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Agent Skill · 研究检索

superpowers-dispatching-parallel-agents(超级大国派遣平行特工)

superpowers-dispatching-parallel-agents 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

superpowers-dispatching-parallel-agents 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 17,213。

总安装 6,732周安装 275GitHub Stars 公开资料未说明下载 2,156核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:superpowers-dispatching-parallel-agents(超级大国派遣平行特工)
来源仓库:https://github.com/demo112/superpowers-dispatching-parallel-agents
安装命令:
openclaw skills install superpowers-dispatching-parallel-agents
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install superpowers-dispatching-parallel-agents

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

将独立任务分派给同时处理孤立问题的集中代理,无需共享状态或顺序依赖关系,以便更快地解决问题。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install superpowers-dispatching-parallel-agents;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

superpowers-dispatching-parallel-agents 怎么安装?

在终端执行 openclaw skills install superpowers-dispatching-parallel-agents。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

superpowers-dispatching-parallel-agents 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

superpowers-dispatching-parallel-agents 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install superpowers-dispatching-parallel-agents 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

superpowers-dispatching-parallel-agents 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

superpowers-dispatching-parallel-agents 用的人多吗?

公开数据是累计安装 6,732 次,近一周安装 275 次,下载 2,156 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

superpowers-dispatching-parallel-agents 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
superpowers-dispatching-parallel-agents

超级大国派遣平行特工 · 本页

6,732—执行命令MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

<!-- Original: https://github.com/obra/superpowers, MIT License -->


name: superpowers-dispatching-parallel-agents description: Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies version: 1.0.0


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

Multiple failures?
  → Are they independent?
    → yes → Can they work in parallel?
      → yes → Parallel dispatch
      → no (shared state) → Sequential agents
    → no (related) → Single agent investigates all

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

Spawn subagents simultaneously — each handles one domain:

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

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?
Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:

1. "should abort tool with partial output capture" - expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed
3. "should properly track pendingToolCount" - expects 3 results but gets 0

These are timing/race condition issues. Your task:

1. Read the test file and understand what each test verifies
2. Identify root cause - timing issues or actual bugs?
3. Fix by:
   - Replacing arbitrary timeouts with event-based waiting
   - Fixing bugs in abort implementation if found
   - Adjusting test expectations if testing changed behavior

Do NOT just increase timeouts - find the real issue.

Return: Summary of what you found and what you fixed.

Common Mistakes

MistakeFix
Too broad: "Fix all the tests"Specific: "Fix agent-tool-abort.test.ts"
No context: "Fix the race condition"Paste error messages and test names
No constraints: Agent might refactor everything"Do NOT change production code" or "Fix tests only"
Vague output: "Fix it""Return summary of root cause and changes"

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

平台分布

OpenClaw

79.71%
按下载量换算1,719

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install superpowers-dispatching-parallel-agents 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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