Story To Video Workflow logo

Story To Video Workflow

Organization
Utopai-Research
story-to-video-workflow

Orchestrates story, script, screenplay, concept, product promo, and multi-shot idea work into finished video. Use first when the user asks to make a video from a story or script; asks what next in a story video project; or needs a decision spanning script splitting, image refs, voices or VO, video clips, render strategy, Timeline ordering, or final Timeline handoff. Routes execution to script-compose, image-compose, voice-compose, and video-compose before those skills' CLIs are used.

Overview

PublisherUtopai-Research
Repositorypai-code
Skill namestory-to-video-workflow
Stars
352
Forks
44
Bundled files
Instructions only
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 Utopai-Research on GitHub. Read the source before you install it.

Installation

Install the Story To Video Workflow 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.
    https://github.com/Utopai-Research/pai-code/tree/main/skills/story-to-video-workflow
  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/Utopai-Research/pai-code.git /tmp/pai-code
mkdir -p .claude/skills
cp -r /tmp/pai-code/skills/story-to-video-workflow .claude/skills/story-to-video-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Story To Video Workflow 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 Story To Video Workflow 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 Story To Video Workflow 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.

Story-to-video workflow

Contract

  • This skill wakes first for story/script/promo-to-video work.
  • Own sequencing only. Before execution, load the matching capability skill; do not call generate_* here.
  • Recommendations are not consent. Stop before paid generation or pipeline changes unless the user explicitly approved an autonomous workflow.

Default arc

Use this ladder unless the user skips, reorders, supplies refs, or asks for a rough direct render:

  1. Clarify only blockers.
  2. Raw idea/story -> script-compose production script; existing screenplay -> capture/adapt.
  3. script-compose splits <=15s dialogue-aware shots and extracts characters, material variants, detailed locations/location variants, and speaker/VO needs.
  4. image-compose creates useful visual anchors: base/variant character sheets and detailed location/detail anchors.
  5. voice-compose creates reusable anchors for every speaker and VO/narrator.
  6. Confirm shot count, durations, continuity needs, and first blocker.
  7. Default render path: straight-to-video from refs. Storyboard only if requested, hard to control, or needed for diagnosis.
  8. Default dispatch: hybrid. Chain continuous dependent shots; render independent scenes/shots in parallel.
  9. Render clips, assign Timeline shot_id when sequence order is unambiguous, then hand off to Timeline.

Plan ahead internally, but only ask the next meaningful user-facing choice; the Consent and gates ladder fixes when render path and dispatch become askable.

Skill routing

NeedLoad next
Script capture, rewrite, split, or analysisscript-compose
Character, location, storyboard, starting frame, or visual anchorimage-compose
Narration, dialogue read, character voice, or audio nodevoice-compose
Clip render, continuation, audio refs, storyboard animation, or video promptvideo-compose
Scene/ref grouping or canvas layout framesgroups-compose

Capability skills own CLI flags, node grammar, refs, and recovery hints. PROJECT_AGENT.md owns shared failure handling.

Consent and gates

  • Draft-only, failed, and cancelled generations do not advance the pipeline.
  • One-off generation outside the story pipeline routes directly to the capability skill.
  • Honor explicit rough-direct/skip choices.
  • Gate ladder: script -> shot notes -> anchors/user refs/rough-direct -> real clip plan -> render path -> dispatch. Ask each rung only after the prior one is real; stop after render-path unless the user already names dispatch too.

VO and dialogue invariants

  • Script/shot notes carry dialogue/VO until final audio exists.
  • audio_result.data.text is source of truth only for approved final narration/line reads.
  • video-compose includes spoken text verbatim and treats voice samples as timbre anchors.

Recommendation shape

Follow the project PROJECT_AGENT.md § "Recommendation and choice shape". Recommend one concrete next step. Add a second option only when there is a real tradeoff.

Planning checkpoint

Before recommending refs/video, inspect workflow.json when needed and summarize only:

  • Target duration from user duration, timestamps, or a rough estimate.
  • Planned <=15s shot count.
  • Characters, material variants, detailed locations/location variants, close/detail needs, speakers/VO.
  • First missing anchor blocking the next clip.

If the story implies more than roughly 3 minutes, recommend narrowing scope before clip planning.

After shot notes, missing video-bound character/location/voice anchors are the default next step; include a rough-direct skip when speed matters. Once anchors/user refs/rough-direct are settled, offer only a short ref review or clip-plan confirmation if ambiguity remains.

Render path

Ask only after the script/shot plan is settled and anchors, usable refs, rough-direct, or a simple single-clip case make rendering real. If anchors are still missing, return to Planning checkpoint.

Use project choice shape:

  • header: Render
  • question: Choose render path.
  • options:
    • label: Straight to video (Recommended) description: Fastest path to motion.
    • label: Storyboard first description: Generate storyboard images first for composition control.

For storyboard-first, load image-compose Pattern 6: one composite mosaic per clip/<=15s shot note, subtype storyboard.

Dispatch for multiple clips

Ask only after render path is picked and a multi-clip plan exists. Skip for one clip. Use project choice shape:

  • header: Dispatch
  • question: Choose clip dispatch.
  • options: order these by the observable story signals below; suffix the first label with (Recommended).
    • label: Hybrid description: Chain within continuous scenes; render separate scenes independently.
    • label: Parallel description: Render all clips independently.
    • label: Sequential description: Each clip continues from the previous one; boundaries default to a hard cut to a new angle (avoids the same-shot seam) — keep a boundary same-shot only for an unbroken oner.

Signals: continuous scene/state -> sequential (hard-cut handoffs between clips); a single unbroken action the viewer must read as ONE motion -> one ≤15s clip, else sequential with a same-shot handoff; separate scenes/time jumps/wardrobe changes/montage -> parallel; continuous clusters separated by hard cuts -> hybrid. Do not chain video refs across location, time, wardrobe/state, dream/reality, or montage breaks.

After media results

After terminal generate_*:

  1. If it is only draft-stage JSON, report the price/status and stop.
  2. If ok:false, follow project failure handling and do not advance the pipeline.
  3. If ok:true, identify the landed node id from the result or canvas state.
  4. Read workflow.json if shots, refs, voices, clips, or reel order affect the next decision.
  5. Recommend exactly one next useful filmmaking move.

Typical priority:

  • Script note landed -> recommend splitting into <=15s shot notes and extracting anchors.
  • Shot notes exist but anchors are missing -> recommend the first missing character/location anchor, with a rough-direct skip option. Do not ask render path or dispatch yet.
  • Character/location ref landed -> finish remaining anchors; then ref review, clip-plan confirmation, or straight-to-video. Mention storyboard only if requested/useful.
  • Voice landed -> recommend using it with the matching visual ref in the next dialogue/narration clip.
  • Storyboard landed -> recommend review or animating the matching clip.
  • Video clip landed -> recommend the next clip, or Timeline handoff when all planned clips are ready.

Final handoff

Timeline owns reel order. Numeric video_result.data.shot_id means a clip is in the reel. When all planned story clips are ready and order is unambiguous, assign shot_id = 1..N with one updateBatch before handoff:

node "$PAI_REPO_ROOT/server/cli/canvas_mutate.js" \
  --op updateBatch \
  --payload-json '{"updates":[{"id":"<video_1>","patch":{"shot_id":1}},{"id":"<video_2>","patch":{"shot_id":2}}]}'

Do not use generate_video.js --shot-id for speculative/partial ordering. Assign after clips land. Local export uses reel_stitch.js only on explicit request. Then tell the user to open Timeline to inspect and preview.

Frequently asked questions

What does the Story To Video Workflow AI skill do?

Orchestrates story, script, screenplay, concept, product promo, and multi-shot idea work into finished video. Use first when the user asks to make a video from a story or script; asks what next in a story video project; or needs a decision spanning script splitting, image refs, voices or VO, video clips, render strategy, Timeline ordering, or final Timeline handoff. Routes execution to script-compose, image-compose, voice-compose, and video-compose before those skills' CLIs are used.

Why use Story To Video Workflow on TypingMind?

Because you install it once and use it with any model. Story To Video Workflow 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 Story To Video Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Utopai-Research/pai-code/tree/main/skills/story-to-video-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Story To Video Workflow?

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 Story To Video Workflow?

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

Is the Story To Video Workflow AI skill free?

It is published on GitHub by Utopai-Research. Check the repository for licensing terms. 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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