Guides
By AnyCap Team
How to add video generation
to an AI agent
AI agents can write code, answer questions, and automate workflows, but they cannot create videos on their own. AnyCap adds video generation capabilities through a skill file and CLI so the agent can discover models, submit jobs, and wait for results without leaving the workflow.
This guide walks through adding video generation to any AI agent, including Claude Code, Cursor, and Codex. It covers setup, model discovery, prompt structure, and the async workflow that turns a plain-language request into a finished video asset.
The practical goal is simple: after setup, your agent should be able to treat video generation like any other tool call. A user can ask for a product demo, motion concept, or short social clip, and the agent can handle the end-to-end execution path for them.
What you need
- An AI agent that can run shell commands (Claude Code, Cursor, Codex, and similar tools)
- Node.js 18+ for skills.sh and npm-based installation flows
- A browser for the one-time login flow
- A clear prompt or a reference image for the first generation test
Video generation is asynchronous. The CLI submits the request, polls until the job is finished, and returns the result URL. Your agent can manage that polling loop automatically after setup.
Install the AnyCap skill
# For Claude Code
npx -y skills add anycap-ai/anycap -a claude-code -y
# For Cursor
npx -y skills add anycap-ai/anycap -a cursor -y
This places the AnyCap skill into your agent's skills directory. The file gives the agent discoverable instructions for video generation so it knows which commands to run instead of improvising from docs.
Install the AnyCap CLI
curl -fsSL https://anycap.ai/install.sh | sh
Or use npm install -g @anycap/cli. The CLI is the execution surface that actually submits and tracks multimodal jobs.
Log in
anycap login
This opens the browser-based auth flow. One login covers video generation and the rest of the AnyCap capability set, which keeps agent workflows simpler later on.
Discover available video models
anycap video models
This lists the available video models and is worth doing before the first run. Agents can use that output to match the request to the best available model instead of hardcoding the same choice every time.
Generate your first video
anycap video generate --model veo-3.1 --prompt "a drone shot over a mountain lake at sunrise"
The CLI submits the request and polls until the video is ready. Once the job finishes, it returns the resulting URL so the agent can continue with posting, embedding, or review steps.
Use video generation in agent workflows
Once setup is complete, your agent can generate videos from natural-language requests. It can select a model, draft a prompt, submit the generation job, and wait for completion without manual command-by-command supervision.
# Ask your agent naturally
"Create a 5-second product demo video for our new feature"
# Or generate from a reference image
"Animate this mockup into a short onboarding walkthrough"
The agent reads the skill, uses the CLI as the runtime, and manages the asynchronous generation flow end to end.
Where video generation helps most
Product demos
Turn release notes or a feature summary into a short product demo video that an agent can generate as part of launch prep.
Marketing experiments
Ask the agent for multiple short variations of an ad concept, hero animation, or social teaser without leaving the terminal workflow.
Design handoff
Animate a static mockup into a walkthrough video so teams can review motion and narrative earlier in the design cycle.
How to get better video outputs
Prompt quality matters more for video than for many text tasks because the model has to infer shot composition, movement, duration, and visual continuity. A short vague prompt often still works, but a structured prompt usually gives more repeatable results.
For best results, tell the agent the subject, camera motion, scene, lighting, tone, and length. If you have a visual starting point, use a reference image so the agent can anchor the video around a specific layout or art direction.
A good pattern is to ask the agent for a draft prompt first, review it, and then have it run the generation command. That keeps the workflow collaborative while still letting the agent handle the operational details.
Common setup and workflow mistakes
Skipping model discovery
Different models have different strengths, durations, and motion styles. Running anycap video models first helps the agent choose a model that matches the request.
Using underspecified prompts
A request like 'make a product video' is usually too broad. Add scene context, pacing, camera direction, and intended use so the agent can build a stronger prompt.
Treating video generation like a synchronous command
Video jobs take time. The agent should submit the request, keep polling, and only continue the workflow after the result URL is available.
FAQ
Which video models does AnyCap support?
AnyCap provides access to multiple video generation models including Veo 3.1, Kling 3.0, and others. Run anycap video models to see the currently available options and choose based on motion style, duration, and output goals.
How long does video generation take?
Video generation is asynchronous. The CLI submits the request and polls for completion. Many jobs finish within tens of seconds to a few minutes depending on the model, queue state, and settings. Agents can handle that waiting loop for you.
Can I generate videos from reference images?
Yes. Some models support image-to-video generation. Upload a reference image using AnyCap Drive or provide the relevant asset path so the agent can anchor the motion around an existing design or frame.
Does this work with agents other than Claude Code?
Yes. Any agent that can run shell commands can use AnyCap video generation, including Cursor, Codex, and similar tools. Install the corresponding skill target and the rest of the workflow stays nearly the same.