Orchestrates streaming multi-agent workflows by passing outputs between stages.
npx skills add https://github.com/qiphon/learn --skill stream-chain-qiphon
Please help me install this Agent Skill. Skill: stream-chain Source: https://github.com/qiphon/learn/tree/main/.opencode/skills/stream-chain Command: npx skills add https://github.com/qiphon/learn --skill stream-chain-qiphon
Stream-JSON chaining enables seamless coordination of multiple agents, passing outputs along a pipeline to build complex, sequential workflows.
claude-flow stream-chain run "Analyze data" "Transform results" "Validate output"
High-intent search queries and answers about installing and using this skill.
Multi-agent workflow coordination passes complete step outputs between stages to build sequential pipelines. You orchestrate data processing and automation tasks by configuring contextual data flow across custom chains or predefined pipelines with streaming execution.
Streaming pipeline orchestration executes sequential stages by passing the full output of one step as context to the next. You need it for API orchestration and data transformation tasks requiring contextual data flow across multiple agents in real time.
You run a custom prompt chain by executing a command with quoted sequential steps, such as passing "Analyze data" "Transform results" "Validate output". This passes each step's output as context to the next stage without additional setup beyond the host CLI.
Yes, predefined pipelines execute common workflows with minimal setup. You use battle-tested templates to orchestrate data processing and API automation directly through the host CLI, requiring no additional dependencies or environment configuration.
Multi-agent streaming workflow execution supports configurable timeouts for sequential stages. You can orchestrate custom chains and predefined pipelines while controlling execution limits to prevent hanging during API orchestration and data transformation tasks.
Custom chains let you design bespoke prompt sequences where each step uses the previous output as context. Predefined pipelines provide battle-tested templates for common workflows, both executing sequential stages in a streaming fashion with configurable timeouts.
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