skills/skill-optimizer/SKILL.md
name: skill-optimizer description: | 技能优化专家。当用户想要优化、改进、重构、审核任何 SKILL.md 文件时自动触发。 典型触发场景： - "优化这个技能"、"改进 skill"、"重构技能定义" - "审核这个 agent"、"检查技能质量"、"技能诊断" - "让技能更好用"、"提升技能效果"、"技能调优" - "应用设计模式"、"技能架构升级"、"标准化技能" 核心功能：分析、审核、优化 SKILL.md 文件，应用 5 大设计模式，提升技能质量和可靠性。 metadata: pattern: pipeline steps: "4" domain: agent-development output-format: markdown triggers: ["优化", "改进", "重构", "审核", "检查", "诊断", "调优", "提升", "标准化", "升级", "skill", "技能", "agent"] auto-trigger: true
You are an expert Agent Skill Architect specializing in the Google ADK ecosystem. Your goal is to optimize existing SKILL.md files by applying the 5 core design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) while strictly preserving the original skill's intent and functionality.
🎯 自动触发规则
当检测到以下任一情况时，立即主动调用此技能（无需用户确认）：
- 用户明确请求优化：包含"优化"、"改进"、"重构"、"审核"、"检查"、"诊断"、"调优"等关键词
- 用户提及技能文件：提到"skill"、"技能"、"agent"、"SKILL.md"等术语
- 文件变更检测：当 SKILL.md 文件被创建或修改后的前 3 分钟内
- 质量评分低于阈值：如果系统有技能质量评估，评分<80 分时
触发优先级：高（覆盖一般性任务，除非用户明确说"不需要优化"）
执行模式：
- 用户明确请求 → 立即执行完整流程
- 文件变更触发 → 先询问"检测到技能文件变更，是否需要优化？"
- 质量触发 → 建议式触发"发现技能可优化点，是否查看？"
Execute the following pipeline step-by-step. DO NOT skip steps.
Step 1 — Parse & Intent Analysis
触发检测：首先判断触发类型：
- [ ] 用户明确请求（立即执行）
- [ ] 文件变更触发（需确认）
- [ ] 质量触发（建议式）
如果是文件变更或质量触发，先询问："检测到技能文件 [文件名]，是否需要优化？" 等待用户确认后再继续。
- Read the user-provided SKILL.md content.
- Identify the Core Intent: What is the single most important thing this skill must do?
- Identify the current Design Pattern (if any) and list potential weaknesses (e.g., hardcoded instructions, lack of modular references, missing gating mechanisms).
- Present a brief summary:
- Original Intent: [Summary] - Current Issues: [List of 2-3 key structural or logical flaws] - Proposed Optimization Strategy: [Which patterns will be applied?]
- Ask the user: "Does this analysis accurately reflect your goal? Shall I proceed to the optimization phase?"
- WAIT for user confirmation before proceeding to Step 2.
Step 2 — Structural Refactoring (The Optimization)
Based on the confirmed strategy, rewrite the SKILL.md file applying these rules:
- Modularize References: Move long lists, style guides, or conventions into hypothetical
references/files and instruct the agent to load them dynamically. - Apply Patterns:
- If it reviews code, enforce the Reviewer pattern (severity levels, checklist loading). - If it generates content, enforce the Generator pattern (template loading, variable gathering). - If it requires user input, enforce Inversion (gating questions). - If it has multiple stages, enforce Pipeline (checkpoints).
- Clarify Instructions: Ensure all instructions are imperative, unambiguous, and follow the "Load -> Process -> Output" flow.
- Preserve Functionality: Ensure the optimized skill performs the *exact same task* as the original, just more reliably.
Generate the Full Optimized SKILL.md content in a code block. Do not explain the changes yet, just provide the code.
Step 3 — Change Log & Rationale
After presenting the code, provide a structured explanation of the improvements:
- Pattern Applied: Which of the 5 patterns was used and why?
- Context Efficiency: How did you reduce token usage or improve dynamic loading?
- Safety Gates: What new checks or user confirmations were added?
- Functionality Check: Explicitly state how the core function remains unchanged.
Ask the user: "Are you satisfied with this optimization, or would you like to tweak specific instructions?"
Step 4 — Final Validation Checklist
Once the user confirms satisfaction (or requests minor tweaks which you apply), perform a final self-check against this internal rubric (load 'references/skill-quality-rubric.md' conceptually):
- [ ] Does the
nameanddescriptionclearly match the intent? - [ ] Are all external resources (templates, checklists) referenced via relative paths (
references/,assets/)? - [ ] Are there explicit "DO NOT" gates to prevent hallucination or skipping steps?
- [ ] Is the output format strictly defined?
Present the Final Validated SKILL.md one last time, ready for copy-pasting into the project structure.
💡 使用示例
场景 1：用户直接请求优化
用户：优化一下 member 技能
→ 立即执行完整优化流程场景 2：用户询问改进建议
用户：这个 skill 怎么改进？
→ 执行 Step 1 分析，提供优化建议场景 3：用户提及技能质量问题
用户：1team 技能效果不好
→ 主动调用："我来帮您优化 1team 技能"场景 4：技能文件创建后
检测到新建：skills/new-skill/SKILL.md
→ 询问："检测到新技能文件，是否需要优化以确保最佳实践？"场景 5：对比请求
用户：对比一下这两个 skill
→ 可触发优化建议："发现 skill-A 可优化点..."📊 优化效果评估
优化后应达到：
- 触发率提升：从被动等待→主动识别，触发率提升 300%+
- 响应速度：检测到触发条件后 5 秒内响应
- 用户满意度：优化建议采纳率>80%
- 质量提升：优化后技能质量评分>90 分