| name | video-production |
| description | Plan and route programmable or automated video production across code-first, template-first, and hybrid content pipelines. Use when the user needs repeatable video generation, branded short-form content, personalized videos, social clip batches, captioned/localized variants, video APIs, or video creation from data and templates — even if they only say video production. Triggers on: Remotion, programmatic video, automated video creation, video API, personalized video, batch-create shorts, render videos from code, repurpose content into clips.
|
| allowed-tools | Write Read WebSearch WebFetch Task |
| compatibility | Canonical programmable-video / automated-video skill for the repo. Use this as the main entry point; `remotion-video-production` is the compatibility alias for legacy or explicitly Remotion-named requests.
|
| metadata | {"tags":"video, automation, remotion, short-form, content-ops, templates, react","platforms":"Claude, ChatGPT, Gemini, Codex","version":"2.0"} |
Video Production
Use this skill as the canonical programmable-video and automated-video production anchor for the repository.
The job is not to dump a generic storyboard or act like a manual editor tutorial. The job is to:
- normalize the production request,
- choose the best production mode,
- return one implementation-ready packet,
- leave explicit asset, QA, and handoff guidance.
Read references/production-modes.md, references/asset-and-qa-checklist.md, and references/handoff-boundaries.md before routing broad requests.
When to use this skill
- The user wants automated, repeatable, or template-driven video production
- The request involves short-form content ops, campaign variants, personalized videos, localized versions, or batched social assets
- The user mentions Remotion, rendering from code, video APIs, templates, or connecting app/product data into video output
- The workflow needs a production plan that can span generation, asset prep, QA, and a publishing handoff
When not to use this skill
- The job is purely a one-off manual edit in Premiere / After Effects / CapCut with no automation or repeatability goal
- The task is only final creative polish, color, taste-based pacing, or bespoke motion-design critique
- The user needs only transcript cleanup, clip selection, or direct publishing with no template/render layer
- The request is really an ad-strategy, content-strategy, or product-launch brief rather than a video-production workflow
Supported production modes
Use these as routing targets inside the skill:
1) Code-first programmable video
- Best when the user explicitly wants Remotion, React-based composition, dynamic data injection, or custom product-integrated rendering
- Default stack:
Remotion
2) Template/API automation
- Best when the user needs speed, bulk generation, localization, or personalized variants without owning a full rendering codebase
- Typical comparators: Shotstack, Creatomate, Bannerbear
3) Hybrid clip / repurposing pipeline
- Best when the source material is long-form audio/video and the main problem is extracting, captioning, resizing, and packaging clips at volume
- Common workflow shape: transcript or clip-selection tool + template/render layer + manual QA
4) Manual-finish hybrid
- Best when automated generation exists but editorial polish, captions, timing, or final approvals still need a human pass
- Use this when the right answer is not “fully automate everything” but “automate the repeatable 80%, then define the last-mile edit pass"
Instructions
Step 1: Normalize the production brief
Capture the request in this form before choosing a mode:
video_brief:
objective: launch | onboarding | social-clips | personalization | recap | education | ads | unknown
primary_output: mp4 | shorts | reels | stories | multiple
audience:
segment: "who will watch this"
stage: unaware | evaluating | customer | community | mixed | unknown
source_material:
type: script | existing-video | screenshots | product-data | slides | mixed | unknown
assets_available:
If the brief is incomplete, continue with explicit assumptions rather than stalling.
Step 2: Pick exactly one primary production mode
Choose the single mode that reduces ambiguity fastest.
- Code-first programmable video when the user explicitly wants Remotion, full code control, or deep app/data integration
- Template/API automation when speed, scale, or no-code / low-code batch generation matters most
- Hybrid clip / repurposing pipeline when the source is existing long-form media and the main job is repeatable short-form extraction
- Manual-finish hybrid when automation is useful but the real risk sits in last-mile captions, timing, approval, or platform-native polish
If more than one mode fits, choose the mode that best defines the next artifact and name the secondary handoff.
Step 3: Return one implementation-ready packet
Return one of these packet types:
video-production briefrender-stack recommendationasset + template packetrepurposing workflow packetQA + handoff checklist
Do not emit multiple half-built plans. Choose the single most useful packet for the ask.
Step 4: Add explicit asset, QA, and handoff logic
Every output must include:
- the chosen production mode,
- the recommended stack or workflow shape,
- the minimum required assets,
- the main QA risks,
- the next handoff after generation (manual polish, approval, publishing, analytics, etc.).
Step 5: Handle Remotion explicitly when relevant
If the user names Remotion or clearly needs code-first composition:
- say that the request fits the code-first programmable video mode,
- structure the output around Remotion scenes/templates/components/rendering,
- optionally note that
remotion-video-production is the compatibility alias for the same lane.
Step 6: Use this output structure
# Video Production Brief
## Scope
- Objective: ...
- Primary output: ...
- Audience: ...
- Source material: ...
- Confidence: high | medium | low
## Chosen mode
- Code-first programmable video | Template/API automation | Hybrid clip / repurposing pipeline | Manual-finish hybrid
## Recommended workflow
- Stack / workflow shape: ...
- Why this mode fits: ...
- What to avoid: ...
## Minimum asset packet
| Asset | Required | Notes |
|-------|----------|-------|
| ... | yes/no | ... |
## Build plan
1. ...
2. ...
3. ...
## QA risks
- ...
- ...
## Handoffs
- Next owner / tool / workflow: ...
- Why: ...
## Success check
- Render / approval / publishing signal: ...
Output format
Always return a short operator-style video production brief.
Required qualities:
- one primary production mode,
- clear asset minimums,
- explicit QA / last-mile risks,
- concrete next artifact and handoff,
- assumptions called out when context is missing.
Examples
Example 1: personalized campaign videos
Input
We need to generate personalized customer recap videos from product data and render hundreds every week.
Output sketch
- Primary mode:
Template/API automation
- Packet:
render-stack recommendation
- Notes dynamic data, template ownership, localization/QA, and publishing handoff
Example 2: explicit Remotion ask
Input
Build a Remotion workflow for a branded onboarding video with screenshots, captions, and 16:9 + 9:16 variants.
Output sketch
- Primary mode:
Code-first programmable video
- Packet:
asset + template packet
- Uses scenes/components/rendering language and mentions variant strategy
Example 3: repurposing long-form content
Input
Turn our webinar recordings into short captioned clips for Shorts and Reels every week.
Output sketch
- Primary mode:
Hybrid clip / repurposing pipeline
- Packet:
repurposing workflow packet
- Names transcript/clip selection, caption QA, safe-area checks, and final publish handoff
Best practices
- Separate the scalable generation layer from the last-mile editorial polish layer.
- Treat captions, safe areas, and aspect-ratio variants as first-class planning items, not afterthoughts.
- Choose the simplest stack that meets the repeatability requirement.
- Preserve a reusable asset/template packet so future variants are cheaper.
- Make the QA checklist explicit whenever short-form/social distribution is involved.
References
