sunnydata-parallel-agents — for Claude Code sunnydata-parallel-agents, ai-brainstorming, community, for Claude Code, ide skills, SUPERPOWERS-EXTRAS-USAGE-zh-TW.md, Dispatching, Parallel, Overview, delegate

v1.0.0

このスキルについて

適した場面: Ideal for AI agents that need 繁體中文說明 :見 superpowers-extras-usage-zh-tw.md(全系列 sp- 摘要)。. ローカライズされた概要: 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。 Dispatching Parallel Agents Overview You delegate tasks to specialized agents with isolated context. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

機能

繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。
Dispatching Parallel Agents
Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.
digraph when to use {
"Multiple failures?" [shape=diamond];

# Core Topics

Zenobia000 Zenobia000
[0]
[0]
Updated: 4/16/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
64
Canonical Locale
zh
Detected Body Locale
zh

適した場面: Ideal for AI agents that need 繁體中文說明 :見 superpowers-extras-usage-zh-tw.md(全系列 sp- 摘要)。. ローカライズされた概要: 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。 Dispatching Parallel Agents Overview You delegate tasks to specialized agents with isolated context. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

このスキルを使用する理由

推奨ポイント: sunnydata-parallel-agents helps agents 繁體中文說明 :見 superpowers-extras-usage-zh-tw.md(全系列 sp- 摘要)。. 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。 Dispatching Parallel Agents Overview You delegate tasks

おすすめ

適した場面: Ideal for AI agents that need 繁體中文說明 :見 superpowers-extras-usage-zh-tw.md(全系列 sp- 摘要)。.

実現可能なユースケース for sunnydata-parallel-agents

ユースケース: Applying 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。
ユースケース: Applying Dispatching Parallel Agents
ユースケース: Applying Core principle: Dispatch one agent per independent problem domain. Let them work concurrently

! セキュリティと制限

  • 制約事項: Need to understand full system state
  • 制約事項: Constraints: Don't change other code
  • 制約事項: Verify fixes don't conflict

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is sunnydata-parallel-agents?

適した場面: Ideal for AI agents that need 繁體中文說明 :見 superpowers-extras-usage-zh-tw.md(全系列 sp- 摘要)。. ローカライズされた概要: 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。 Dispatching Parallel Agents Overview You delegate tasks to specialized agents with isolated context. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install sunnydata-parallel-agents?

Run the command: npx killer-skills add Zenobia000/ai-brainstorming/sunnydata-parallel-agents. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for sunnydata-parallel-agents?

Key use cases include: ユースケース: Applying 繁體中文說明 :見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp- 摘要)。, ユースケース: Applying Dispatching Parallel Agents, ユースケース: Applying Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.

Which IDEs are compatible with sunnydata-parallel-agents?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for sunnydata-parallel-agents?

制約事項: Need to understand full system state. 制約事項: Constraints: Don't change other code. 制約事項: Verify fixes don't conflict.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add Zenobia000/ai-brainstorming/sunnydata-parallel-agents. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use sunnydata-parallel-agents immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

sunnydata-parallel-agents

Install sunnydata-parallel-agents, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

繁體中文說明:見 SUPERPOWERS-EXTRAS-USAGE-zh-TW.md(全系列 sp-* 摘要)。

Dispatching Parallel Agents

Overview

You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.

When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.

Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.

When to Use

dot
1digraph when_to_use { 2 "Multiple failures?" [shape=diamond]; 3 "Are they independent?" [shape=diamond]; 4 "Single agent investigates all" [shape=box]; 5 "One agent per problem domain" [shape=box]; 6 "Can they work in parallel?" [shape=diamond]; 7 "Sequential agents" [shape=box]; 8 "Parallel dispatch" [shape=box]; 9 10 "Multiple failures?" -> "Are they independent?" [label="yes"]; 11 "Are they independent?" -> "Single agent investigates all" [label="no - related"]; 12 "Are they independent?" -> "Can they work in parallel?" [label="yes"]; 13 "Can they work in parallel?" -> "Parallel dispatch" [label="yes"]; 14 "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"]; 15}

Use when:

  • 3+ test files failing with different root causes
  • Multiple subsystems broken independently
  • Each problem can be understood without context from others
  • No shared state between investigations

Don't use when:

  • Failures are related (fix one might fix others)
  • Need to understand full system state
  • Agents would interfere with each other

The Pattern

1. Identify Independent Domains

Group failures by what's broken:

  • File A tests: Tool approval flow
  • File B tests: Batch completion behavior
  • File C tests: Abort functionality

Each domain is independent - fixing tool approval doesn't affect abort tests.

2. Create Focused Agent Tasks

Each agent gets:

  • Specific scope: One test file or subsystem
  • Clear goal: Make these tests pass
  • Constraints: Don't change other code
  • Expected output: Summary of what you found and fixed

3. Dispatch in Parallel

typescript
1// In Claude Code / AI environment 2Task("Fix agent-tool-abort.test.ts failures") 3Task("Fix batch-completion-behavior.test.ts failures") 4Task("Fix tool-approval-race-conditions.test.ts failures") 5// All three run concurrently

4. Review and Integrate

When agents return:

  • Read each summary
  • Verify fixes don't conflict
  • Run full test suite
  • Integrate all changes

Agent Prompt Structure

Good agent prompts are:

  1. Focused - One clear problem domain
  2. Self-contained - All context needed to understand the problem
  3. Specific about output - What should the agent return?
markdown
1Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts: 2 31. "should abort tool with partial output capture" - expects 'interrupted at' in message 42. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed 53. "should properly track pendingToolCount" - expects 3 results but gets 0 6 7These are timing/race condition issues. Your task: 8 91. Read the test file and understand what each test verifies 102. Identify root cause - timing issues or actual bugs? 113. Fix by: 12 - Replacing arbitrary timeouts with event-based waiting 13 - Fixing bugs in abort implementation if found 14 - Adjusting test expectations if testing changed behavior 15 16Do NOT just increase timeouts - find the real issue. 17 18Return: Summary of what you found and what you fixed.

Common Mistakes

❌ Too broad: "Fix all the tests" - agent gets lost ✅ Specific: "Fix agent-tool-abort.test.ts" - focused scope

❌ No context: "Fix the race condition" - agent doesn't know where ✅ Context: Paste the error messages and test names

❌ No constraints: Agent might refactor everything ✅ Constraints: "Do NOT change production code" or "Fix tests only"

❌ Vague output: "Fix it" - you don't know what changed ✅ Specific: "Return summary of root cause and changes"

When NOT to Use

Related failures: Fixing one might fix others - investigate together first Need full context: Understanding requires seeing entire system Exploratory debugging: You don't know what's broken yet Shared state: Agents would interfere (editing same files, using same resources)

Real Example from Session

Scenario: 6 test failures across 3 files after major refactoring

Failures:

  • agent-tool-abort.test.ts: 3 failures (timing issues)
  • batch-completion-behavior.test.ts: 2 failures (tools not executing)
  • tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)

Decision: Independent domains - abort logic separate from batch completion separate from race conditions

Dispatch:

Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts

Results:

  • Agent 1: Replaced timeouts with event-based waiting
  • Agent 2: Fixed event structure bug (threadId in wrong place)
  • Agent 3: Added wait for async tool execution to complete

Integration: All fixes independent, no conflicts, full suite green

Time saved: 3 problems solved in parallel vs sequentially

Key Benefits

  1. Parallelization - Multiple investigations happen simultaneously
  2. Focus - Each agent has narrow scope, less context to track
  3. Independence - Agents don't interfere with each other
  4. Speed - 3 problems solved in time of 1

Verification

After agents return:

  1. Review each summary - Understand what changed
  2. Check for conflicts - Did agents edit same code?
  3. Run full suite - Verify all fixes work together
  4. Spot check - Agents can make systematic errors

Real-World Impact

From debugging session (2025-10-03):

  • 6 failures across 3 files
  • 3 agents dispatched in parallel
  • All investigations completed concurrently
  • All fixes integrated successfully
  • Zero conflicts between agent changes

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