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Script Compose

Organization
Utopai-Research
script-compose

Handles explicit screenplay/story work on the filmmaking canvas. Triages screenplay (use verbatim), story/concept (iterate then rewrite), or neither (defer). Captures the final script note/title; on explicit command, splits into <=15s shot notes and extracts characters, variants, locations, and speaking/VO needs. Use for writing, adapting, rewriting, splitting, analyzing, or breaking down scripts/stories. Preserves dialogue verbatim and returns multi-stage planning to story-to-video-workflow before media generation. Does not split/analyze on file drop or without explicit intent.

Overview

PublisherUtopai-Research
Repositorypai-code
Skill namescript-compose
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 Script Compose 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/script-compose
  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/script-compose .claude/skills/script-compose
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Script Compose 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 Script Compose 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 Script Compose 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.

Run only on explicit user intent, never on file drop. Dropped text/PDF already exists as a note (data.body) and mirror (./assets/notes/<note_id>.md).

Defaults: a 30s beat is one moment; match the input language; with characters, prefer meaningful dialogue and let narration support rather than carry the scene, but let wordless action carry a beat instead of packing every one with dialogue.

Stop at script capture, shot notes, and anchor extraction. Route multi-stage work back to story-to-video-workflow.

Capture target duration when observable:

  • Explicit user duration wins ("30 seconds", "2-minute short").
  • Timestamp blocks come next; sum them.
  • Otherwise estimate roughly and mark it as an estimate.

Store target_duration_sec and duration_basis when known. If implied runtime is >~3 minutes, call out scope before shot/video planning.

1. Triage → Capture

Classify the input, then capture as in §2. Never skip straight to §3.

  • Screenplay (INT./EXT. + ALL-CAPS cues + dialogue) → use verbatim. For dropped text/PDF, read workflow.json data.body or ./assets/notes/<note_id>.md. Pick a 2–5 word title; identify duration basis; do not rewrite to fit.
  • Story / concept (prose, pitch, logline) → sketch ONE paragraph back (setting, characters, conflict, target duration) and ask if it's the shape. Iterate. On "yes/go", rewrite using the rules below, then capture.
  • Neither → don't run; defer to image-compose / video-compose.

Torn between screenplay and story? Prefer screenplay — safer than rewriting.

Rewrite rules (story → screenplay):

  • Format: INT./EXT. LOCATION - TIME slug, present-tense action, ALL-CAPS cue + dialogue. No scene numbering. No camera directions (that's video-compose).
  • Preserve user-quoted dialogue verbatim.
  • With characters, include dialogue that reveals motive/conflict/relationship; avoid narration-only exposition unless VO-driven.
  • Pace speech at ~2.2-2.5 words/sec plus reaction/action room.
  • Duration: match if stated; default 30–45s. Don't overshoot.
  • Short input, longer target? Keep verbatim and ask "reads as ~Ns; extend?" — don't silently pad.

2. Capture — canvas note + title

ONE note. No split. Canvas writes go through the mutator, never direct workflow.json.

  1. read ./workflow.json (read-only inspection — see if title is already set).
  2. Append the script note via the mutator with subtype: "script":
    node "$PAI_REPO_ROOT/server/cli/canvas_mutate.js" \
      --op addNode \
      --payload-json '{"node":{"type":"note","data":{"subtype":"script","label":"Script: <title>","body":"<full screenplay verbatim>","metadata":{"author":"agent","timestamp":"<ISO>","target_duration_sec":45,"duration_basis":"estimated from script length"}}}}'
    Omit target_duration_sec / duration_basis only when there is no defensible signal. Stdout returns assigned.node_id — keep it for §3 (shots derive from this id).
  3. Set the workflow title if empty:
    node "$PAI_REPO_ROOT/server/cli/canvas_mutate.js" --op setTitle --payload-json '{"title":"<title>"}'
  4. Confirm with Captured., then offer the next step as a choice rendered per the project PROJECT_AGENT.md § "Recommendation and choice shape". Recommended option: "Split it into <=15s shots and extract characters/locations/voices." Plus an escape to do something else.

STOP. Do NOT proceed to §3 without an explicit user command.

3. Analyze — on explicit user command

Triggers (judge intent): "split into shots / clips", "break this up", "pull the characters / locations", "who's in this", "analyze this script", "design the characters from this script". Not triggers: "what's in this", "summarize", "tell me about it" — those are read-and-reply.

When triggered:

  1. Slug — kebab-case of the working title. Collision → suffix -2, -3.
  2. Shot splits (≤15s each): use metadata.target_duration_sec or estimate. Split on natural beats (slug/dialogue/location/time/appearance changes). For >15s material, keep resulting shots as close to 15s as natural (default ≈ ceil(total_seconds / 15) shots); split shorter only for hard cuts, dialogue turns, continuity shifts, or strong beats — don't over-fragment just because the script's own time markers say so. Pace speech at ~2.2-2.5 words/sec plus reaction/action room; silent action ~3–5s. If dialogue cannot fit naturally, split it; reduce only when the user asked for compression. Never rewrite — shot bodies are verbatim slices. Each shot note has subtype: "shot". Build one addBatch with N shot notes + N derived edges:
    node "$PAI_REPO_ROOT/server/cli/canvas_mutate.js" \
      --op addBatch \
      --payload-json '{
        "nodes": [
          {"type":"note","data":{"subtype":"shot","label":"Shot 1 (0–15s)","body":"<slice>","metadata":{"author":"agent","timestamp":"<ISO>"}}},
          {"type":"note","data":{"subtype":"shot","label":"Shot 2 (15–30s)","body":"<slice>","metadata":{"author":"agent","timestamp":"<ISO>"}}}
        ],
        "edges": [
          {"from":"<script_note_id>","to":"$0","kind":"derived"},
          {"from":"<script_note_id>","to":"$1","kind":"derived"}
        ]
      }'
    $N placeholders are 0-indexed positions in nodes; the mutator resolves them to the assigned ids after running. Reply's assigned.node_ids is the array of shot ids in the same order.
  3. Anchor extraction — from the shot bodies, extract only downstream needs:
    • Characters: recurring/visually important people/entities. Include one-line base visuals only when given.
    • Variants: same character with materially different on-screen look by scene/shot: age jump, costume change, injury, disguise, transformation, wet/dirty/bloodied state if it must persist across shots. Do not create variants for transient expressions or tiny props.
    • Locations: distinct settings plus same-setting variants when framing/scale, time, weather, lighting, dressing, story state, or close/detail coverage matters.
    • Voices: every speaking character and narration/V.O.; preserve speaker labels and dialogue language.
    • Missing anchors: first character, variant, location, or voice that blocks rendering Shot 1.
  4. Parse offer — ONE compact planning line plus a soft next step:

    Plan check: ~<seconds>s, <shots> shots, <N> character(s), <V> variant(s), <M> location(s), <S> voice need(s). Missing: <first blocker>. If N>0, V>0, M>0, or S>0, offer next step with project choice shape. Recommended: "Design the character/location anchors, then voices." Anchors include base/variant character sheets, detailed location/location variants, and speaker/VO voice anchors. On approval, route to image-compose first (base character sheets, needed character variants, and location stills) with --source-node-id <script_note_id> so the new nodes wire back to the script. After image anchors land, route speaking/narration needs to voice-compose. Don't generate inside script-compose. Skip the offer if every count is 0.

If the user's command was narrower ("just the shots", "only characters"), do only that sub-step and skip the offer.

4. Revisions

Surgical (title still fits): update script-note body + affected shot bodies in place. Use the mutator's updateNode op (one call per node, or batched via updateBatch). Structural (title no longer fits): new script note (addNode); old→new edge addEdge with kind:"derived"; new shot family via addBatch against the new script note. Leave old shots; delete only if asked (deleteNode cascades edges for you).

Frequently asked questions

What does the Script Compose AI skill do?

Handles explicit screenplay/story work on the filmmaking canvas. Triages screenplay (use verbatim), story/concept (iterate then rewrite), or neither (defer). Captures the final script note/title; on explicit command, splits into <=15s shot notes and extracts characters, variants, locations, and speaking/VO needs. Use for writing, adapting, rewriting, splitting, analyzing, or breaking down scripts/stories. Preserves dialogue verbatim and returns multi-stage planning to story-to-video-workflow before media generation. Does not split/analyze on file drop or without explicit intent.

Why use Script Compose on TypingMind?

Because you install it once and use it with any model. Script Compose 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 Script Compose in TypingMind?

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

Which AI models can use Script Compose?

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 Script Compose?

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

Is the Script Compose 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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