AGENT SKILL

task-orchestrator

Use when an org role acts as task orchestrator and must run multi-agent work: sequence phases, hand off context, enforce gates, retry or escalate. Covers small verifiable tasks, explicit state tracking and capped retries.

License 许可
Apache-2.0
所属仓库
monoes/monomind
最近更新
2026-11-10
Stars
★ 23
Forks
2

01 前言 Front Matter

name
task-orchestrator
tags
[ "engineering", "coordination", "agents" ]
tools
[ "monograph_query", "monograph_context", "monograph_impact" ]
source
https://github.com/monoes/monomind
license
Apache-2.0
description
Use when an org role acts as task orchestrator and must run multi-agent work: sequence phases, hand off context, enforce gates, retry or escalate. Covers small verifiable tasks, explicit state tracking and capped retries.

03 SKILL.md 正文

Task Orchestrator — Best Practices

Focus

Coordinates multi-step, multi-agent work end-to-end — sequencing phases, handing off context between specialists, enforcing quality gates, and deciding when to retry, escalate, or advance.

Best practices

  • Break work into the smallest independently-verifiable tasks, and require each to pass its own check before advancing — no batch-and-hope.
  • Give every handoff full context: what was done, what's expected next, and any prior feedback — agents fail when they receive vague instructions.
  • Track explicit state (current phase, task, retry count, blockers) so progress is inspectable at any point, not just inferred from logs.
  • Cap retries per task (e.g. 3 attempts) with a clear escalation path instead of looping indefinitely on a failing step.
  • Never advance a phase until its quality gate is met — skipping validation to "keep moving" compounds failures downstream.
  • Route each task to the specialist best suited for it rather than a generalist, and be explicit about why.
  • Prefer parallelizing independent tasks and serializing only genuine dependencies — false serialization wastes the most time in orchestration.
  • Report status with data (task N/M complete, retry count, blockers) rather than vague progress claims.

Common pitfalls

  • Advancing to the next phase because an agent claimed success, without independent verification of the actual output.
  • Serializing tasks that have no real dependency between them, when they could run concurrently.
  • Losing context on handoff — the next agent re-derives requirements from scratch instead of inheriting them.
  • Retrying a failing task with identical instructions instead of incorporating the specific failure feedback into the retry.
  • Treating "agent didn't error" as equivalent to "task is done correctly" — completion and correctness are different checks.

Tools & techniques

  • Explicit state machines/plans (phase → task → retry-count → status) rather than ad hoc coordination via chat history.
  • Dependency graphs to identify which tasks can run in parallel vs. must be sequential.
  • Quality gates with concrete pass/fail criteria (tests pass, QA sign-off, schema validation) attached to each phase transition.
  • Structured status/completion reports (tasks total/completed/blocked, retries used, next action) for both human and downstream-agent consumption.
  • Circuit-breaker style escalation: after N failed retries, stop looping and surface the blocker instead of silently continuing.

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