agent-orchestration
AI DevKit · Proactively orchestrate running AI agents — scan statuses, assess progress, send next instructions, and coordinate multi-agent workflows. Use when users ask to manage agents, orchestrate work across agents, or check on agent progress.
SKILL.md
Full skill instructions
Agent Orchestration
Use only for multi-agent supervision: coordinating dependencies, polling progress, unblocking waiting agents, relaying outputs, resolving conflicts, and verifying completion across agents. For one-off list/detail/send/start/kill work, use $agent-management or $agent-communication.
Use $agent-management for safe agent selection and lifecycle actions. Use $agent-communication for list/detail/send mechanics. Use $verify before accepting any agent's completion claim.
Rules
- Own the loop until assigned work is complete, blocked, or stopped.
- Run
agent list --jsonbefore each pass; never assume names/statuses. - Inspect waiting, idle, unknown, missing, or stale agents before acting.
- Send self-contained instructions and avoid duplicate follow-ups.
- Sequence agents that touch the same files; relay only relevant upstream output.
- Escalate only for repeated failures, unresolved conflicts, product/business decisions, or destructive/shared/production/security-sensitive actions.
Loop
If the goal or agent ownership is unclear, run one scan/detail pass. Ask the user once only if context is still insufficient.
- Scan agents.
- Assess agents needing attention with
detail --tail 10. - Act: approve, clarify, correct, delegate, relay, verify, or escalate.
- Report one brief status line.
- Sleep 10-60s and repeat.
Completion
Finish when all assigned work is verified, blocked with a clear reason, or stopped by the user. Summarize per-agent outcomes, verification, unresolved issues, and next step.