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Stage Consistency

Community
Orkas-AI
stage-consistency

Multi-shot narrative & character consistency — a character bible with a locked front-portrait anchor, view-matched reference selection, recent-frame carry-forward, Cameo (a user photo as the lead), and global planning for long scripts/novels. Trigger on top of the generation line when the SAME character must look the same across many shots/scenes, or for long-form story→video; do NOT trigger for one-off single-shot generation.

Overview

PublisherOrkas-AI
RepositoryOrkas-VideoStudio
Skill namestage-consistency
Stars
500
Forks
25
Bundled files
Instructions only
LicenseMIT
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 Orkas-AI on GitHub. Read the source before you install it.

Installation

Install the Stage Consistency 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/Orkas-AI/Orkas-VideoStudio/tree/main/packages/skills/stage-consistency
  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/Orkas-AI/Orkas-VideoStudio.git /tmp/Orkas-VideoStudio
mkdir -p .claude/skills
cp -r /tmp/Orkas-VideoStudio/packages/skills/stage-consistency .claude/skills/stage-consistency
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Stage Consistency 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 Stage Consistency 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 Stage Consistency 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.

stage-consistency

How to keep characters, settings, and style consistent across many generated shots (and across scenes in a long story). This is the depth layer on top of the generation line: the generation line makes clips; this skill makes the SAME character look the same in every clip. Describe the outcome; the tools are ovs image for portraits (incl. reference-image editing), ovs video for image-to-video, ovs edit extract-frame to pull an anchor frame, and a long-document strategy (chunking + retrieval/RAG over the source) for long sources (or the equivalent MCP tools).

1. Character bible (extract once, lock the look)

From the script/idea, extract every recurring character into project/characters/bible.json. For each character separate:

  • static features — the immutable visual identity: face, hair, body shape, ethnicity, age range. These NEVER change shot-to-shot.
  • dynamic features — changeable: clothing, accessories, expression.
  • a single canonical name (merge every alias/pronoun for the same person to one id).
json
{
  "alice": {
    "static": "early-20s East-Asian woman, oval face, long black hair, slim build",
    "dynamic": "green linen dress, small gold earrings",
    "portrait": { "front": "characters/alice_front.png", "side": "characters/alice_side.png", "back": "characters/alice_back.png" }
  }
}

2. Portrait anchor (generate once, then LOCK)

For each character, generate ONE front portrait (full-body, front view, plain background) from its static+dynamic features with ovs image. This is the anchor — never regenerate it. Derive side/back views by reference-image editing the front portrait (so all three views are the same person). Save paths into the bible.

  • Cameo (the user uploads a photo to BE a character): use that photo AS the front portrait — skip generation for that character. Everything downstream references it.

3. Storyboard with character binding

Decompose the script into shots. Each shot records:

  • which characters are visible (and at what camera angle — front / side / back),
  • a first-frame description and the motion,
  • describe motion by VISUAL FEATURES, not names — write "the figure in the green dress turns to the figure in the blue shirt", NOT "Alice turns to Bob". (Generators ground on appearance, not names.)

4. Per-shot reference selection (the core consistency mechanism)

For each shot, before generating it, assemble the reference images and bind them explicitly:

  1. Candidates = for every visible character, the portrait view that MATCHES the shot's camera angle (front shot → front portrait, profile → side portrait) plus the most recent already-generated frame that shares this scene/camera.
  2. Priority: a recent prior frame of the same camera > an older frame > portrait-only; same-camera > different-camera. Keep relative positions (if a character was on the left, keep them on the left). Don't pick two references that show the same face.
  3. Explicit binding: write a generation prompt that maps each reference to what it's for — "ref 1 (front portrait) → facial features & hair; ref 2 (previous frame) → lighting & background".
  4. Generate the shot with ovs video image-to-video using those reference images.

4b. Verify the keyframe BEFORE you pay to animate

For a character-critical shot, generate the first-frame image first (cheap), check it, and only then drive the video from that locked frame — catching drift at the still is far cheaper than discovering it after paying to animate.

  • Check the keyframe against the bible on the identity axes, in priority order: 1) character — gender, age, ethnicity, facial features, body shape, hairstyle; 2) spatial — relative positions/perspective match the reference (left stays left); 3) accuracy — matches the shot's description. Read it yourself if multimodal; if you cannot see images, mark it unverified and lean harder on a strong locked reference.
  • Re-roll the cheap image, not the expensive video, until it matches; generate a few candidates and keep the most consistent one. Never hard-fail — pick the best and note its shortcoming.

5. Carry-forward (continuity between shots)

After a shot is generated, pull its representative frame with ovs edit extract-frame (e.g. its last frame) into project/frames/. That frame becomes a high-priority reference for the NEXT shot in the same scene — this is what carries lighting, composition, and the exact current look forward, so drift doesn't accumulate.

Cache the decision, not just the pixels. Record each shot's chosen references + the binding prompt (e.g. in the plan/segment) alongside the produced frame. A resumed run then reuses the SAME references and prompt and reproduces the same frame, instead of re-rolling the selection and silently drifting from the earlier run.

6. Long script / novel → movie (global planning first)

For long-form, plan globally BEFORE generating:

  1. Chunk the source; for a very long novel, use a long-document strategy (chunking + retrieval/RAG over the source) and retrieve the relevant chunks per event instead of holding it all at once.
  2. Extract events → scenes; extract characters per scene.
  3. Merge characters so the bible is global: same person across scenes → one entry (track the name variant each scene uses); same name but conflicting static features (e.g. a child vs. the adult years later) → split into two characters. New character → append.
  4. Carry the ONE global bible (locked portraits) through every scene, so a character looks identical across the whole story.

Default scope caps (cost control)

A story multiplies billable calls (portraits per character + clips per shot + carry-forward frames). Default bounds unless the user explicitly asks for more: ≤ 3 characters, ≤ 3 scenes, ≤ 6 shots total. Above these, state the full count + the rough number of billable image/video generations in the pre-generation confirmation and get the user's explicit OK before generating anything.

Rules & boundary

  • The anchor is sacred: a character's front portrait is generated once and reused; never silently regenerate it (that's how looks drift).
  • Cost discipline: portraits + per-shot generation are billable; state the exact character/shot counts in the pre-generation confirmation and never start generating before the user approves.
  • This skill is the consistency METHOD; it does not itself author HTML (stage-compose) or cut user footage (stage-edit). It sits on top of the generation line.

Frequently asked questions

What does the Stage Consistency AI skill do?

Multi-shot narrative & character consistency — a character bible with a locked front-portrait anchor, view-matched reference selection, recent-frame carry-forward, Cameo (a user photo as the lead), and global planning for long scripts/novels. Trigger on top of the generation line when the SAME character must look the same across many shots/scenes, or for long-form story→video; do NOT trigger for one-off single-shot generation.

Why use Stage Consistency on TypingMind?

Because you install it once and use it with any model. Stage Consistency 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 Stage Consistency in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Orkas-AI/Orkas-VideoStudio/tree/main/packages/skills/stage-consistency. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Stage Consistency?

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 Stage Consistency?

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

Is the Stage Consistency AI skill free?

Yes. It is published on GitHub by Orkas-AI under the MIT 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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