Agent skill

Scene Splitter

by hassancs91 in hassancs91/claude-image-generation

Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream.

MITAuto-check passedFrontend & Design

Install Scene Splitter

skills CLI
$ npx skills add hassancs91/claude-image-generation --skill scene-splitter -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hassancs91/claude-image-generation scene-splitter --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hassancs91/claude-image-generation.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/scene-splitter .claude/skills/scene-splitter && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scene-splitter
GitHub stars
102
Token cost
~2.3k tokens
SKILL.md length
1,155 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream.

  • Works in 6 steps: Story analysis → Scene identification → Per-scene metadata → …
  • The user wants to split a story into scenes
  • SKILL.md covers When this skill applies, Architectural rule: one scene…, Reading level + length cap… and Workflow — five stages with…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scene Splitter is an agent skill from hassancs91/claude-image-generation. Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream. The first step of the AI Storybook pipeline. Tuned for beginner-level stories (short sentences, ~8-12 scenes), with a per-scene length cap so each scene fits one phone screen without scrolling. Use this skill whenever the user wants to split a story into scenes, prepare a story for the storybook pipeline, break a story into pages/panels, or produce a…

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Frontend & Design, covering Text to speech and voice, Plain language and style rules and Image generation. It works with Storybook and Adobe Illustrator. The repository describes itself as: Connect Claude to image generation with Agent Skills. Three levels: a zero-cost code-based design engine, a Three.js 3D renderer, and a real diffusion model on Cloudflare. Plus… The licence is MIT.

When your agent uses it

  • The user wants to split a story into scenes
  • Prepare a story for the storybook pipeline
  • Break a story into pages/panels
  • Produce a scenes spine for the illustrator and narrator

Example prompts

  • “split this story into scenes”
  • “break this into pages”
  • “prepare this story for the storybook”
  • “/scene-splitter”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Story analysis
  2. Scene identification
  3. Per-scene metadata
  4. 5: Validation (reconstruction + length cap)
  5. HARD GATE: review scenes
  6. Output

What it can do on your machine

Read from SKILL.md and the folder at commit f533831. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scene Splitter loads about 2.3k tokens when it runs. Until then it costs about 243 tokens; SKILL.md has 1,155 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~243
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hassancs91/claude-image-generation at commit f533831, republished under its MIT licence (© hassancs91). 1,155 words, ~2,318 tokens.

Download SKILL.mdSave it as .claude/skills/scene-splitter/SKILL.md (or your agent's skills folder).
name
scene-splitter
description
Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream. The first step of the AI Storybook pipeline. Tuned for beginner-level stories (short sentences, ~8-12 scenes), with a per-scene length cap so each scene fits one phone screen without scrolling. Use this skill whenever the user wants to split a story into scenes, prepare a story for the storybook pipeline, break a story into pages/panels, or produce a scenes spine for the illustrator and narrator. Trigger on phrases like "split this story into scenes", "break this into pages", "prepare this story for the storybook", "make scenes for illustration", or whenever the user provides an English story and wants it chunked for a picture-book pipeline. Outputs a {slug}_scenes.json file consumed by the story-illustrator and story-narrator so they produce aligned output (1 image + 1 audio per scene).

Scene Splitter

Takes a plain English story and produces a numbered list of scenes — each scene being one moment that gets exactly one illustration and one narration clip downstream.

This is the upstream spine of the AI Storybook pipeline. Splitting once here and feeding both the illustrator and the narrator from the same {slug}_scenes.json keeps everything in lockstep: image N pairs with audio N pairs with paragraph N. No drift, no negotiation between skills.

When this skill applies

The user has an English story and wants to prepare it for the illustration + narration pipeline. Common phrasings: "split this into scenes", "break this into pages", "prepare this story for the storybook", "make scenes for illustration".

The skill does NOT apply to:

  • Stories that need only narration OR only images standalone (the illustrator and narrator can split on their own when used independently)
  • Non-narrative content (essays, instructions, lists)

Architectural rule: one scene = one moment = one image = one narration clip

A scene is a single moment that fits one illustration. Operationally, a new scene starts at any of:

  • Action transition — a different action happens
  • Scene change — a different location or a significant time jump
  • Emotional turn — joy → fear, calm → urgency, doubt → resolve
  • Character entry/exit — someone new appears or leaves the moment
  • Dialogue beat — a meaningful line of speech that deserves its own picture

Scenes are deliberately short and many. The storybook player renders one scene at a time, and the text MUST fit on a phone screen without scrolling. That drives the hard length cap below.

Reading level + length cap (beginner default)

This pipeline targets beginner readers. The cap is screen-fit-driven, not API-driven:

LevelPer-scene hard capTarget scene count
beginner (default)240 characters8–12 scenes

The cap is the number of characters in a single scene's text. Keep most scenes well under it (~120–180 chars reads best on a phone). To scale this pipeline up for longer/harder stories, raise the cap and the target count here — nothing downstream needs to change.

Workflow — five stages with one hard gate

Stage 1: Story analysis
  1. Read the full story.
  2. Detect: total character count, named characters, distinct scenes/locations, rough emotional arc.
  3. Determine the slug from the title (line 1) or filename — lowercased, hyphenated, ASCII-safe (^[a-z0-9-]+$, max 60 chars). Examples: "The Little Cloud" → the-little-cloud; red_balloon.md → red-balloon.
  4. Estimate scene count (aim for the 8–12 beginner range; more for a longer story).
  5. Output a one-line summary: "Story is [N] chars, [X] characters, [Y] locations, ~[Z] scenes expected at beginner level. Slug: {slug}."
Stage 2: Scene identification

Walk the story in order. At each candidate boundary, decide: new scene or extend the current one? Use the boundary rules above. When unsure, prefer MORE scenes — it's easier to merge two at the gate than to retroactively split one.

For each scene, capture the exact text from the source. The splitter chooses BOUNDARIES, not content — do not reword, summarize, or rewrite the author's prose. (If a sentence must be trimmed to fit the cap, that's an author decision — surface it at the gate, don't silently edit.)

Stage 3: Per-scene metadata

For each scene, populate:

  • index — 1-based
  • role — cover for scene 1, closing for the last scene, body for everything else
  • panel_type — one of establishing | action | reaction | detail (drives framing variety downstream):
    • establishing — wide, sets the place. Good for scene 1 and any location change.
    • action — something is happening; dynamic composition.
    • reaction — close on a character's face/feeling.
    • detail — tight on one object or element.
  • text — the exact story text for this scene (no title prefix on scene 1 — see below)
  • char_count — character count of text
  • scene — location/setting in 2–4 words (e.g. "sunny meadow", "cozy kitchen")
  • characters — array of named characters present in this moment
  • mood — emotional tone in 1–3 words (e.g. "warm, curious")
  • dominant_action — what happens in this moment, in one sentence (the illustrator turns this into the image prompt)

Title handling for the cover scene (LOCKED). The story's title (the # Heading on line 1) MUST NOT appear in scenes[0].text. The player shows the title in the header and builds a dedicated cover page from the first illustration (with the spoken title clip); scene 1's text then renders as its own story page. If the title were left in scenes[0].text it would show up twice (header + as scene 1's paragraph). Strip it from scene 1's text and store it at the top level instead:

json
{
  "story_slug": "the-little-cloud",
  "story_title": "The Little Cloud",
  "target_level": "beginner",
  "language": "en",
  "total_scenes": 9,
  "scenes": [
    {
      "index": 1,
      "role": "cover",
      "panel_type": "establishing",
      "text": "High in the sky lived a little cloud named Pip.",
      "char_count": 47,
      "scene": "wide blue sky",
      "characters": ["Pip"],
      "mood": "gentle, bright",
      "dominant_action": "A small white cloud drifts alone in a big blue sky."
    }
  ]
}
Show full SKILL.md (429 more words)Show less
Stage 3.5: Validation (reconstruction + length cap)

Reconstruction check. Concatenate story_title + "\n\n" + scenes[*].text (single spaces between scenes) and verify it reconstructs the original story. The title goes at the front because it was stripped from scenes[0].text. If even one word is missing or duplicated, abort and report which boundary is broken — do NOT proceed to the gate with broken reconstruction.

Length-cap check. For every scene, verify char_count ≤ 240. If any scene is over cap:

  1. Do NOT show the gate yet.
  2. List the over-cap scenes (index, char_count, first ~80 chars).
  3. Re-enter Stage 2 for those scenes: find an internal boundary (action shift, emotional turn, dialogue handoff) and split. If a scene is one indivisible moment but still over cap, flag it OVERSIZED — needs author trim rather than force-splitting on a non-boundary.
  4. Re-run the reconstruction check, then proceed.
Stage 4 — HARD GATE: review scenes

Show the user the proposed scene list in a compact table (so 10+ scenes fit on screen). Columns: index · role · panel_type · mood · char_count · first ~60 chars + ellipsis · dominant_action.

Below the table, prompt:

Scene plan ready — review and approve before I write scenes.json. Reply:

  • go to commit
  • merge N M to combine two adjacent scenes
  • split N to split a scene further (I'll propose where)
  • edit N to fix one scene's metadata
  • fix N <reason> for free-form feedback (e.g. fix 7 too long, split before "but then")
  • redo to restart with a different approach

If the user issues a change, apply it, re-run Stage 3.5 validation, and re-show the gate. Loop until go.

This gate is where human taste enters: it costs nothing now, but a wrong boundary means a wrong image AND a wrong audio clip downstream.

Stage 5: Output

Write {slug}_scenes.json to the working directory stories/{slug}/. Confirm in one line: "Wrote {slug}_scenes.json — N scenes, ready for the illustrator and narrator."

What this skill does NOT do

  • Does not edit or rewrite story text — only chooses boundaries.
  • Does not generate audio or images — those are downstream skills.
  • Does not classify or tag the story (this beginner pipeline skips classification by design).
  • Does not write to any database or upload anything — local file output only.

Compatibility notes

  • Output is consumed by story-illustrator and story-narrator. Both accept {slug}_scenes.json and skip their own splitting, so the image for scene N and the audio for scene N stay aligned by index.
  • Image filenames downstream are {slug}_part_NN.png; audio filenames are {slug}_part_NN.mp3, where NN is the zero-padded scene index. The publisher pairs them by that index.
  • Be conservative: when in doubt, more scenes. The user can merge at the gate.

© hassancs91, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/scene-splitter of hassancs91/claude-image-generation.

Open the folder on GitHubat commit f533831

Compare with similar skills

Scene Splitter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scene Splitter compared with similar skills
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Scene Splitter this skillhassancs91/claude-image-generation102—~2.3kAutomated safety check: PassMIT
Gimi IllustrationGiMi-Xiaomi/gimi-illustration-skill703—~1.7kAutomated safety check: PassMIT
Screen Demo Productioncalesthio/generative-media-skills197—~3.5kAutomated safety check: PassMIT
Wedding Video Guided Wizardaaronyi97/wedding-video-guided-wizard310—~1kAutomated safety check: PassMIT
Document Illustratorop7418/Document-illustrator-skill599—~1.6kAutomated safety check: NotesMIT
Hbg Life SimulationMr-funny/hbg-life-simulation141—~5.8kAutomated safety check: PassMIT

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Questions about Scene Splitter

What does Scene Splitter do?

Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream. Scene Splitter is an agent skill from hassancs91/claude-image-generation. Splits a plain English story into a numbered list of SCENES — each scene being one moment that gets exactly one illustration AND one narration clip downstream.

When should I use Scene Splitter?

Scene Splitter fits situations like: the user wants to split a story into scenes; prepare a story for the storybook pipeline; break a story into pages/panels; produce a scenes spine for the illustrator and narrator.

How do I install Scene Splitter in Claude Code?

Run `npx skills add hassancs91/claude-image-generation --skill scene-splitter -a claude-code`. Or copy the skill folder (.claude/skills/scene-splitter in hassancs91/claude-image-generation) into .claude/skills/scene-splitter in your project. Claude Code loads it when a task matches its description.

How do I install Scene Splitter in Codex?

Run `npx skills add hassancs91/claude-image-generation --skill scene-splitter -a codex`. Or copy the skill folder (.claude/skills/scene-splitter in hassancs91/claude-image-generation) into .agents/skills/scene-splitter in your project. Codex loads it when a task matches its description.

Can I use Scene Splitter in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hassancs91/claude-image-generation --skill scene-splitter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scene-splitter, .gemini/skills/scene-splitter, .github/skills/scene-splitter and .opencode/skills/scene-splitter in your project.

What does Scene Splitter need to run?

SKILL.md names no scripts, command-line tools or credentials: Scene Splitter is instructions for the agent only.

Does Scene Splitter access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scene Splitter safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scene Splitter use?

Scene Splitter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scene Splitter use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scene Splitter?

Skills that share tags, products or a category with Scene Splitter: Gimi Illustration (GiMi-Xiaomi/gimi-illustration-skill, 703 stars), Screen Demo Production (calesthio/generative-media-skills, 197 stars), Wedding Video Guided Wizard (aaronyi97/wedding-video-guided-wizard, 310 stars) and Document Illustrator (op7418/Document-illustrator-skill, 599 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scene Splitter?

hassancs91 (a GitHub user) maintains it in hassancs91/claude-image-generation, which has 102 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on August 18, 2026.

Source: hassancs91/claude-image-generation on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.