Chain AI agents into sequential, branching, or parallel-then-merge workflows.
npx skills add https://github.com/suhwan/claude-registry --skill pipeline-suhwan
Please help me install this Agent Skill. Skill: pipeline Source: https://github.com/suhwan/claude-registry/tree/main/skills/orchestration/pipeline Command: npx skills add https://github.com/suhwan/claude-registry --skill pipeline-suhwan
Chaining multiple AI agents into a coherent, maintainable workflow eliminates manual handoffs, reduces errors, and speeds up complex projects by passing results from one stage to the next.
Define your desired agent sequence (for example, explore -> architect -> executor) and run the pipeline to execute the workflow.
High-intent search queries and answers about installing and using this skill.
To chain multiple AI agents into a sequential workflow, you define an ordered sequence of stages with explicit data passing. The pipeline connects one agent's output directly to the next, forming a linear progression from initial input to final result.
Parallel-then-merge agent orchestration runs multiple stages concurrently and then merges their results for final execution. This pattern speeds up complex data processing by utilizing simultaneous operations before consolidating outputs into a final agent stage.
Branching and conditional routing direct outputs to different agents based on prior results. You configure the pipeline with valid prompts that define stages and data contracts, allowing the workflow to dynamically route data to specific downstream agents.
Yes, explicit frontmatter in SKILL.md is required to define agent chaining pipelines. You must also provide valid prompts that establish stage definitions, data contracts, and error handling before executing the workflow.
Common use cases for branching AI agent pipelines include code review and implementation workflows, debugging processes, and research-to-action sequences. These patterns eliminate manual handoffs by automatically passing results between specialized agents.
melikhanmutluMap LLM-suited tasks into disciplined project architectures with prototype-driven evaluation.
chanhee-kangOrchestrate AI agents into sequential, branching, and parallel-merge pipelines.
gajjalaashok75-UICoordinate multiple specialized agents to analyze tasks and consolidate findings into prioritized recommendations.
sswapnil2Dispatch parallel agents for independent tasks without shared state.
