry needs a placeholder.

For visual cards:

  • Use real image URLs or data image URLs in preview.
  • For Design Style catalog choices, call manage_design_style with action: "list" first, then map each returned preset to { id: preset.presetId, label: preset.name } (no thumbnails in this build).
  • Never hardcode catalog preset ids unless the user already selected one.

For voice cards:

  • Use audioUrl for the sample file.
  • Prefer the documented sample path such as /voice-samples/doubao-liuchang.mp3 or a public HTTPS URL. Do not pass localhost sample URLs; MCP host iframes do not reliably resolve editor-local media.
  • Keep user-visible descriptions provider-neutral. Describe the voice the same way the native OpenChatCut audition UI does: gender / age range / tone / use case, such as Female / young / friendly, general or 男 / 中年 / 低沉知识解说. Do not show provider names like ElevenLabs or Doubao in option descriptions.
  • Keep the option id equal to the provider voice id needed by submit_voice.

For native start-scenario cards:

  • Use this when the user should choose which OpenChatCut video workflow to start, such as talking-head editing, MG animation, long-video-to-shorts, product/app promo, AI short film, or explainer video.
  • Present these as common video needs/scenarios, not as the only possible video workflows. The form must also let the user describe a different video need.
  • Use exactly one single-select field with variant: "scenario".
  • Provide options using the canonical ids below and localized labels in the user's language. Include id: "__other__" only when you need to customize the off-list label; otherwise the backend appends a localized Other option.
  • English, Chinese, and Spanish scenario labels/descriptions/starter prompts are built in. For any other user language, faithfully translate each scenario's English label, description, and starter prompt into the user's language and pass those localized values in the option objects. Preserve OpenChatCut product terms and workflow meaning; do not add new requirements. Use submitPrompt for the translated starter prompt. This override is specific to variant: "scenario"; ordinary option cards, voice cards, and visual style cards already get their visible text from the values you pass.
  • Do not provide custom preview or audioUrl; the backend fills the native first-screen preview image.
  • Canonical ids: talking-head, motion-graphics, long-video-to-shorts, app-promo, ai-cinematic-short-film, explainer, plus __other__ for a free-form video need.
  • Example options: { "id": "talking-head", "label": "Talking Head Editing" }, { "id": "motion-graphics", "label": "Motion Graphics" }, { "id": "long-video-to-shorts", "label": "Long Video to Shorts" }, { "id": "app-promo", "label": "Product / App Promo" }, { "id": "ai-cinematic-short-film", "label": "AI Short Film" }, { "id": "explainer", "label": "Explainer Video" }, { "id": "__other__", "label": "Something else" }.

Current Gaps

ask_followup_questions is for structured answers only. It does not support native custom HTML, timeline parameter bridges, editor item selection, or file upload fields. For files already held by the agent runtime or attached directly to the chat outside this card, the media-import workflow is still valid: call import_media and run the helper/direct upload path. For files the user wants to place directly in a project, ask them to use the OpenChatCut editor upload UI.

Example

json
{
  "title": "Your video idea",
  "prompt": "Choose or fill in what you have in mind so OpenChatCut can pick a good starting direction.",
  "submitLabel": "Send idea",
  "messagePrefix": "Continue with this video direction:",
  "fields": [
    {
      "id": "goal",
      "label": "What's the main goal of this video?",
      "type": "single",
      "options": [
        { "id": "product_intro", "label": "Product intro" },
        { "id": "social_ad", "label": "Social ad" },
        { "id": "__other__", "label": "Something else" }
      ]
    },
    {
      "id": "elements",
      "label": "What should it include? (Select all that apply)",
      "type": "multi",
      "options": [
        { "id": "broll", "label": "B-roll" },
        { "id": "logo", "label": "Brand logo" },
        { "id": "__other__", "label": "Something else" }
      ]
    }
  ]
}

Frequently asked questions

What does the Widget Forms AI skill do?

Use when the agent should ask the user for structured input with an in-chat form, including single-select, multi-select, text fields, style pickers, or voice audition choices.

Why use Widget Forms on TypingMind?

Because you install it once and use it with any model. Widget Forms is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Widget Forms in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/0xsline/OpenChatCut/tree/main/src/agent/skills/widget-forms. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Widget Forms?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Widget Forms?

As many as you like. As long as a model supports skills, you can use Widget Forms with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Widget Forms AI skill free?

Yes. It is published on GitHub by 0xsline under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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