mcpbeat Sign in

Story To Video Workflow Agent Skill

>- 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.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
320
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Utopai-Research/pai-pro --skill story-to-video-workflow

The instruction itself

12 sections, as written by the author

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:

  • Clarify only blockers.
  • Raw idea/story -> script-compose production script; existing screenplay -> capture/adapt.
  • script-compose splits <=15s dialogue-aware shots and extracts characters, material variants, detailed locations/location variants, and speaker/VO needs.
  • image-compose creates useful visual anchors: base/variant character sheets and detailed location/detail anchors.
  • voice-compose creates reusable anchors for every speaker and VO/narrator.
  • Confirm shot count, durations, continuity needs, and first blocker.
  • Default render path: straight-to-video from refs. Storyboard only if requested, hard to control, or needed for diagnosis.
  • Default dispatch: hybrid. Chain continuous dependent shots; render independent scenes/shots in parallel.
  • 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

| Need | Load next |

|---|---|

| Script capture, rewrite, split, or analysis | script-compose |

| Character, location, storyboard, starting frame, or visual anchor | image-compose |

| Narration, dialogue read, character voice, or audio node | voice-compose |

| Clip render, continuation, audio refs, storyboard animation, or video prompt | video-compose |

| Scene/ref grouping or canvas layout frames | groups-compose |

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

  • 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_*:

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

Other skills for the same job

different authors, same section of the catalogue
Canvas Design
by anthropics
vendor ×13

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

1388k tokens
Algorithmic Art
by anthropics
vendor ×10

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

15k tokens scripts
Image Enhancer
by frostant
×6

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

635 tokens
Video Downloader
by CommandCodeAI
×4

Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.

671 tokens
Histolab
by christophacham
×3

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

18k tokens
Omero Integration
by christophacham
×3

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

32k tokens
Pydicom
by christophacham
×3

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

13k tokens scripts
Transformers
by christophacham
×3

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

13k tokens

How to use it

Copy the folder

Take utopai-research/story-to-video-workflow from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.