Token导航 LogoToken导航

Agent Skill · 研究检索

create-workflow-command(创建工作流命令)

create-workflow-command 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

create-workflow-command 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,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 42,285。

总安装 1,091周安装 45GitHub Stars 891下载 356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
unknown

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:create-workflow-command(创建工作流命令)
来源仓库:https://github.com/neolabhq/context-engineering-kit
仓库路径:skills/create-workflow-command
安装命令:
npx skills add https://github.com/neolabhq/context-engineering-kit --skill create-workflow-command
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/neolabhq/context-engineering-kit --skill create-workflow-command

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。

该技能创建 orchestrator 命令,通过分发子代理执行多步工作流,解决上下文膨胀问题。适用于需要分解复杂任务、避免主指令过长或提升模块化协作效率的场景。将详细步骤存储于独立文件,由子代理按需读取,保持主命令简洁高效。安装方式:GitHub 仓库,使用 npx 命令添加;注意文件读写权限与子代理配置路径。

create-workflow-command 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/create-workflow-command

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/neolabhq/context-engineering-kit --skill creat…;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

create-workflow-command 怎么安装?

在终端执行 npx skills add https://github.com/neolabhq/context-engineering-kit --skill create-workflow…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

create-workflow-command 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

create-workflow-command 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 npx skills add https://github.com/neolabhq/context-engineering-kit --skill create-workflow-command 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。

create-workflow-command 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

create-workflow-command 用的人多吗?

公开数据是累计安装 1,091 次,近一周安装 45 次,下载 356 次,GitHub 891 星。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。

create-workflow-command 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 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。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
create-workflow-command

创建工作流命令 · 本页

1,091891执行命令未明确通过

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

Create Workflow Command

Create a command that orchestrates multi-step workflows by dispatching sub-agents with task-specific instructions stored in separate files.

User Input

Workflow Name: $1
Description: $2

Architecture Overview

Workflow commands solve the context bloat problem: instead of embedding detailed step instructions in the main command (polluting orchestrator context), store them in separate task files that sub-agents read on-demand.

plugins/<plugin-name>/
├── commands/
│   └── <workflow>.md          # Lean orchestrator (~50-100 tokens per step)
├── agents/                     # Optional: reusable executor agents
│   └── step-executor.md       # Custom agent with specific tools/behavior
└── tasks/                      # All task instructions directly here
    ├── step-1-<name>.md       # Full instructions (~500+ tokens each)
    ├── step-2-<name>.md
    ├── step-3-<name>.md
    └── common-context.md      # Shared context across workflows

Key Principles

1. Context Isolation

Each sub-agent gets its own isolated context window. The main orchestrator stays lean while sub-agents load detailed instructions from files.

ComponentContext CostPurpose
Orchestrator command~50-100 tokens/stepDispatch and coordinate
Task file~500+ tokensDetailed step instructions
Sub-agent base~294 tokensSystem prompt overhead

2. Sub-Agent Capabilities

Sub-agents spawned via Task tool:

CapabilityAvailableNotes
Read tool✅ YesCan read any file
Write tool✅ YesIf not restricted
Grep/Glob✅ YesFor code search
Skills loading❌ NoSkills don't auto-load in sub-agents
Spawn sub-agents❌ NoCannot nest Task tool
Resume context✅ YesVia resume parameter

3. File Reference Pattern

Use ${CLAUDE_PLUGIN_ROOT} for portable paths within plugin:

Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-workflow-name.md and execute.

Sub-agent will use Read tool to fetch the file content.

Implementation Process

Step 1: Gather Requirements

Ask user (if not provided):

  1. Workflow name: kebab-case identifier (e.g., feature-implementation)
  2. Description: What the workflow accomplishes
  3. Steps: List of discrete steps with:

- Step name - Step goal - Required tools - Expected output

  1. Execution mode: Sequential or parallel steps
  2. Agent type: general-purpose or custom agent

Step 2: Create Directory Structure

# Create tasks directory (if it doesn't exist)
mkdir -p ${CLAUDE_PLUGIN_ROOT}/tasks

# Optional: Create agents directory (if using custom agents)
mkdir -p ${CLAUDE_PLUGIN_ROOT}/agents

Note: All task files (both workflow-specific steps and shared context) are placed directly in tasks/ without subdirectories.

Step 3: Create Task Files

For each step, create a task file with this structure:

# Step N: <Step Name>

## Context
You are executing step N of the <workflow-name> workflow.

## Goal
<Clear, specific goal for this step>

## Input
<What this step receives from previous steps or user>

## Instructions
1. <Specific action>
2. <Specific action>
3. <Specific action>

## Constraints
- <Limitation or boundary>
- <What NOT to do>

## Expected Output
<What to return to orchestrator>

## Success Criteria
- [ ] <Measurable outcome>
- [ ] <Measurable outcome>

Step 4: Create Orchestrator Command

Create the main command file with this pattern:

---
description: <Workflow description>
argument-hint: <Required arguments>
allowed-tools: Task, Read
model: sonnet
---

# <Workflow Name>

## User Input

\`\`\`text
$ARGUMENTS
\`\`\`

## Workflow Execution

### Step 1: <Step Name>

Launch general-purpose agent:
- **Description**: "<3-5 word summary>"
- **Prompt**:
  \`\`\`
  Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-<workflow>-<name>.md and execute.

  Context:
  - TARGET: $1
  - MODE: $2
  \`\`\`

**Capture**: <What to extract from result>

### Step 2: <Step Name>

Launch general-purpose agent:
- **Description**: "<3-5 word summary>"
- **Prompt**:
  \`\`\`
  Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-2-<workflow>-<name>.md and execute.

  Context from Step 1:
  - <Key data from previous step>
  \`\`\`

### Step 3: <Step Name>

[Continue pattern...]

## Completion

Summarize workflow results:
1. <What was accomplished>
2. <Key outputs>
3. <Next steps if any>
Frontmatter Options
FieldPurposeDefault
descriptionBrief description of workflow purposeRequired
argument-hintExpected arguments descriptionNone
allowed-toolsTools the command can useInherits from conversation
modelSpecific Claude model (sonnet, opus, haiku)Inherits from conversation

Model selection:

  • haiku - Fast, efficient for simple workflows
  • sonnet - Balanced performance (recommended default)
  • opus - Maximum capability for complex orchestration

Execution Patterns

Pattern A: Sequential Steps (Default)

Each step depends on previous step's output:

### Step 1: Analyze
Launch agent → Get analysis result

### Step 2: Plan (uses Step 1 result)
Launch agent with Step 1 context → Get plan

### Step 3: Execute (uses Step 2 result)
Launch agent with Step 2 context → Complete

Pattern B: Parallel Independent Steps

Steps can run concurrently:

### Analysis Phase (Parallel)

Launch 3 agents simultaneously:
1. Agent 1: Security analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1a-security.md
2. Agent 2: Performance analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1b-performance.md
3. Agent 3: Code quality analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1c-quality.md

**Wait for all**, then consolidate results.

### Synthesis Phase
Launch agent with all analysis results...

Pattern C: Stateful Multi-Step (Resume)

When steps need shared context:

### Step 1: Initialize
Launch agent, **capture agent_id**

### Step 2: Continue (same context)
Resume agent using agent_id:
- **resume**: <agent_id from Step 1>
- **prompt**: "Proceed to phase 2: <additional instructions>"

Example: Feature Implementation Workflow

Orchestrator Command

---
description: Execute feature implementation through research, planning, and coding phases
argument-hint: [feature-description]
allowed-tools: Task, Read, TodoWrite
model: sonnet
---

# Implement Feature

## User Input
\`\`\`text
$ARGUMENTS
\`\`\`

Create TodoWrite with workflow steps.

## Phase 1: Research

Launch general-purpose agent:
- **Description**: "Research feature requirements"
- **Prompt**:
  \`\`\`
  Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-feature-impl-research.md

  Feature: $ARGUMENTS
  \`\`\`

**Extract**: Key findings, constraints, existing patterns

## Phase 2: Architecture

Launch general-purpose agent:
- **Description**: "Design feature architecture"
- **Prompt**:
  \`\`\`
  Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-2-feature-impl-architecture.md

  Feature: $ARGUMENTS
  Research findings: <summary from Phase 1>
  \`\`\`

**Extract**: File structure, components, interfaces

## Phase 3: Implementation

Launch developer agent:
- **Description**: "Implement feature code"
- **Prompt**:
  \`\`\`
  Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-3-feature-impl-implement.md

  Architecture: <summary from Phase 2>
  \`\`\`

## Completion

Mark todos complete. Report:
1. Files created/modified
2. Tests added
3. Remaining work

Task File Example (step-1-feature-impl-research.md)

# Step 1: Feature Research

## Context
You are the research phase of a feature implementation workflow.

## Goal
Thoroughly understand the feature requirements and existing codebase context before any implementation begins.

## Instructions

1. **Parse Feature Request**
   - Extract core requirements
   - Identify acceptance criteria
   - Note any constraints mentioned

2. **Codebase Analysis**
   - Search for similar existing features
   - Identify relevant patterns and conventions
   - Find reusable components/utilities

3. **Dependency Check**
   - What existing code will this feature interact with?
   - Are there breaking change risks?
   - What tests exist for related functionality?

4. **Gap Analysis**
   - What's missing from the request?
   - What clarifications might be needed?
   - What edge cases should be considered?

## Constraints
- Do NOT write any implementation code
- Do NOT modify any files
- Focus purely on research and analysis

## Expected Output

Return a structured research summary:

\`\`\`markdown
## Feature Understanding
- Core requirement: <summary>
- Acceptance criteria: <list>

## Codebase Context
- Similar features: <list with file paths>
- Patterns to follow: <list>
- Reusable code: <list with file paths>

## Dependencies
- Files affected: <list>
- Tests to consider: <list>

## Open Questions
- <Question 1>
- <Question 2>

## Recommendation
<Brief recommendation for architecture phase>
\`\`\`

## Success Criteria
- [ ] Feature requirements clearly articulated
- [ ] Relevant existing code identified
- [ ] No implementation attempted
- [ ] Clear handoff to architecture phase

Known Limitations

LimitationImpactWorkaround
No nested sub-agentsSub-agents can't spawn Task toolKeep all orchestration in main command
No skill auto-loadingSub-agents don't trigger skillsPass explicit file paths or inline context
Fresh context per agentEach dispatch starts emptyUse resume pattern OR pass summaries
File read latencyExtra tool call per stepAcceptable trade-off for context savings

Validation Checklist

Before finalizing workflow command:

  • Each step has clear, specific goal
  • Task files are self-contained (sub-agent doesn't need external context)
  • File paths use ${CLAUDE_PLUGIN_ROOT} for portability
  • Context passed between steps is minimal (summaries, not full data)
  • Orchestrator command stays lean (<100 tokens per step dispatch)
  • Error handling defined for step failures
  • Success criteria measurable for each step

Create the Workflow

Based on user input, create:

  1. Directories:

- ${CLAUDE_PLUGIN_ROOT}/tasks/ - All task files directly here - ${CLAUDE_PLUGIN_ROOT}/agents/ - (Optional) Custom agent definitions

  1. Task files: Create in tasks/ directory with naming pattern step-N-<workflow>-<name>.md

- Example: step-1-feature-impl-research.md - Example: step-2-feature-impl-architecture.md - Shared context: common-context.md directly in tasks/

  1. Orchestrator command: Lean dispatch logic in commands/<workflow-name>.md
  2. Custom agents (Optional): If workflow needs specialized agent behavior in agents/
  3. Update plugin.json: Add command to plugin manifest if needed

After creation, suggest testing with /customaize-agent:test-prompt command.

平台分布

Codex

35.16%
按下载量换算125

Claude

28.62%
按下载量换算102

Cursor

16.92%
按下载量换算60

Gemini CLI

9.51%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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 npx skills add https://github.com/neolabhq/context-engineering-kit --skill create-workflow-command 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。

继续浏览同类 Skills