mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-11 02:33:51 +08:00
主要变更: - 模板重命名:采用优先级前缀(01-通用, 02-专用, 03-领域特定) - 目录调整:bug-diagnosis从development移至analysis - 引用更新:5个命令文件中21处模板引用更新为新路径 - 路径统一:所有引用统一使用完整路径格式 模板变更详情: - analysis/:8个模板(01-trace-code-execution, 01-diagnose-bug-root-cause等) - development/:5个模板(02-implement-feature, 02-refactor-codebase等) - planning/:5个模板(01-plan-architecture-design, 02-breakdown-task-steps等) - memory/:1个模板(02-document-module-structure) 命令文件更新: - cli/mode/bug-diagnosis.md(6处引用) - cli/mode/code-analysis.md(6处引用) - cli/mode/plan.md(6处引用) - task/execute.md(1处引用) - workflow/tools/test-task-generate.md(2处引用) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
30 lines
1.3 KiB
Plaintext
30 lines
1.3 KiB
Plaintext
Analyze performance characteristics and optimization opportunities.
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## CORE CHECKLIST ⚡
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□ Focus on measurable metrics (e.g., latency, memory, CPU usage)
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□ Provide file:line references for all identified bottlenecks
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□ Distinguish between algorithmic and resource-based issues
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□ Apply RULES template requirements exactly as specified
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## REQUIRED ANALYSIS
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1. Identify performance bottlenecks and resource usage patterns
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2. Assess algorithm efficiency and data structure choices
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3. Evaluate caching strategies and optimization techniques
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4. Review memory management and resource cleanup
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5. Document performance metrics and improvement opportunities
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## OUTPUT REQUIREMENTS
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- Performance bottleneck identification with specific file:line locations
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- Algorithm complexity analysis and optimization suggestions
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- Caching pattern documentation and recommendations
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- Memory usage patterns and optimization opportunities
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- Prioritized list of performance improvements
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## VERIFICATION CHECKLIST ✓
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□ All CONTEXT files analyzed for performance characteristics
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□ Every bottleneck is backed by a code reference (file:line)
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□ Both algorithmic and resource-related issues are covered
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□ Recommendations are prioritized by potential impact
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Focus: Measurable performance improvements and concrete optimization strategies.
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