Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/dojocodinglabs/remotion-superpowers/generate-clipgit clone --depth 1 https://github.com/DojoCodingLabs/remotion-superpowersWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/dojocodinglabs/remotion-superpowers/generate-clip)<a href="https://agentmods.dev/commands/dojocodinglabs/remotion-superpowers/generate-clip"><img src="https://agentmods.dev/badge/commands/dojocodinglabs/remotion-superpowers/generate-clip.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00032 | $0.01071 |
| Opus 5 | $0.00016 | $0.00535 |
| Sonnet 5 | $0.00006 | $0.00214 |
| Haiku 4.5 | $0.00003 | $0.00107 |
Grade A, and why
generate-clip scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -o public/footage/[descriptive-name].mp4 "[replicate_output_url]" How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Clip — AI Video Generation
You are helping the user generate custom AI video clips for their Remotion project. This creates unique footage that doesn't exist anywhere — going beyond stock footage.
Workflow
1. Understand the Need
Ask the user:
- What should the clip show? — A scene, product, animation, nature, abstract?
- Duration — How long? (typically 3-10 seconds for AI clips)
- Style — Cinematic, drone shot, close-up, slow motion, time-lapse?
- Source — Text-to-video (from description) or image-to-video (animate a still)?
2. Craft the Video Prompt
Good video generation prompts include:
- Scene description with action/motion
- Camera movement (pan, zoom, dolly, crane, static)
- Lighting and mood
- Speed (slow motion, normal, time-lapse)
Good prompt examples:
- "Slow overhead crane shot of a modern city at golden hour, cars moving on streets, warm cinematic lighting"
- "Close-up of coffee being poured into a white mug, steam rising, soft morning light, shallow depth of field"
- "Abstract fluid art animation, flowing metallic gold and deep blue paint, mesmerizing slow motion"
- "Drone flying through a forest canopy, sunlight filtering through leaves, cinematic nature footage"
3. Generate via remotion-media
Use remotion-media generate_video:
- prompt: [crafted prompt]
- project_path: [project root path]
The clip is saved to the project's public/ directory.
4. Generate via Replicate MCP (if available)
If the user has REPLICATE_API_TOKEN and wants specific models, use the Replicate MCP tools:
Text-to-Video:
# Wan 2.5 (open source, fast)
replicate_create_prediction:
model: "wan-video/wan-2.5-t2v-fast"
input:
prompt: "[crafted prompt]"
num_frames: 81
resolution: "480p"
Image-to-Video (animate a still):
# Kling 2.6 (cinematic, top-tier I2V)
replicate_create_prediction:
model: "kwaivgi/kling-v2.6-pro"
input:
image: "[image_url]"
prompt: "[motion description]"
duration: 5
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 119 lines · 32 tokens per session scan A c7933a4453b6
generate-clip is a command published in the GitHub repository DojoCodingLabs/remotion-superpowers (116 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 1,071 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
generate-all
Generate creative assets for all posts in the current month's calendar.
edit-image
Edit a generated image — adjust background, lighting, colors, or composition.
index-assets
Index or re-index a brand's visual asset library.
render-carousels
Render multi-slide carousels from HTML templates via Playwright.
edit-post
Edit an existing post's copy, visual direction, or metadata.
generate-post
Generate creative for a single post by ID.