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Batch Montage Variants

CommunityPopular
0xsline
batch-montage-variants

Turn one pool of existing project media into a batch of distinct, publishable montage cuts instead of a single hero edit. Use for batch montage, one-source-many-outputs, 一源多出, 批量混剪, 批量出片, 矩阵号, 多账号分发, variant batches, deduped cuts, 去重变体, 防搬运, or when the user asks for N different versions of the same footage.

Overview

Publisher0xsline
RepositoryOpenChatCut
Skill namebatch-montage-variants
Stars
1.9K
Forks
277
Bundled files
2
LicenseAGPL-3.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by 0xsline on GitHub. Read the source before you install it.

Installation

Install the Batch Montage Variants 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.
  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/0xsline/OpenChatCut.git /tmp/OpenChatCut
mkdir -p .claude/skills
cp -r /tmp/OpenChatCut/src/agent/skills/batch-montage-variants .claude/skills/batch-montage-variants
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Batch Montage Variants 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 Batch Montage Variants 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 Batch Montage Variants 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.

Batch Montage Variants

Use this workflow when the user wants many finished cuts from the same media pool — matrix accounts, A/B hook testing, platform variants, or daily batch output. Each output must satisfy the agreed mode: structural differentiation, a controlled hook test, or a platform adaptation.

This is an OpenChatCut-native workflow. Use the current project, source assets, asset-frame inspection, AV/script context, and built-in editing tools. Editing imported media requires no external service. Use external sources or processing pipelines only when explicitly requested by the user.

When to switch workflows

  • One hero edit from many clips → Multi Clips to Reels. Do not use this skill to make a single cut.
  • One long source defining a story → Long Video to Shorts.
  • N distinct outputs from a shared pool → this skill.

Tell the user when you switch, and say why.

Workflow

  1. Read the project state before editing. Inventory the source pool: clip count, usable duration, aspect ratio, visual subjects, motion/energy, audio quality, and duplicates.
  2. Confirm the user wants more than one output and the pool supports the selected mode. Do not pad a distinct-cut batch with near-duplicates; controlled tests and platform adaptations may intentionally reuse an edit.
  3. Fix the batch contract before cutting — mode (distinct cuts, controlled hook test, or platform adaptations), count, target duration, platform, audience, language, and music bed. If more than one is missing, ask in one <widget> after loading widget-forms.
  4. Score every source asset for reuse headroom before planning. See references/variation-matrix.md. Reserve distinct hooks where the mode requires them; shared end cards and intentional shared bodies are allowed.
  5. Build the variation matrix before editing. For distinct cuts, plan different hooks and structural changes. For controlled hook tests, change only the hook and keep the body fixed. For platform adaptations, retain the approved content and adapt presentation. See references/variation-matrix.md.
  6. Plan the dedup budget with references/dedup-rules.md. Decide up front which dimensions carry the differentiation, and record the minimum separation each pair of cuts must clear.
  7. Get the matrix approved before cutting the whole batch. Show the plan as a table: cut number, hook, order signature, rhythm, duration, packaging, and the dimension that makes it different from every other cut in the batch.
  8. Cut the first variant end to end and verify it renders and exports before batching the rest. Do not generate eight timelines that share one unverified mistake.
  9. Cut remaining variants from the approved matrix. Reuse picks and trims according to the approved mode; distinct cuts and hook tests need different openings, while platform adaptations may share them.
  10. Name timelines so the batch is auditable: <topic>-v<NN>-<hook-label>, consistent across the batch.
  11. QA each cut individually and the batch as a set. See references/dedup-rules.md for the pairwise checks.
  12. Report per-cut timeline names, durations, the differentiation dimension for each, and which pair is the most similar — that is the pair the user should review first.

Plan Format

Present the batch as one table, not prose:

#Hook assetOrder signatureRhythmDurationPackagingDiffers by
v01clip_07 (product in hand)A-C-E-Bfast-cut, 1.2s avg28sbold captions + beat cutshook + order
v02clip_12 (before/after)C-A-B-Eslow open, ramp34sminimal captions + zoomhook + rhythm
  • Total outputs, platform, shared vs distinct music bed
  • Source pool size and reuse headroom
  • Dedup budget and minimum separation
  • Risks: thin pool, single-use assets, near-duplicate pairs

Rules

  • Distinct cuts and hook tests need different opening three seconds. Platform adaptations may share an opening and must be labeled as adaptations, not distinct edits.
  • For distinct cuts, differentiate on structure before decoration. Styling changes alone do not establish structural differentiation; these checks do not guarantee a platform's duplicate-detection outcome.
  • Do not pad a thin pool into N cuts. Report the honest maximum and ask.
  • Distinct cuts and hook tests need different hooks; platform adaptations may reuse the same approved hook.
  • Keep one variable dominant per cut. When everything differs slightly, nothing reads as different.
  • Batch output is not permission to lower the bar: each cut must stand alone as publishable.
  • Do not use captions or titles to invent claims the footage does not support, in any variant.
  • Report the most-similar pair explicitly. Hiding it defeats the point of the batch.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Batch Montage Variants AI skill do?

Turn one pool of existing project media into a batch of distinct, publishable montage cuts instead of a single hero edit. Use for batch montage, one-source-many-outputs, 一源多出, 批量混剪, 批量出片, 矩阵号, 多账号分发, variant batches, deduped cuts, 去重变体, 防搬运, or when the user asks for N different versions of the same footage.

Why use Batch Montage Variants on TypingMind?

Because you install it once and use it with any model. Batch Montage Variants 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 Batch Montage Variants in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/0xsline/OpenChatCut/tree/main/src/agent/skills/batch-montage-variants. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Batch Montage Variants?

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 Batch Montage Variants?

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

Is the Batch Montage Variants AI skill free?

Yes. It is published on GitHub by 0xsline under the AGPL-3.0 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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