mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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633d918da1
- Introduced quality gates specification for skill tuning, detailing quality dimensions, scoring, and gate definitions. - Added comprehensive tuning strategies for various issue categories, including context explosion, long-tail forgetting, data flow, and agent coordination. - Created templates for diagnosis reports and fix proposals to standardize documentation and reporting processes.
323 lines
9.0 KiB
Markdown
323 lines
9.0 KiB
Markdown
# Action: Gemini Analysis
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动态调用 Gemini CLI 进行深度分析，根据用户需求或诊断结果选择分析类型。
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## Role
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- 接收用户指定的分析需求或从诊断结果推断需求
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- 构建适当的 CLI 命令
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- 执行分析并解析结果
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- 更新状态以供后续动作使用
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## Preconditions
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- `state.status === 'running'`
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- 满足以下任一条件:
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- `state.gemini_analysis_requested === true` (用户请求)
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- `state.issues.some(i => i.severity === 'critical')` (发现严重问题)
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- `state.analysis_type !== null` (已指定分析类型)
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## Analysis Types
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### 1. root_cause - 问题根因分析
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针对用户描述的问题进行深度分析。
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```javascript
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const analysisPrompt = `
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PURPOSE: Identify root cause of skill execution issue: ${state.user_issue_description}
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TASK:
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• Analyze skill structure at: ${state.target_skill.path}
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• Identify anti-patterns in phase files
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• Trace data flow through state management
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• Check agent coordination patterns
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: JSON with structure:
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{
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"root_causes": [
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{ "id": "RC-001", "description": "...", "severity": "high", "evidence": ["file:line"] }
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],
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"patterns_found": [
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{ "pattern": "...", "type": "anti-pattern|best-practice", "locations": [] }
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],
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"recommendations": [
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{ "priority": 1, "action": "...", "rationale": "..." }
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]
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}
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RULES: Focus on execution flow, state management, agent coordination
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`;
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```
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### 2. architecture - 架构审查
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评估 skill 的整体架构设计。
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```javascript
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const analysisPrompt = `
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PURPOSE: Review skill architecture for: ${state.target_skill.name}
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TASK:
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• Evaluate phase decomposition and responsibility separation
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• Check state schema design and data flow
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• Assess agent coordination and error handling
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• Review scalability and maintainability
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: Markdown report with sections:
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- Executive Summary
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- Phase Architecture Assessment
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- State Management Evaluation
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- Agent Coordination Analysis
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- Improvement Recommendations (prioritized)
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RULES: Focus on modularity, extensibility, maintainability
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`;
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```
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### 3. prompt_optimization - 提示词优化
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分析和优化 phase 中的提示词。
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```javascript
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const analysisPrompt = `
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PURPOSE: Optimize prompts in skill phases for better output quality
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TASK:
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• Analyze existing prompts for clarity and specificity
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• Identify ambiguous instructions
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• Check output format specifications
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• Evaluate constraint communication
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MODE: analysis
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CONTEXT: @phases/**/*.md
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EXPECTED: JSON with structure:
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{
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"prompt_issues": [
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{ "file": "...", "issue": "...", "severity": "...", "suggestion": "..." }
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],
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"optimized_prompts": [
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{ "file": "...", "original": "...", "optimized": "...", "rationale": "..." }
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]
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}
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RULES: Preserve intent, improve clarity, add structured output requirements
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`;
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```
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### 4. performance - 性能分析
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分析 Token 消耗和执行效率。
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```javascript
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const analysisPrompt = `
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PURPOSE: Analyze performance bottlenecks in skill execution
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TASK:
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• Estimate token consumption per phase
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• Identify redundant data passing
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• Check for unnecessary full-content transfers
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• Evaluate caching opportunities
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: JSON with structure:
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{
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"token_estimates": [
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{ "phase": "...", "estimated_tokens": 1000, "breakdown": {} }
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],
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"bottlenecks": [
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{ "type": "...", "location": "...", "impact": "high|medium|low", "fix": "..." }
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],
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"optimization_suggestions": []
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}
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RULES: Focus on token efficiency, reduce redundancy
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`;
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```
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### 5. custom - 自定义分析
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用户指定的自定义分析需求。
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```javascript
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const analysisPrompt = `
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PURPOSE: ${state.custom_analysis_purpose}
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TASK: ${state.custom_analysis_tasks}
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: ${state.custom_analysis_expected}
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RULES: ${state.custom_analysis_rules || 'Follow best practices'}
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`;
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```
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## Execution
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```javascript
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async function executeGeminiAnalysis(state, workDir) {
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// 1. 确定分析类型
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const analysisType = state.analysis_type || determineAnalysisType(state);
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// 2. 构建 prompt
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const prompt = buildAnalysisPrompt(analysisType, state);
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// 3. 构建 CLI 命令
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const cliCommand = `ccw cli -p "${escapeForShell(prompt)}" --tool gemini --mode analysis --cd "${state.target_skill.path}"`;
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console.log(`Executing Gemini analysis: ${analysisType}`);
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console.log(`Command: ${cliCommand}`);
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// 4. 执行 CLI (后台运行)
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const result = Bash({
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command: cliCommand,
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run_in_background: true,
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timeout: 300000 // 5 minutes
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});
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// 5. 等待结果
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// 注意: 根据 CLAUDE.md 指引，CLI 后台执行后应停止轮询
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// 结果会在 CLI 完成后写入 state
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return {
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stateUpdates: {
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gemini_analysis: {
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type: analysisType,
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status: 'running',
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started_at: new Date().toISOString(),
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task_id: result.task_id
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}
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},
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outputFiles: [],
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summary: `Gemini ${analysisType} analysis started in background`
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};
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}
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function determineAnalysisType(state) {
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// 根据状态推断分析类型
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if (state.user_issue_description && state.user_issue_description.length > 100) {
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return 'root_cause';
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}
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if (state.issues.some(i => i.severity === 'critical')) {
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return 'root_cause';
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}
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if (state.focus_areas.includes('architecture')) {
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return 'architecture';
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}
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if (state.focus_areas.includes('prompt')) {
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return 'prompt_optimization';
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}
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if (state.focus_areas.includes('performance')) {
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return 'performance';
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}
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return 'root_cause'; // 默认
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}
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function buildAnalysisPrompt(type, state) {
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const templates = {
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root_cause: () => `
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PURPOSE: Identify root cause of skill execution issue: ${state.user_issue_description}
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TASK: • Analyze skill structure • Identify anti-patterns • Trace data flow issues • Check agent coordination
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: JSON { root_causes: [], patterns_found: [], recommendations: [] }
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RULES: Focus on execution flow, be specific about file:line locations
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`,
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architecture: () => `
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PURPOSE: Review skill architecture for ${state.target_skill.name}
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TASK: • Evaluate phase decomposition • Check state design • Assess agent coordination • Review extensibility
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: Markdown architecture assessment report
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RULES: Focus on modularity and maintainability
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`,
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prompt_optimization: () => `
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PURPOSE: Optimize prompts in skill for better output quality
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TASK: • Analyze prompt clarity • Check output specifications • Evaluate constraint handling
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MODE: analysis
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CONTEXT: @phases/**/*.md
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EXPECTED: JSON { prompt_issues: [], optimized_prompts: [] }
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RULES: Preserve intent, improve clarity
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`,
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performance: () => `
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PURPOSE: Analyze performance bottlenecks in skill
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TASK: • Estimate token consumption • Identify redundancy • Check data transfer efficiency
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: JSON { token_estimates: [], bottlenecks: [], optimization_suggestions: [] }
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RULES: Focus on token efficiency
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`,
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custom: () => `
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PURPOSE: ${state.custom_analysis_purpose}
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TASK: ${state.custom_analysis_tasks}
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MODE: analysis
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CONTEXT: @**/*.md
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EXPECTED: ${state.custom_analysis_expected}
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RULES: ${state.custom_analysis_rules || 'Best practices'}
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`
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};
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return templates[type]();
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}
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function escapeForShell(str) {
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// 转义 shell 特殊字符
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return str.replace(/"/g, '\\"').replace(/\$/g, '\\$').replace(/`/g, '\\`');
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}
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```
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## Output
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### State Updates
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```javascript
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{
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gemini_analysis: {
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type: 'root_cause' | 'architecture' | 'prompt_optimization' | 'performance' | 'custom',
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status: 'running' | 'completed' | 'failed',
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started_at: '2024-01-01T00:00:00Z',
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completed_at: '2024-01-01T00:05:00Z',
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task_id: 'xxx',
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result: { /* 分析结果 */ },
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error: null
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},
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// 分析结果合并到 issues
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issues: [
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...state.issues,
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...newIssuesFromAnalysis
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]
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}
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```
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### Output Files
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- `${workDir}/diagnosis/gemini-analysis-${type}.json` - 原始分析结果
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- `${workDir}/diagnosis/gemini-analysis-${type}.md` - 格式化报告
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## Post-Execution
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分析完成后:
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1. 解析 CLI 输出为结构化数据
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2. 提取新发现的 issues 合并到 state.issues
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3. 更新 recommendations 到 state
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4. 触发下一步动作 (通常是 action-generate-report 或 action-propose-fixes)
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## Error Handling
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| Error | Recovery |
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|-------|----------|
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| CLI 超时 | 重试一次，仍失败则跳过 Gemini 分析 |
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| 解析失败 | 保存原始输出，手动处理 |
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| 无结果 | 标记为 skipped，继续流程 |
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## User Interaction
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如果 `state.analysis_type === null` 且无法自动推断，询问用户:
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```javascript
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AskUserQuestion({
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questions: [{
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question: '请选择 Gemini 分析类型',
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header: '分析类型',
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options: [
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{ label: '问题根因分析', description: '深度分析用户描述的问题' },
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{ label: '架构审查', description: '评估整体架构设计' },
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{ label: '提示词优化', description: '分析和优化 phase 提示词' },
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{ label: '性能分析', description: '分析 Token 消耗和执行效率' }
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],
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multiSelect: false
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}]
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});
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```
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