orchestrate — community orchestrate, nPlayerNext, community, ide skills

v1.0.0

关于此技能

非常适合需要原生适应和全面内容分析的AI代理,使用Claude Code Automatic parallel multi-agent orchestration (includes Review Loop)

projectdx75 projectdx75
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[0]
更新于: 3/17/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 4/11

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

Concrete use-case guidance Explicit limitations and caution
Review Score
4/11
Quality Score
42
Canonical Locale
en
Detected Body Locale
en

非常适合需要原生适应和全面内容分析的AI代理,使用Claude Code Automatic parallel multi-agent orchestration (includes Review Loop)

核心价值

赋予代理以Claude Code编排会话的能力,利用Task工具进行同步子代理生成和从.yaml文件加载用户首选项,同时生成会话ID和处理语言设置

适用 Agent 类型

非常适合需要原生适应和全面内容分析的AI代理,使用Claude Code

赋予的主要能力 · orchestrate

使用用户特定首选项初始化会话
生成唯一的会话ID用于跟踪和分析
加载和适应.json文件中的计划以进行自定义内容分析

! 使用限制与门槛

  • 需要.agents/plan.json文件存在
  • 需要访问.agents/config/user-preferences.yaml文件
  • 依赖于Claude Code和Task工具的功能

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The page lacks a strong recommendation layer.
  • - The underlying skill quality score is below the review floor.

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.

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

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

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

orchestrate 是什么?

非常适合需要原生适应和全面内容分析的AI代理,使用Claude Code Automatic parallel multi-agent orchestration (includes Review Loop)

如何安装 orchestrate?

运行命令:npx killer-skills add projectdx75/nPlayerNext/orchestrate。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

orchestrate 适用于哪些场景?

典型场景包括:使用用户特定首选项初始化会话、生成唯一的会话ID用于跟踪和分析、加载和适应.json文件中的计划以进行自定义内容分析。

orchestrate 支持哪些 IDE 或 Agent?

该技能兼容 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。可使用 Killer-Skills CLI 一条命令通用安装。

orchestrate 有哪些限制?

需要.agents/plan.json文件存在;需要访问.agents/config/user-preferences.yaml文件;依赖于Claude Code和Task工具的功能。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add projectdx75/nPlayerNext/orchestrate。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    orchestrate 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

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

orchestrate

安装 orchestrate,这是一款面向AI agent workflows and automation的 AI Agent Skill。查看评审结论、使用场景与安装路径。

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

/orchestrate

Claude Code Native Adaptation

Spawn .claude/agents/ subagents via Task tool instead of CLI (oh-my-ag agent:spawn). Task tool returns synchronously, so no polling needed.

Step 1: Load Plan

  • Check if .agents/plan.json exists
  • If not: Guide user to run /plan first

Step 2: Initialize Session

  1. Load .agents/config/user-preferences.yaml
  2. Response language follows language setting in .agents/config/user-preferences.yaml
  3. Generate session ID (format: session-YYYYMMDD-HHMMSS)
  4. Show agent routing (based on skill-routing.md)

Step 3: Spawn Agents (Parallel Task tool calls)

Spawn agents by priority tier:

  • Multiple Task tool calls in same message = true parallel execution
  • Each agent: Use .claude/agents/{agent}.md definition
  • Include in prompt: Task description, API contract, context
  • Include API contracts from .agents/skills/_shared/api-contracts/ if they exist
  • Load only task-relevant context (check codebase structure around affected domains)

Agent mapping:

DomainSubagent File
backend.claude/agents/backend-impl.md
frontend.claude/agents/frontend-impl.md
mobile.claude/agents/mobile-impl.md
db.claude/agents/db-impl.md
qa.claude/agents/qa-reviewer.md
debug.claude/agents/debug-investigator.md
pm.claude/agents/pm-planner.md

Step 4: Monitoring

Task tool returns results directly → No polling needed. Check status, files changed, issues in each agent result.

Step 5: Agent-to-Agent Review Loop (Native Loop)

Main agent directly controls this loop. Maintain iteration counter.

iteration = 0
MAX_SELF = 3, MAX_CROSS = 2, MAX_TOTAL = 5

LOOP:
  iteration += 1
  if iteration > MAX_TOTAL → FORCE_COMPLETE (include quality warning)

  [1] Self-Review:
      Check self-review section in implementation agent results
      PASS → Proceed to [2]
      FAIL (self_count < MAX_SELF) → Re-spawn Task tool (with feedback) → LOOP
      FAIL (self_count >= MAX_SELF) → Force proceed to [2]

  [2] Automated Verification:
      Run lint/type-check/tests via Bash tool
      PASS → Proceed to [3]
      FAIL → Feed output back to agent as correction context (max 2 retries) → re-run [2]
      FAIL (retries exhausted) → Proceed to [3] with verification failure noted

  [3] Cross-Review:
      Spawn `qa-reviewer` subagent via Task tool
      Parse QA results: PASS / FAIL
      PASS → ACCEPT
      FAIL (cross_count < MAX_CROSS) → Feedback format:
        ## Review Feedback (iteration {n}/{MAX_TOTAL})
        **Reviewer**: qa-reviewer
        **Verdict**: FAIL
        **Issues**: [Specific file:line references]
        **Fix instruction**: [How to fix]
      → Re-spawn implementation agent Task tool (include feedback) → LOOP
      FAIL (cross_count >= MAX_CROSS) → Report to user with review history

Step 6: Collect Results

After all agents complete:

  • Collect .agents/results/result-{agent}.md
  • Organize completed/failed tasks, changed files, remaining issues

Step 7: Final Report

Session summary:

  • Completed tasks
  • Failed tasks (if failed after retries, include error details)
  • Next step suggestions: Manual fix, re-run specific agent, /review QA

$ARGUMENTS

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