What problem does it solve? Coordinating multi-step automation across models, scripts, and services requires manual orchestration, dependency tracking, and error handling. This Skill lets you define jobs and steps in YAML, wire them together with dependency conditions, validate the DAG, execute runs, and inspect run history and logs through the swamp CLI. ## Core Features & Use Cases - Workflow lifecycle management: Search, create, edit, validate, evaluate, run, and delete workflows, with auto-assigned IDs and schema-first scaffolding. - Dependency orchestration: Define job and step dependencies with trigger conditions, forEach iteration, concurrency limits, allow-failure steps, and nested workflow calls (max depth 10). - Run observability: Query run history, fetch the latest run, and view per-step logs for debugging failures. - Use Case: Build a deploy pipeline where a build job compiles code, a deploy job runs only on success with environment inputs, and a scheduled cron trigger re-runs the workflow automatically via swamp serve. ## Quick Start Ask the AI to create a new swamp workflow named my-deploy, add build and deploy jobs to the generated YAML, validate it, and run it with an environment input.
