AI Agent Skills for Video Production: Automate Your Entire Pipeline

AI video production involves dozens of repetitive steps: writing scripts, generating images, creating video clips, compositing audio, adding metadata, and uploading to platforms. Each step has its own tools, APIs, and configuration. AI agent skills turn these steps into executable, repeatable instructions that coding agents can run on your behalf with a single command.

This guide covers what agent skills are, how they work, and how to set them up with Claude Code and OpenClaw to automate your entire video pipeline.

What Are AI Agent Skills?

An AI agent skill is a machine-readable instruction file (typically named SKILL.md) that tells an AI coding agent exactly how to execute a specific task. Think of it as a recipe: it declares the inputs required, the steps to follow, the tools to call, and the expected output. The agent reads the file, interprets the instructions, and carries out the work autonomously.

Skills differ from traditional scripts or shell commands in an important way. A shell script executes a fixed sequence of operations. A skill file is interpreted by an AI agent that can adapt, handle edge cases, make decisions within the boundaries you define, and chain together API calls, file operations, and CLI commands without you writing glue code. The agent is the runtime.

The SKILL.md format is supported by several AI coding agents including Claude Code, OpenClaw, and Codex CLI. Each agent reads the same file format, which means skills are portable across tools. You write the skill once and run it wherever your preferred agent is installed.

How Agent Skills Work

The execution flow for an agent skill follows a predictable pattern:

1. User triggers the skill. You invoke a skill by name, either through a slash command (like /seedance-video-generator in Claude Code) or through a platform interface in OpenClaw.

2. Agent reads the SKILL.md file. The agent locates the skill file in its configured skills directory, parses the frontmatter for metadata, and reads the instruction body.

3. Agent plans the execution. Based on the instructions, the agent determines which tools it needs (API calls, file reads/writes, CLI commands, browser actions) and in what order to run them. If inputs are missing, the agent prompts you for them.

4. Agent executes the steps. The agent works through each step: calling APIs, writing files to disk, running shell commands, processing data, and handling errors as specified in the skill instructions.

5. Agent returns the result. Once all steps are complete, the agent presents the output (a generated video file, a metadata JSON, a set of thumbnails) and reports any issues encountered during execution.

The key advantage is composability. You can chain skills together so that the output of one skill becomes the input of the next. A script-to-storyboard skill produces a shot list. That shot list feeds into a video generation skill. The generated clips feed into an editing skill. The edited video feeds into a metadata and upload skill. Each skill is a self-contained unit, and the agent orchestrates the chain.

Available Video Production Skills

The following skills cover the core stages of an AI video production pipeline. You can use them individually or chain them together for end-to-end automation.

Each skill is a standalone SKILL.md file. You can install only the skills you need and add more over time as your workflow evolves.

Getting Started with Claude Code

Claude Code is Anthropic's CLI tool for running AI agents in your terminal. Here is how to set up video production skills.

Step 1: Install Claude Code.

If you have not already installed Claude Code, add it globally via npm:

npm install -g @anthropic-ai/claude-code

Verify the installation by running claude --version in your terminal.

Step 2: Create the skills directory.

Claude Code looks for skills in the .claude/skills/ directory within your project or home directory. Create it if it does not exist:

mkdir -p .claude/skills

Step 3: Add SKILL.md files.

Place each skill file into the skills directory. For example, to add the video generator skill:

.claude/skills/
  seedance-video-generator/
    SKILL.md
  youtube-metadata-generator/
    SKILL.md
  script-to-storyboard/
    SKILL.md

Each subdirectory contains a single SKILL.md file with the full instructions for that skill.

Step 4: Configure environment variables.

Most video production skills require API keys. Set them in your shell profile or a .env file:

export SEEDANCE_API_KEY="your-seedance-api-key"
export ELEVENLABS_API_KEY="your-elevenlabs-api-key"
export YOUTUBE_API_KEY="your-youtube-api-key"

Step 5: Invoke the skill.

Run Claude Code and trigger a skill by name:

claude
> /seedance-video-generator

Claude Code reads the SKILL.md, asks for any required inputs (prompt text, aspect ratio), and executes the pipeline. The generated video is saved to your configured output directory.

Getting Started with OpenClaw

OpenClaw is an open-source platform for managing and running AI agent skills through a web interface and CLI. It provides a skill registry, execution history, and team collaboration features.

Step 1: Install the OpenClaw CLI.

npm install -g openclaw

Step 2: Initialize your workspace.

openclaw init

This creates an openclaw.config.json file and a skills/ directory in your project.

Step 3: Import skills from the registry.

OpenClaw maintains a public registry of community-contributed skills. Import video production skills directly:

openclaw install seedance-video-generator
openclaw install youtube-metadata-generator
openclaw install script-to-storyboard

Step 4: Run a skill.

openclaw run seedance-video-generator --prompt "A drone shot of a coastal city at golden hour"

OpenClaw also supports chaining skills in a pipeline configuration file, which lets you define multi-step workflows declaratively.

Creating Custom Skills

Every SKILL.md file follows a consistent format with frontmatter metadata and an instruction body. Here is the structure.

Frontmatter:

---
name: my-custom-skill
description: A brief explanation of what this skill does
version: 1.0.0
inputs:
  - name: prompt
    type: string
    required: true
    description: The text prompt for video generation
  - name: aspect_ratio
    type: string
    required: false
    default: "16:9"
    description: Output aspect ratio
outputs:
  - name: video_file
    type: file
    description: The generated MP4 video
---

Instruction body:

After the frontmatter, write the execution steps in plain Markdown. The agent interprets these as instructions to follow in order.

## Execution Steps

1. Validate that the `prompt` input is at least 10 characters long. If not, ask the user to provide a more descriptive prompt.

2. Call the Seedance 2.0 API text-to-video endpoint:
   - Endpoint: `POST https://api.seedance.ai/v2/generate/text`
   - Include the prompt, aspect_ratio, and duration (default 4 seconds) in the request body.
   - Use the SEEDANCE_API_KEY environment variable for authentication.

3. Poll the task status endpoint every 5 seconds until the status is "completed" or "failed".

4. If the generation succeeded, download the video from the `video_url` field and save it to `./output/{timestamp}-generated.mp4`.

5. Return the file path and generation metadata (task ID, duration, resolution) to the user.

## Error Handling

- If the API returns a 429 rate limit error, wait 30 seconds and retry up to 3 times.
- If the generation fails, report the error message and suggest the user simplify the prompt.

## References

- [Seedance 2.0 API docs](/blog/seedance-2-0-api)
- [Prompt writing guide](/blog/seedance-prompt-guide)

The key principle is clarity. Write your instructions as if you are explaining the task to a competent developer who has never seen your codebase. Spell out API endpoints, file paths, error handling rules, and expected outputs.

Real-World Automation Examples

Solo Creator: Daily YouTube Shorts Automation

A solo creator produces one YouTube Short every day. Their pipeline involves writing a script, generating a video clip, adding voiceover, creating a thumbnail, and uploading with optimized metadata.

Without skills, this takes 2-3 hours of manual work per video. With a skill chain, the creator runs a single command:

claude
> Run my daily-short pipeline for today's topic: "3 tips for better AI prompts"

The agent executes the chain: script-to-storyboard produces the shot list, seedance-video-generator creates the clip, audio-narration-generator adds voiceover, youtube-metadata-generator creates the title and tags, and the creator reviews the final output before uploading. Total hands-on time drops to about 15 minutes for review and approval.

Agency: Batch Video Production for Clients

A video marketing agency produces 20-30 short-form videos per week for multiple clients. Each client has brand guidelines, preferred styles, and target platforms.

The agency creates client-specific skill configurations that include brand colors, logo overlays, tone-of-voice settings, and platform targets. A project manager triggers batch generation:

openclaw run batch-pipeline --config clients/acme-corp.json --count 5

The agent generates five videos following ACME Corp's brand guidelines, creates platform-specific crops (16:9 for YouTube, 9:16 for Shorts and Reels), and produces metadata for each platform. The team reviews outputs in the OpenClaw dashboard and approves them for delivery.

Developer: API-First Pipeline with Webhook Triggers

A developer integrates video generation into a SaaS product. When a user submits a prompt through the web app, a webhook triggers the video pipeline.

The developer writes a skill that listens for incoming webhook payloads, validates the request, generates the video via the Seedance API, uploads the result to cloud storage, and sends a notification back to the user. The skill runs as a background job managed by the agent, with error handling and retry logic defined in the SKILL.md file.

Webhook received → Validate payload → Generate video → Upload to S3 → Notify user

This approach keeps the pipeline logic readable and maintainable inside a skill file rather than buried in application code.

Limitations and Best Practices

What Agents Cannot Do

AI agents are powerful executors, but they have real limitations in the context of video production:

• Subjective creative judgment. An agent can generate a video from a prompt, but it cannot tell you whether the result is aesthetically good. You still need human review for creative quality.
• Real-time monitoring. Agents execute tasks and return results. They do not sit and watch a rendering process in real time. Long-running tasks rely on polling or callbacks.
• Complex editing decisions. Cutting a 10-minute video down to a 60-second highlight reel requires editorial judgment that agents cannot reliably provide. Use agents for mechanical editing tasks (trimming, resizing, format conversion) rather than creative editing.
• Platform-specific nuances. Each social media platform has evolving rules about content, format, and metadata. Agents follow the instructions in your skill file, so you need to keep skills updated as platforms change.

Best Practices

1. Version your skills. Keep SKILL.md files in version control. When you update an API endpoint or change a workflow step, the change is tracked and reversible.

2. Test incrementally. Run each skill individually before chaining them together. Verify that the inputs and outputs match between connected skills.

3. Keep skills focused. A single skill should do one thing well. Prefer a chain of three focused skills over one monolithic skill that tries to handle the entire pipeline.

4. Define clear error handling. Specify what should happen when an API call fails, when a file is missing, or when an input is invalid. Agents follow your error handling instructions literally.

5. Use environment variables for secrets. Never hardcode API keys in SKILL.md files. Reference environment variables and document which keys each skill requires.

6. Document your inputs and outputs. Clear frontmatter makes skills self-documenting. Another team member (or your future self) should be able to understand what a skill does from the frontmatter alone.

7. Review agent output before publishing. Automate generation and preparation, but keep a human in the loop for the final publish step. This catches quality issues and prevents accidental uploads.

FAQ

What is a SKILL.md file?

A SKILL.md file is a Markdown document that contains machine-readable instructions for an AI coding agent. It declares the skill's name, description, required inputs, expected outputs, and step-by-step execution instructions. The agent reads this file and carries out the work described in it.

Do I need coding experience?

Basic familiarity with the command line and environment variables is helpful, but you do not need to be a programmer. SKILL.md files are written in plain Markdown with clear instructions. If you can follow a recipe, you can write and use a skill. For more advanced customizations (custom API integrations, webhook handling), some development experience is useful.

Which AI agents support SKILL.md?

Claude Code (by Anthropic), OpenClaw, and Codex CLI all support the SKILL.md format. The core file structure is the same across agents, though each agent may have additional features or configuration options. Skills written for one agent generally work with others with minimal adjustment.

Can agents call external APIs?

Yes. Agents can make HTTP requests to any API you configure them to use, including Seedance, ElevenLabs, YouTube Data API, cloud storage services, and others. You specify the API endpoints and authentication method in the SKILL.md file, and the agent handles the requests.

How do I debug a failing skill?

Start by checking the agent's output log for error messages. Common issues include missing environment variables (API keys not set), incorrect API endpoints, and malformed input data. Run the failing step in isolation to narrow down the problem. Most agents support verbose logging modes that show each step as it executes.

Are agent skills free to use?

The agent tools themselves have varying pricing. Claude Code requires an Anthropic API subscription. OpenClaw offers a free tier for individual use. The skills are just instruction files and cost nothing. However, the underlying services that skills call (Seedance API, ElevenLabs, YouTube API) have their own pricing. Your total cost depends on which APIs your skills invoke and how often.

Can I share skills with others?

Yes. Since skills are plain Markdown files, you can share them through Git repositories, the OpenClaw public registry, or any file-sharing method. Shared skills should document their required environment variables and API dependencies so that others can set them up correctly.

Related Articles

Continue building your AI video production pipeline with these resources:

• Seedance 2.0 API: Complete Integration Guide — Technical reference for the Seedance video generation API
• Seedance 2.0 Tutorial: Complete Guide — Get started with AI video generation from scratch
• Seedance Prompt Guide — Write better prompts for higher quality video output
• Seedance Pricing Breakdown — Plan your budget for API-based video generation

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