ci-cd-and-automation for Openclaw

1. Ensure your AI agent environment supports SKILL.md-based skills such as Claude Code, Cursor, Codex, or Gemini CLI.
2. Confirm your operating system is supported: Windows, macOS, or Linux.
3. Make sure the agent runtime already includes a working LLM API connection.
4. Provide a valid source dataset or input content before triggering the skill.
5. Configure API credentials or access tokens if your deployment requires authenticated services.
6. Validate that any referenced files, paths, and dependencies exist before execution.

Recommended checks:

# Verify your runtime and dependencies are available
node --version
python --version

# Example: confirm environment variables are set
export API_KEY="your-api-key"
export SKILL_MODE="ci-cd-and-automation"

If your workflow invokes external commands, test them in a sandbox first and confirm the command syntax, permissions, and timeout settings before production use.

Openclaw Skills setup tip: keep inputs small during initial validation, then scale to batch execution once the workflow is verified.

The skill uses a lightweight, structured request/response schema centered on input content and execution metadata.

Input Parameters

Output Shape

{
  "success": true,
  "data": {
    "result": "ci-cd-and-automation processing result",
    "metadata": {
      "skill": "ci-cd-and-automation",
      "version": "1.0.0",
      "pricing_tier": "L3-专业级"
    }
  },
  "error": null
}

Metadata Taxonomy

• skill: fixed identifier for the skill instance
• version: semantic version string
• pricing_tier: operational tier used for billing and packaging
• success: boolean status flag
• error: nullable error object or message

Operational Data Characteristics

• Supports structured input and structured output for predictable automation.
• Compatible with multiple source formats, including JSON, text, and Markdown.
• Designed for batch operations and result export.
• Built to preserve traceability through source-based verification and execution metadata.

Error Taxonomy