feat: Add code analysis and LLM action templates with detailed configurations and examples

- Introduced a comprehensive code analysis action template for integrating code exploration and analysis capabilities. - Added LLM action template for seamless integration of LLM calls with customizable prompts and tools. - Implemented a benchmark search script to compare multiple search methods across various dimensions including speed, result quality, ranking stability, and coverage. - Provided preset configurations for common analysis tasks and LLM actions, enhancing usability and flexibility.
2026-02-15 02:42:45 +08:00 · 2026-01-03 17:37:49 +08:00
parent 6a45035e3f
commit be498acf59
10 changed files with 3076 additions and 217 deletions
--- a/.claude/skills/skill-generator/templates/llm-action.md
+++ b/.claude/skills/skill-generator/templates/llm-action.md
@@ -0,0 +1,355 @@
+# LLM Action Template
+
+LLM 动作模板，用于在 Skill 中集成 LLM 调用能力。
+
+---
+
+## 配置结构
+
+```typescript
+interface LLMActionConfig {
+  id: string;                    // "llm-analyze", "llm-generate"
+  name: string;                  // "LLM Analysis"
+  type: 'llm';                   // 动作类型标识
+
+  // LLM 工具配置
+  tool: {
+    primary: 'gemini' | 'qwen' | 'codex';
+    fallback_chain: string[];    // ['gemini', 'qwen', 'codex']
+  };
+
+  // 执行模式
+  mode: 'analysis' | 'write';
+
+  // 提示词配置
+  prompt: {
+    template: string;            // 提示词模板路径或内联
+    variables: string[];         // 需要替换的变量
+  };
+
+  // 输入输出
+  input: string[];               // 依赖的上下文文件
+  output: string;                // 输出文件路径
+
+  // 超时配置
+  timeout?: number;              // 毫秒，默认 600000 (10min)
+}
+```
+
+---
+
+## 模板生成函数
+
+```javascript
+function generateLLMAction(config) {
+  const { id, name, tool, mode, prompt, input, output, timeout = 600000 } = config;
+
+  return `
+# ${name}
+
+## Action: ${id}
+
+### 执行逻辑
+
+\`\`\`javascript
+async function execute${toPascalCase(id)}(context) {
+  const workDir = context.workDir;
+  const state = context.state;
+
+  // 1. 收集输入上下文
+  const inputContext = ${JSON.stringify(input)}.map(f => {
+    const path = \`\${workDir}/\${f}\`;
+    return Read(path);
+  }).join('\\n\\n---\\n\\n');
+
+  // 2. 构建提示词
+  const promptTemplate = \`${prompt.template}\`;
+  const finalPrompt = promptTemplate
+    ${prompt.variables.map(v => `.replace('{{${v}}}', context.${v} || '')`).join('\n    ')};
+
+  // 3. 执行 LLM 调用 (带 fallback)
+  const tools = ['${tool.primary}', ${tool.fallback_chain.map(t => `'${t}'`).join(', ')}];
+  let result = null;
+  let usedTool = null;
+
+  for (const t of tools) {
+    try {
+      result = await callLLM(t, finalPrompt, '${mode}', ${timeout});
+      usedTool = t;
+      break;
+    } catch (error) {
+      console.log(\`\${t} failed: \${error.message}, trying next...\`);
+    }
+  }
+
+  if (!result) {
+    throw new Error('All LLM tools failed');
+  }
+
+  // 4. 保存结果
+  Write(\`\${workDir}/${output}\`, result);
+
+  // 5. 更新状态
+  state.llm_calls = (state.llm_calls || 0) + 1;
+  state.last_llm_tool = usedTool;
+
+  return {
+    success: true,
+    output: '${output}',
+    tool_used: usedTool
+  };
+}
+
+// LLM 调用封装
+async function callLLM(tool, prompt, mode, timeout) {
+  const modeFlag = mode === 'write' ? '--mode write' : '--mode analysis';
+
+  // 使用 CCW CLI 统一接口
+  const command = \`ccw cli -p "\${escapePrompt(prompt)}" --tool \${tool} \${modeFlag}\`;
+
+  const result = Bash({
+    command,
+    timeout,
+    run_in_background: true  // 异步执行
+  });
+
+  // 等待完成
+  return await waitForResult(result.task_id, timeout);
+}
+
+function escapePrompt(prompt) {
+  // 转义双引号和特殊字符
+  return prompt.replace(/"/g, '\\\\"').replace(/\$/g, '\\\\$');
+}
+\`\`\`
+
+### Prompt 模板
+
+\`\`\`
+${prompt.template}
+\`\`\`
+
+### 变量说明
+
+${prompt.variables.map(v => `- \`{{${v}}}\`: ${v} 变量`).join('\n')}
+`;
+}
+
+function toPascalCase(str) {
+  return str.split('-').map(s => s.charAt(0).toUpperCase() + s.slice(1)).join('');
+}
+```
+
+---
+
+## 预置 LLM 动作模板
+
+### 1. 代码分析动作
+
+```yaml
+id: llm-code-analysis
+name: LLM Code Analysis
+type: llm
+tool:
+  primary: gemini
+  fallback_chain: [qwen]
+mode: analysis
+prompt:
+  template: |
+    PURPOSE: 分析代码结构和模式，提取关键设计特征
+    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
+    MODE: analysis
+    CONTEXT: {{code_context}}
+    EXPECTED: JSON 格式的分析报告，包含 modules, dependencies, patterns, quality_score
+    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md)
+  variables:
+    - code_context
+input:
+  - collected-code.md
+output: analysis-report.json
+timeout: 900000
+```
+
+### 2. 文档生成动作
+
+```yaml
+id: llm-doc-generation
+name: LLM Documentation Generation
+type: llm
+tool:
+  primary: gemini
+  fallback_chain: [qwen, codex]
+mode: write
+prompt:
+  template: |
+    PURPOSE: 根据分析结果生成高质量文档
+    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
+    MODE: write
+    CONTEXT: {{analysis_report}}
+    EXPECTED: 完整的 Markdown 文档，包含目录、章节、图表
+    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/write-protocol.md)
+  variables:
+    - analysis_report
+input:
+  - analysis-report.json
+output: generated-doc.md
+timeout: 1200000
+```
+
+### 3. 代码重构建议动作
+
+```yaml
+id: llm-refactor-suggest
+name: LLM Refactoring Suggestions
+type: llm
+tool:
+  primary: codex
+  fallback_chain: [gemini]
+mode: analysis
+prompt:
+  template: |
+    PURPOSE: 分析代码并提供重构建议
+    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
+    MODE: analysis
+    CONTEXT: {{source_code}}
+    EXPECTED: 重构建议列表，每项包含 location, issue, suggestion, impact
+    RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md)
+  variables:
+    - source_code
+input:
+  - source-files.md
+output: refactor-suggestions.json
+timeout: 600000
+```
+
+---
+
+## 使用示例
+
+### 在 Phase 中使用 LLM 动作
+
+```javascript
+// phases/02-llm-analysis.md
+
+const llmConfig = {
+  id: 'llm-analyze-skill',
+  name: 'Skill Pattern Analysis',
+  type: 'llm',
+  tool: {
+    primary: 'gemini',
+    fallback_chain: ['qwen']
+  },
+  mode: 'analysis',
+  prompt: {
+    template: `
+PURPOSE: 分析现有 Skill 的设计模式
+TASK:
+• 提取 Skill 结构规范
+• 识别 Phase 组织模式
+• 分析 Agent 调用模式
+MODE: analysis
+CONTEXT: {{skill_source}}
+EXPECTED: 结构化的设计模式分析
+`,
+    variables: ['skill_source']
+  },
+  input: ['collected-skills.md'],
+  output: 'skill-patterns.json'
+};
+
+// 执行
+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
+
+```javascript
+// autonomous-orchestrator 中的 LLM 动作调度
+
+const actions = [
+  { type: 'collect', priority: 100 },
+  { type: 'llm', id: 'llm-analyze', priority: 90 },  // LLM 分析
+  { type: 'process', priority: 80 },
+  { type: 'llm', id: 'llm-generate', priority: 70 }, // LLM 生成
+  { type: 'validate', priority: 60 }
+];
+
+for (const action of sortByPriority(actions)) {
+  if (action.type === 'llm') {
+    const llmResult = await executeLLMAction(
+      getLLMConfig(action.id),
+      context
+    );
+    context.state[action.id] = llmResult;
+  }
+}
+```
+
+---
+
+## 错误处理
+
+```javascript
+async function executeLLMActionWithRetry(config, context, maxRetries = 3) {
+  let lastError = null;
+
+  for (let attempt = 1; attempt <= maxRetries; attempt++) {
+    try {
+      return await executeLLMAction(config, context);
+    } catch (error) {
+      lastError = error;
+      console.log(`Attempt ${attempt} failed: ${error.message}`);
+
+      // 指数退避
+      if (attempt < maxRetries) {
+        await sleep(Math.pow(2, attempt) * 1000);
+      }
+    }
+  }
+
+  // 所有重试失败
+  return {
+    success: false,
+    error: lastError.message,
+    fallback: 'manual_review_required'
+  };
+}
+```
+
+---
+
+## 最佳实践
+
+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
+
+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
+
+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
+
+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