refactor: Convert skill-generator from Chinese to English and remove emoji icons

- Convert all markdown files from Chinese to English - Remove all emoji/icon decorations (🔧📋⚙️✅🏁🔍📚⛔⭐) - Update all section headers, descriptions, and documentation - Keep all content logic, structure, code examples unchanged - Maintain template variables and file paths as-is Files converted (9 files total): - SKILL.md: Output structure comments - templates/skill-md.md: All Chinese descriptions and comments - specs/reference-docs-spec.md: All section headers and explanations - phases/01-requirements-discovery.md through 05-validation.md (5 files) - specs/execution-modes.md, skill-requirements.md, cli-integration.md, scripting-integration.md (4 files) - templates/sequential-phase.md, autonomous-orchestrator.md, autonomous-action.md, code-analysis-action.md, llm-action.md, script-template.md (6 files) All 16 files in skill-generator are now fully in English.
2026-02-15 02:42:45 +08:00 · 2026-01-29 15:42:46 +08:00
parent b791c09476
commit 9762445876
18 changed files with 2049 additions and 2066 deletions
--- a/.claude/skills/skill-generator/templates/llm-action.md
+++ b/.claude/skills/skill-generator/templates/llm-action.md
@@ -1,56 +1,56 @@
 # LLM Action Template

-LLM 动作模板，用于在 Skill 中集成 LLM 调用能力。
+LLM action template for integrating LLM call capabilities into a Skill.

 ## Purpose

-为 Skill 生成 LLM 动作，通过 CCW CLI 统一接口调用 Gemini/Qwen/Codex 进行分析或生成。
+Generate LLM actions for a Skill, call Gemini/Qwen/Codex through CCW CLI unified interface for analysis or generation.

 ## Usage Context

 | Phase | Usage |
 |-------|-------|
-| Optional | 当 Skill 需要 LLM 能力时使用 |
-| Generation Trigger | 用户选择添加 llm 动作类型 |
-| Tools | gemini, qwen, codex (支持 fallback chain) |
+| Optional | Use when Skill requires LLM capabilities |
+| Generation Trigger | User selects to add llm action type |
+| Tools | gemini, qwen, codex (supports fallback chain) |

 ---

-## 配置结构
+## Configuration Structure

 ```typescript
 interface LLMActionConfig {
  id: string;                    // "llm-analyze", "llm-generate"
  name: string;                  // "LLM Analysis"
-  type: 'llm';                   // 动作类型标识
+  type: 'llm';                   // Action type identifier

-  // LLM 工具配置
+  // LLM tool config
  tool: {
    primary: 'gemini' | 'qwen' | 'codex';
    fallback_chain: string[];    // ['gemini', 'qwen', 'codex']
  };

-  // 执行模式
+  // Execution mode
  mode: 'analysis' | 'write';

-  // 提示词配置
+  // Prompt config
  prompt: {
-    template: string;            // 提示词模板路径或内联
-    variables: string[];         // 需要替换的变量
+    template: string;            // Prompt template path or inline
+    variables: string[];         // Variables to replace
  };

-  // 输入输出
-  input: string[];               // 依赖的上下文文件
-  output: string;                // 输出文件路径
+  // Input/Output
+  input: string[];               // Dependent context files
+  output: string;                // Output file path

-  // 超时配置
-  timeout?: number;              // 毫秒，默认 600000 (10min)
+  // Timeout config
+  timeout?: number;              // Milliseconds, default 600000 (10min)
 }
 ```

 ---

-## 模板生成函数
+## Template Generation Function

 ```javascript
 function generateLLMAction(config) {
@@ -61,25 +61,25 @@ function generateLLMAction(config) {

 ## Action: ${id}

-### 执行逻辑
+### Execution Logic

 \`\`\`javascript
 async function execute${toPascalCase(id)}(context) {
  const workDir = context.workDir;
  const state = context.state;

-  // 1. 收集输入上下文
+  // 1. Collect input context
  const inputContext = ${JSON.stringify(input)}.map(f => {
    const path = \`\${workDir}/\${f}\`;
    return Read(path);
  }).join('\\n\\n---\\n\\n');

-  // 2. 构建提示词
+  // 2. Build prompt
  const promptTemplate = \`${prompt.template}\`;
  const finalPrompt = promptTemplate
    ${prompt.variables.map(v => `.replace('{{${v}}}', context.${v} || '')`).join('\n    ')};

-  // 3. 执行 LLM 调用 (带 fallback)
+  // 3. Execute LLM call (with fallback)
  const tools = ['${tool.primary}', ${tool.fallback_chain.map(t => `'${t}'`).join(', ')}];
  let result = null;
  let usedTool = null;
@@ -98,10 +98,10 @@ async function execute${toPascalCase(id)}(context) {
    throw new Error('All LLM tools failed');
  }

-  // 4. 保存结果
+  // 4. Save result
  Write(\`\${workDir}/${output}\`, result);

-  // 5. 更新状态
+  // 5. Update state
  state.llm_calls = (state.llm_calls || 0) + 1;
  state.last_llm_tool = usedTool;

@@ -112,38 +112,38 @@ async function execute${toPascalCase(id)}(context) {
  };
 }

-// LLM 调用封装
+// LLM call wrapper
 async function callLLM(tool, prompt, mode, timeout) {
  const modeFlag = mode === 'write' ? '--mode write' : '--mode analysis';

-  // 使用 CCW CLI 统一接口
+  // Use CCW CLI unified interface
  const command = \`ccw cli -p "\${escapePrompt(prompt)}" --tool \${tool} \${modeFlag}\`;

  const result = Bash({
    command,
    timeout,
-    run_in_background: true  // 异步执行
+    run_in_background: true  // Async execution
  });

-  // 等待完成
+  // Wait for completion
  return await waitForResult(result.task_id, timeout);
 }

 function escapePrompt(prompt) {
-  // 转义双引号和特殊字符
+  // Escape double quotes and special characters
  return prompt.replace(/"/g, '\\\\"').replace(/\$/g, '\\\\$');
 }
 \`\`\`

-### Prompt 模板
+### Prompt Template

 \`\`\`
 ${prompt.template}
 \`\`\`

-### 变量说明
+### Variable Descriptions

-${prompt.variables.map(v => `- \`{{${v}}}\`: ${v} 变量`).join('\n')}
+${prompt.variables.map(v => `- \`{{${v}}}\`: ${v} variable`).join('\n')}
 `;
 }

@@ -154,11 +154,11 @@ function toPascalCase(str) {

 ---

-## 预置 LLM 动作模板
+## Preset LLM Action Templates

-### 1. 代码分析动作
+### 1. Code Analysis Action

-```yaml
+\`\`\`yaml
 id: llm-code-analysis
 name: LLM Code Analysis
 type: llm
@@ -168,15 +168,15 @@ tool:
 mode: analysis
 prompt:
  template: |
-    PURPOSE: 分析代码结构和模式，提取关键设计特征
+    PURPOSE: Analyze code structure and patterns, extract key design features
    TASK:
-    • 识别主要模块和组件
-    • 分析依赖关系
-    • 提取设计模式
-    • 评估代码质量
+    • Identify main modules and components
+    • Analyze dependencies
+    • Extract design patterns
+    • Evaluate code quality
    MODE: analysis
    CONTEXT: {{code_context}}
-    EXPECTED: JSON 格式的分析报告，包含 modules, dependencies, patterns, quality_score
+    EXPECTED: JSON formatted analysis report with modules, dependencies, patterns, quality_score
    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md)
  variables:
    - code_context
@@ -184,11 +184,11 @@ input:
  - collected-code.md
 output: analysis-report.json
 timeout: 900000
-```
+\`\`\`

-### 2. 文档生成动作
+### 2. Documentation Generation Action

-```yaml
+\`\`\`yaml
 id: llm-doc-generation
 name: LLM Documentation Generation
 type: llm
@@ -198,15 +198,15 @@ tool:
 mode: write
 prompt:
  template: |
-    PURPOSE: 根据分析结果生成高质量文档
+    PURPOSE: Generate high-quality documentation based on analysis results
    TASK:
-    • 基于分析报告生成文档大纲
-    • 填充各章节内容
-    • 添加代码示例和说明
-    • 生成 Mermaid 图表
+    • Generate documentation outline based on analysis report
+    • Populate chapter content
+    • Add code examples and explanations
+    • Generate Mermaid diagrams
    MODE: write
    CONTEXT: {{analysis_report}}
-    EXPECTED: 完整的 Markdown 文档，包含目录、章节、图表
+    EXPECTED: Complete Markdown documentation with table of contents, chapters, diagrams
    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/write-protocol.md)
  variables:
    - analysis_report
@@ -214,11 +214,11 @@ input:
  - analysis-report.json
 output: generated-doc.md
 timeout: 1200000
-```
+\`\`\`

-### 3. 代码重构建议动作
+### 3. Code Refactoring Suggestions Action

-```yaml
+\`\`\`yaml
 id: llm-refactor-suggest
 name: LLM Refactoring Suggestions
 type: llm
@@ -228,15 +228,15 @@ tool:
 mode: analysis
 prompt:
  template: |
-    PURPOSE: 分析代码并提供重构建议
+    PURPOSE: Analyze code and provide refactoring suggestions
    TASK:
-    • 识别代码异味 (code smells)
-    • 评估复杂度热点
-    • 提出具体重构方案
-    • 估算重构影响范围
+    • Identify code smells
+    • Evaluate complexity hotspots
+    • Propose specific refactoring plans
+    • Estimate refactoring impact scope
    MODE: analysis
    CONTEXT: {{source_code}}
-    EXPECTED: 重构建议列表，每项包含 location, issue, suggestion, impact
+    EXPECTED: List of refactoring suggestions with location, issue, suggestion, impact fields
    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md)
  variables:
    - source_code
@@ -244,15 +244,15 @@ input:
  - source-files.md
 output: refactor-suggestions.json
 timeout: 600000
-```
+\`\`\`

 ---

-## 使用示例
+## Usage Examples

-### 在 Phase 中使用 LLM 动作
+### Using LLM Actions in Phase

-```javascript
+\`\`\`javascript
 // phases/02-llm-analysis.md

 const llmConfig = {
@@ -265,39 +265,39 @@ const llmConfig = {
  },
  mode: 'analysis',
  prompt: {
-    template: `
-PURPOSE: 分析现有 Skill 的设计模式
+    template: \`
+PURPOSE: Analyze design patterns of existing Skills
 TASK:
-• 提取 Skill 结构规范
-• 识别 Phase 组织模式
-• 分析 Agent 调用模式
+• Extract Skill structure specification
+• Identify Phase organization patterns
+• Analyze Agent invocation patterns
 MODE: analysis
 CONTEXT: {{skill_source}}
-EXPECTED: 结构化的设计模式分析
-`,
+EXPECTED: Structured design pattern analysis
+\`,
    variables: ['skill_source']
  },
  input: ['collected-skills.md'],
  output: 'skill-patterns.json'
 };

-// 执行
+// Execute
 const result = await executeLLMAction(llmConfig, {
  workDir: '.workflow/.scratchpad/skill-gen-xxx',
  skill_source: Read('.workflow/.scratchpad/skill-gen-xxx/collected-skills.md')
 });
-```
+\`\`\`

-### 在 Orchestrator 中调度 LLM 动作
+### Scheduling LLM Actions in Orchestrator

-```javascript
-// autonomous-orchestrator 中的 LLM 动作调度
+\`\`\`javascript
+// Schedule LLM actions in autonomous-orchestrator

 const actions = [
  { type: 'collect', priority: 100 },
-  { type: 'llm', id: 'llm-analyze', priority: 90 },  // LLM 分析
+  { type: 'llm', id: 'llm-analyze', priority: 90 },  // LLM analysis
  { type: 'process', priority: 80 },
-  { type: 'llm', id: 'llm-generate', priority: 70 }, // LLM 生成
+  { type: 'llm', id: 'llm-generate', priority: 70 }, // LLM generation
  { type: 'validate', priority: 60 }
 ];

@@ -310,13 +310,13 @@ for (const action of sortByPriority(actions)) {
    context.state[action.id] = llmResult;
  }
 }
-```
+\`\`\`

 ---

-## 错误处理
+## Error Handling

-```javascript
+\`\`\`javascript
 async function executeLLMActionWithRetry(config, context, maxRetries = 3) {
  let lastError = null;

@@ -325,43 +325,43 @@ async function executeLLMActionWithRetry(config, context, maxRetries = 3) {
      return await executeLLMAction(config, context);
    } catch (error) {
      lastError = error;
-      console.log(`Attempt ${attempt} failed: ${error.message}`);
+      console.log(\`Attempt ${attempt} failed: ${error.message}\`);

-      // 指数退避
+      // Exponential backoff
      if (attempt < maxRetries) {
        await sleep(Math.pow(2, attempt) * 1000);
      }
    }
  }

-  // 所有重试失败
+  // All retries failed
  return {
    success: false,
    error: lastError.message,
    fallback: 'manual_review_required'
  };
 }
-```
+\`\`\`

 ---

-## 最佳实践
+## Best Practices

-1. **选择合适的工具**
-   - 分析任务：Gemini（大上下文）> Qwen
-   - 生成任务：Codex（自主执行）> Gemini > Qwen
-   - 代码修改：Codex > Gemini
+1. **Select Appropriate Tool**
+   - Analysis tasks: Gemini (large context) > Qwen
+   - Generation tasks: Codex (autonomous execution) > Gemini > Qwen
+   - Code modification: Codex > Gemini

-2. **配置 Fallback Chain**
-   - 总是配置至少一个 fallback
-   - 考虑工具特性选择 fallback 顺序
+2. **Configure Fallback Chain**
+   - Always configure at least one fallback
+   - Consider tool characteristics when ordering fallbacks

-3. **超时设置**
-   - 分析任务：10-15 分钟
-   - 生成任务：15-20 分钟
-   - 复杂任务：20-60 分钟
+3. **Timeout Settings**
+   - Analysis tasks: 10-15 minutes
+   - Generation tasks: 15-20 minutes
+   - Complex tasks: 20-60 minutes

-4. **提示词设计**
-   - 使用 PURPOSE/TASK/MODE/CONTEXT/EXPECTED/RULES 结构
-   - 引用标准协议模板
-   - 明确输出格式要求
+4. **Prompt Design**
+   - Use PURPOSE/TASK/MODE/CONTEXT/EXPECTED/RULES structure
+   - Reference standard protocol templates
+   - Clearly specify output format requirements