Orchestrates multi-agent workflows defined by DAGs, managing dependencies, retries, and status tracking.
npx skills add https://github.com/elcoosp/elcoosp-skills --skill sch
Please help me install this Agent Skill. Skill: sch Source: https://github.com/elcoosp/elcoosp-skills/tree/main/virtual-saas-team/agents/core/sch Command: npx skills add https://github.com/elcoosp/elcoosp-skills --skill sch
This Skill acts as the operational engine for executing complex workflows, ensuring tasks are run in the correct order, dependencies are met, and failures are handled gracefully, thereby transforming plans into executed work.
Use the sch skill to ingest a new DAG artifact named 'cdr-001-project-launch' and begin task execution.
High-intent search queries and answers about installing and using this skill.
Multi-agent workflow orchestration is managed by ingesting a DAG artifact to queue tasks, resolving dependencies, and dispatching execution to appropriate agents. The system operates as a deterministic execution engine, ensuring reliable task dispatch and status tracking.
Task execution handles failures through robust retry policies and timeout monitoring. Persistent failures are escalated automatically, ensuring complex workflows continue running reliably even when individual agents encounter execution errors.
Rate limiting and execution cost tracking are supported natively. The orchestration engine enforces rate limits during task dispatch and provides real-time status updates while monitoring resource consumption across the workflow.
Executing a new project plan requires ingesting the DAG artifact into the scheduler. The system then automatically queues all tasks, resolves necessary AI models for each step, and dispatches them to relevant agents for execution.
Task dispatch requires an event bus and agent state store to maintain deterministic execution and reliable status tracking. This architecture ensures the scheduler accurately monitors progress and coordinates dependencies across multi-agent workflows.
Model resolution for dynamic task queues depends on the scheduler's ability to match task requirements with available agents. Limitations arise if necessary AI models are unavailable or if rate limits restrict concurrent task execution during peak workflow processing.
WenyuChiouSplit a high-level goal into a DAG of subtasks and emit .coord/plan.yml plus per-agent task files.
imbue-aiInspect running agent states and logs within the mng framework.
Kit4SomeAutomate 24/7 agent polling, task decomposition, dispatch, and result persistence.
SteelMorganGenerate a JSON task breakdown with dependencies and self-checks for linear execution.
