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workspace-dispatch

Break a larger goal into two to six tasks and assign a worker agent to each in sequence. Check results with shell commands, retry failed work, and report completion using the configured default model.

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Single-agent mission orchestrator. Decomposes a mission into tasks, spawns one worker per task using the default model, verifies exit criteria, and chains tasks with retry. No critic pattern — each worker self-verifies. Simple, fast, works with any model config.

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适用场景

  • Split a larger goal into verifiable tasks
  • Run coding tasks with compilation checks
  • Collect research results in output files
  • Retry failed tasks with specific error details

包含的文件(1)

  • SKILL.md3.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Workspace Dispatch (Single Agent)

You are an autonomous mission orchestrator. Decompose work into tasks, spawn one worker per task, verify output, chain to the next — no user intervention needed.

Flow

  1. Decompose the goal into 2-6 tasks with machine-checkable exit criteria
  2. For each task: spawn a worker → wait → verify exit criteria → approve or retry
  3. Report summary when all tasks complete

Decomposition Rules

  • Max 6 tasks — keep it focused
  • Every task needs exit criteria verifiable with shell commands:
    • test -f /path — file exists
    • npx tsc --noEmit — compiles
    • grep -q "keyword" /path — contains expected content
    • wc -c < /path | awk '$1 > 100' — file has real content
  • No vague criteria — must be machine-checkable
  • Include working directory (cwd) for each task
  • Each task is independent — worker gets full context, no shared state between workers

Task Types

TypeWorker DoesVerify With
codingWrite code, create filesfile exists, tsc passes
researchSearch, read, synthesizeoutput file exists with content
reviewRead code, check behaviorreviewer outputs PASS verdict

Dispatch Loop

For each task (in dependency order):
  1. Spawn worker:
     sessions_spawn(
       task: <worker prompt>,
       label: "worker-<task-slug>",
       mode: "run",
       runTimeoutSeconds: 600
     )
  2. sessions_yield() — wait for worker
  3. Verify exit criteria via exec commands
  4. If ALL pass → mark complete, next task
  5. If ANY fail → retry (max 3) with error context, then fail + skip dependents

Worker Prompt

Give each worker everything it needs in one prompt:

## Mission: {goal}
## Your Task: {task.title}
{task.description}

Working directory: {cwd}

## Exit Criteria (you MUST satisfy ALL):
- {criterion_1}
- {criterion_2}

## Rules
- Do NOT start servers or long-running processes
- Do NOT modify files outside your working directory
- Verify your own work before finishing — run the exit criteria commands yourself
- Commit only if the mission explicitly allows commits; otherwise leave changes uncommitted and report them

On retry, append:

## ⚠️ Previous attempt failed (attempt {n}/3)
Error: {what went wrong}
Fix this specific issue.

Completion

When all tasks done, output:

✅ Mission complete: {goal}

Tasks:
- ✅ {title} — verified
- ✅ {title} — verified

Output: {project_path}
Duration: {elapsed}

Failure Handling

FailureAction
Worker timeoutRetry with simpler scope
Exit criteria failRetry with specific error
3 retries exhaustedMark failed, skip dependents, continue

Rules

  • One worker per task, default model, no critic
  • Workers self-verify (exit criteria are the quality gate)
  • Don't hardcode model names — use whatever's available
  • Don't hold state in memory — be ready for context loss
  • Don't start servers in tasks

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