**用途**: 定义 Agent(Subagent)的职责、擅长领域和配置
澄清用户意图的 Skill。当用户给出模糊想法、需要脑暴、需要讨论设计时触发。触发词:帮我想想、我想做、讨论一下、澄清、clarify。
Claude Code Plugin 开发专家。当用户要创建 Plugin、Skill、Subagent、Slash command、Hook 时触发。也用于解答 Claude Code 开发相关问题。触发词:创建 plugin、写 skill、写 agent、创建 hook、写 command、插件开发。
创建新 Skill 的 Skill。当需要把能力固化为 Skill 时触发。触发词:创建 skill、新建 skill、写一个 skill、create skill。
**用途**: 设计领域专用的 Skills(区别于 Core Skills)
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🚀 AI 驱动的 SOP(标准操作流程)引擎 - 人说想法,Claude 写/执行 Skill,产出结果
sop-engine 的「用户」是 Claude,不是人。
# macOS
brew install jq yq
# 验证安装
jq --version
yq --version
# 基础功能测试 (16/16 通过)
./test-runtime-basic.sh
# 状态管理测试 (16/16 通过)
./test-state-manager.sh
# Workflow 执行测试 (7/8 通过)
./test-executor.sh
.sop-engine/workflows/hello.yaml)workflow:
name: hello-world
description: 第一个 Workflow
nodes:
greet:
type: skill
skill: clarify-skill
input:
question: "Hello, World!"
edges:
- from: greet
to: END
entry: greet
exit: END
trigger:
type: manual
.sop-engine/runtime/dispatcher.sh start hello-world '{}'
节点(Node)= Skill / Workflow / Condition / Loop / Parallel
边(Edge)= 控制流
示例:
collect_data → analyze → check_score → generate_report → END
↓ (score < 60)
alert_user → END
| 类别 | Skills |
|---|---|
| 理解 | clarify-skill |
| 认知 | research-skill, plan-skill, evaluate-skill, reflect-skill |
| 协作 | handoff-skill |
| 记忆 | log-skill |
| 自省 | create-skill, iterate-skill, version-skill |
| 控制 | loop-skill |
| 开发 | claude-code-dev, hook-skill |
| 编排 | skill-lifecycle |
| 设计 | system-create-skill, workflow-define-skill, agent-define-skill |
| 交互 | user-confirm-skill |
| 汇总 | synthesize-skill, summarize-skill |
❌ 错误(生成式): "你想要什么功能?请详细描述。"
✅ 正确(判别式): "我设计了这个方案:
- 功能 A
- 功能 B
- 功能 C
这个方案符合你的预期吗?
A. ✅ 符合,开始执行
B. ❌ 需要调整"
sop-engine/
├── .claude/ # Claude Code 配置
│ ├── skills/ # Skills 定义(22 个)
│ ├── agents/ # Subagents
│ └── commands/ # Slash commands
│
├── .sop-engine/ # Runtime 数据
│ ├── runtime/ # Runtime 脚本(8 个)✅
│ │ ├── executor.sh # Workflow 执行器
│ │ ├── dispatcher.sh # 调度器
│ │ ├── state-manager.sh # 状态管理
│ │ ├── skill-runner.sh # Skill 执行器
│ │ ├── tick.sh # 定时触发
│ │ ├── event-listener.sh # 事件监听
│ │ ├── utils.sh # 工具函数
│ │ └── config.sh # 配置
│ │
│ ├── workflows/ # Workflow 定义
│ ├── running/ # 运行中的实例
│ ├── completed/ # 已完成的实例
│ └── failed/ # 失败的实例
│
├── docs/ # 文档
│ ├── DESIGN.md # 设计文档
│ ├── RUNTIME-TEST-REPORT.md # 测试报告 ✅
│ └── CAPABILITY-MAP.md # 能力图谱
│
└── test-*.sh # 测试脚本 ✅
# 使用 create-skill 命令
/create-skill 数据分析
支持的节点类型:
完整示例:
workflow:
name: data-pipeline
nodes:
collect:
type: skill
skill: data-collect-skill
input:
date: $workflow.input.date
analyze:
type: skill
skill: data-analyze-skill
input:
data: $collect.output
check:
type: condition
expression: "$analyze.output.score >= 80"
success:
type: skill
skill: report-skill
failure:
type: skill
skill: alert-skill
edges:
- from: collect
to: analyze
- from: analyze
to: check
- from: check
to: success
condition: true
- from: check
to: failure
condition: false
- from: success
to: END
- from: failure
to: END
entry: collect
exit: END
trigger:
type: manual
.sop-engine/runtime/dispatcher.sh start workflow-name '{"param":"value"}'
trigger:
type: tick
schedule: "21:00" # 每天 21:00
# 或 cron 格式
schedule: "0 */6 * * *" # 每 6 小时
trigger:
type: file_upload
path: "data/reports/*.pdf"
| 测试模块 | 通过/总数 | 状态 |
|---|---|---|
| 配置和工具函数 | 16/16 | ✅ 完全通过 |
| 状态管理器 | 16/16 | ✅ 完全通过 |
| Workflow 执行器 | 7/8 | ✅ 基本通过 |
详见 测试报告
npx claudepluginhub maxoreric/sop-engineCustom fork of superpowers with additional features - Core skills library for Claude Code: TDD, debugging, collaboration patterns
Convert human SOPs into deterministic agentic workflows. Bundles the agentic-sop + agentic-workflow-audit skills, the /sop-flow command, a project-scoped Stop-hook regression gate, and the portable agentic-sop-kit.
Natural language workflows (SOPs) for AI agents including code-assist, pdd, codebase-summary, code-task-generator, eval, and agent-sop-author.
Core workflow engine - Execute workflow-as-markdown definitions with validation-driven completion
Auto-improving AI sub-agents that learn from their mistakes across sessions
Design complex n8n workflows with AI assistance - loops, branching, error handling
AI skills framework: UNDERSTAND → ENVISION → DELIVER → REFLECT. Process enforcement, 14 workflows, 37 skills, 5 agent personas.