By name: open-montage description: > OpenMontage™ — AI-powered end-to-end video production. Trigger when user says things like: "make a video", "create a montage", "produce a trailer", "edit this footage", "animated explainer", "podcast clip", "documentary", "cinematic trailer", "talking head video", "repurpose this content", "create a video from", "video about". version: 1.0.0 author: Pauli Second Brain™ | Kupuri Media™ invocation: model: default metadata: hermes: tags: [video, production, filmmaking, montage, editing, media, AI, animation] related_skills: []
OpenMontage™
Describe your vision. AI handles the rest — research, scripting, assets, editing, composition.
When to Activate
Activate this skill when the user:
- Says "make a video" or "create a montage"
- Asks for a cinematic trailer, animated explainer, or documentary
- Wants to repurpose a podcast, interview, or existing footage
- Mentions talking head, B-roll, or video editing
- Asks to analyze a reference video and create a variant
- Says "video about" followed by any topic
12 Production Pipelines
| Pipeline | Use When |
|---|---|
animated_explainer |
Concept or product needs visual explanation |
cinematic_trailer |
Hype/promo for product, event, or project |
documentary_montage |
Archive footage + narration storytelling |
talking_head |
Single-speaker scripted or interview format |
podcast_repurpose |
Convert audio podcast to video clip |
social_short |
15–60s vertical or square social content |
brand_story |
Company/founder narrative |
tutorial_walkthrough |
Step-by-step instructional |
news_recap |
Research-driven news summary video |
music_video |
AI-generated visual + audio sync |
product_showcase |
E-commerce or SaaS product demo |
event_highlight |
Conference, meetup, or launch recap |
Core Workflow
New video from description
- Ask user: topic, duration target, style, platform (YouTube / Instagram / LinkedIn)
- Select pipeline based on format
- Run
python -m open_montage.run --pipeline <name> --prompt "<description>" - Report: budget estimate → approval → render → deliver
Reference-driven variant
- User provides a video URL or file path
- Run analysis:
python -m open_montage.analyze --input <path> - Propose 3 differentiated variants
- User selects → full production run
Repurpose existing content
- Identify source type (podcast, interview, article)
- Run
python -m open_montage.repurpose --source <path> --format <pipeline> - Auto-generate script, B-roll selection, captions
Budget & Approval
- Every run produces a cost estimate before rendering
- Default approval threshold: $5.00 (configurable via
MONTAGE_SPEND_CAP) - Free-tier path available: Archive.org + NASA footage + Piper TTS + local Stable Diffusion
Quality Gates
OpenMontage validates automatically:
- Pre-render: script coherence, asset resolution, audio sync
- Post-render: frame inspection, audio levels, duration check
- Audit trail: full JSON checkpoint of every creative decision
Asset Sources (Free Tier)
- Video: Archive.org, NASA, Wikimedia Commons
- Images: Stable Diffusion (local), Wikimedia Commons
- TTS: Piper TTS (local, zero cost)
- Music: Royalty-free archive
Installation
git clone https://github.com/calesthio/OpenMontage
cd OpenMontage
pip install -r requirements.txt
npm install # Remotion compositor
Set OPEN_MONTAGE_PATH in ~/.hermes/.env to the install directory.