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Orchestrating Parallel Workers

skill-qte77-claude-code-plugins-orchestrating-parallel-workers · by qte77

Fan out tasks to parallel background agents with independent context windows. Use when work can be split into independent units that benefit from isolated execution.

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Install

$ agentstack add skill-qte77-claude-code-plugins-orchestrating-parallel-workers

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Parallel Worker Orchestration

Target: $ARGUMENTS

Decomposes work into independent units and dispatches them to parallel background agents. Each agent gets its own context window, preventing cross-contamination between unrelated tasks.

When to Use

  • Multiple independent tasks (e.g., create 3 unrelated files, research 4 topics)
  • Context-heavy work that would pollute the main session
  • Parallel file creation/editing in different areas of the codebase

When NOT to Use

  • Sequential dependent tasks (output of A feeds into B)
  • Single-file changes or trivial edits
  • Tasks requiring shared mutable state between workers
  • Fewer than 2 independent units

Workflow

1. Decompose

Split the user request into independent work units. Each unit must be:

  • Self-contained (no dependency on other units)
  • Well-scoped (clear input, clear expected output)
  • Worth isolating (non-trivial enough to justify agent overhead)

2. Plan

For each unit, define:

| Field | Description | |-------|-------------| | Name | Short identifier (e.g., worker-auth, worker-docs) | | Type | worktree for file changes, shared for read-only research | | Prompt | Complete, self-contained instructions (no shared context) | | Output | What the agent should produce and where |

3. Dispatch

Launch all agents in a single message with multiple Agent tool calls. This ensures true parallel execution. Each agent prompt must include:

  • Full context needed (file paths, requirements, constraints)
  • Expected output format and location
  • No references to other agents or their work

4. Track

Use TaskCreate for each dispatched unit to give the user visibility:

TaskCreate: "worker-auth: implement OAuth module" — status: in_progress
TaskCreate: "worker-docs: write API reference" — status: in_progress

Update tasks as agents complete via TaskUpdate.

5. Collect

After all agents finish:

  • Read each agent's output
  • Validate completeness and correctness
  • Synthesize a summary for the user
  • Handle any git operations (worktree agents lack Bash)

Agent Isolation Modes

| Mode | Use when | File access | |------|----------|-------------| | worktree | Agent creates/edits files | Isolated copy, lead merges | | shared | Agent only reads/researches | Shared repo, no writes |

Constraints

  • Worktree agents lack Bash — the lead agent handles all git operations

(commits, merges, branch management) after collecting results

  • Max ~5 parallel agents — diminishing returns beyond this; context

scheduling overhead increases

  • Self-contained prompts — each agent gets a complete prompt with all

necessary context; no shared memory between agents

  • Lead agent owns coordination — only the lead creates, tracks, and

collects; agents do not spawn sub-agents

Anti-Patterns

  • Dispatching a single trivial task as a "parallel" worker (just do it inline)
  • Sharing mutable state between agents (they cannot see each other's changes)
  • Vague prompts that require agents to ask clarifying questions
  • Dispatching dependent tasks in parallel (use sequential execution instead)
  • Skipping the collect phase (results must be validated and synthesized)

Quality Check

  • Each agent prompt is self-contained and actionable
  • No circular or hidden dependencies between units
  • All agent results collected and validated before reporting to user
  • Git operations performed by lead after collection, not by workers

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.