| name | skill-tuning |
| description | Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Gemini CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug". |
| allowed-tools | Task, AskUserQuestion, Read, Write, Bash, Glob, Grep, mcp__ace-tool__search_context |
Resource Map
基准路径: .claude/skills/code-quality-specialist/references/domains/skill-quality/tuning/
tuning/
├── phases/
│ ├── actions/
│ │ ├── action-abort.md
│ │ ├── action-analyze-requirements.md
│ │ ├── action-apply-fix.md
│ │ ├── action-complete.md
│ │ ├── action-diagnose-agent.md
│ │ ├── action-diagnose-context.md
│ │ ├── action-diagnose-dataflow.md
│ │ ├── action-diagnose-docs.md
│ │ ├── action-diagnose-memory.md
│ │ ├── action-diagnose-token-consumption.md
│ │ ├── action-gemini-analysis.md
│ │ ├── action-generate-report.md
│ │ ├── action-init.md
│ │ ├── action-propose-fixes.md
│ │ └── action-verify.md
│ ├── orchestrator.md
│ └── state-schema.md
├── specs/
│ ├── category-mappings.json
│ ├── dimension-mapping.md
│ ├── problem-taxonomy.md
│ ├── quality-gates.md
│ ├── skill-authoring-principles.md
│ └── tuning-strategies.md
├── templates/
│ ├── diagnosis-report.md
│ └── fix-proposal.md
└── SKILL.md
Skill Tuning
Universal skill diagnosis and optimization tool that identifies and resolves skill execution problems through iterative multi-agent analysis.
Architecture Overview
┌─────────────────────────────────────────────────────────────────────────────┐
│ Skill Tuning Architecture (Autonomous Mode + Gemini CLI) │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⚠️ Phase 0: Specification → 阅读规范 + 理解目标 skill 结构 (强制前置) │
│ Study │
│ ↓ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ Orchestrator (状态驱动决策) │ │
│ │ 读取诊断状态 → 选择下一步动作 → 执行 → 更新状态 → 循环直到完成 │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────┬───────────┼───────────┬────────────┬────────────┐ │
│ ↓ ↓ ↓ ↓ ↓ ↓ │
│ ┌──────┐ ┌──────────┐ ┌─────────┐ ┌────────┐ ┌────────┐ ┌─────────┐ │
│ │ Init │→ │ Analyze │→ │Diagnose │ │Diagnose│ │Diagnose│ │ Gemini │ │
│ │ │ │Requiremts│ │ Context │ │ Memory │ │DataFlow│ │Analysis │ │
│ └──────┘ └──────────┘ └─────────┘ └────────┘ └────────┘ └─────────┘ │
│ │ │ │ │ │ │
│ │ └───────────┴───────────┴────────────┘ │
│ ↓ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ Requirement Analysis (NEW) │ │
│ │ • Phase 1: 维度拆解 (Gemini CLI) - 单一描述 → 多个关注维度 │ │
│ │ • Phase 2: Spec 匹配 - 每个维度 → taxonomy + strategy │ │
│ │ • Phase 3: 覆盖度评估 - 以"有修复策略"为满足标准 │ │
│ │ • Phase 4: 歧义检测 - 识别多义性描述，必要时请求澄清 │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ ↓ │
│ ┌──────────────────┐ │
│ │ Apply Fixes + │ │
│ │ Verify Results │ │
│ └──────────────────┘ │
│ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ Gemini CLI Integration │ │
│ │ 根据用户需求动态调用 gemini cli 进行深度分析: │ │
│ │ • 需求维度拆解 (requirement decomposition) │ │
│ │ • 复杂问题分析 (prompt engineering, architecture review) │ │
│ │ • 代码模式识别 (pattern matching, anti-pattern detection) │ │
│ │ • 修复策略生成 (fix generation, refactoring suggestions) │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Problem Domain
Based on comprehensive analysis, skill-tuning addresses core skill issues and general optimization areas:
Core Skill Issues (自动检测)
| Priority | Problem | Root Cause | Solution Strategy |
|---|
| P0 | Authoring Principles Violation | 中间文件存储, State膨胀, 文件中转 | eliminate_intermediate_files, minimize_state, context_passing |
| P1 | Data Flow Disruption | Scattered state, inconsistent formats | state_centralization, schema_enforcement |
| P2 | Agent Coordination | Fragile call chains, merge complexity | error_wrapping, result_validation |
| P3 | Context Explosion | Token accumulation, multi-turn bloat | sliding_window, context_summarization |
| P4 | Long-tail Forgetting | Early constraint loss | constraint_injection, checkpoint_restore |
| P5 | Token Consumption | Verbose prompts, excessive state, redundant I/O | prompt_compression, lazy_loading, output_minimization |
General Optimization Areas (按需分析 via Gemini CLI)
| Category | Issues | Gemini Analysis Scope |
|---|
| Prompt Engineering | 模糊指令, 输出格式不一致, 幻觉风险 | 提示词优化, 结构化输出设计 |
| Architecture | 阶段划分不合理, 依赖混乱, 扩展性差 | 架构审查, 模块化建议 |
| Performance | 执行慢, Token消耗高, 重复计算 | 性能分析, 缓存策略 |
| Error Handling | 错误恢复不当, 无降级策略, 日志不足 | 容错设计, 可观测性增强 |
| Output Quality | 输出不稳定, 格式漂移, 质量波动 | 质量门控, 验证机制 |
| User Experience | 交互不流畅, 反馈不清晰, 进度不可见 | UX优化, 进度追踪 |
Key Design Principles
- Problem-First Diagnosis: Systematic identification before any fix attempt
- Data-Driven Analysis: Record execution traces, token counts, state snapshots
- Iterative Refinement: Multiple tuning rounds until quality gates pass
- Non-Destructive: All changes are reversible with backup checkpoints
- Agent Coordination: Use specialized sub-agents for each diagnosis type
- Gemini CLI On-Demand: Deep analysis via CLI for complex/custom issues
Gemini CLI Integration
根据用户需求动态调用 Gemini CLI 进行深度分析。
Trigger Conditions
| Condition | Action | CLI Mode |
|---|
| 用户描述复杂问题 | 调用 Gemini 分析问题根因 | analysis |
| 自动诊断发现 critical 问题 | 请求深度分析确认 | analysis |
| 用户请求架构审查 | 执行架构分析 | analysis |
| 需要生成修复代码 | 生成修复提案 | write |
| 标准策略不适用 | 请求定制化策略 | analysis |
CLI Command Template
ccw cli -p "
PURPOSE: ${purpose}
TASK: ${task_steps}
MODE: ${mode}
CONTEXT: @${skill_path}/**/*
EXPECTED: ${expected_output}
RULES: $(cat ~/.claude/workflows/cli-templates/protocols/${mode}-protocol.md) | ${constraints}
" --tool gemini --mode ${mode} --cd ${skill_path}
Analysis Types
1. Problem Root Cause Analysis
ccw cli -p "
PURPOSE: Identify root cause of skill execution issue: ${user_issue_description}
TASK: • Analyze skill structure and phase flow • Identify anti-patterns • Trace data flow issues
MODE: analysis
CONTEXT: @**/*.md
EXPECTED: JSON with { root_causes: [], patterns_found: [], recommendations: [] }
RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) | Focus on execution flow
" --tool gemini --mode analysis
2. Architecture Review
ccw cli -p "
PURPOSE: Review skill architecture for scalability and maintainability
TASK: • Evaluate phase decomposition • Check state management patterns • Assess agent coordination
MODE: analysis
CONTEXT: @**/*.md
EXPECTED: Architecture assessment with improvement recommendations
RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) | Focus on modularity
" --tool gemini --mode analysis
3. Fix Strategy Generation
ccw cli -p "
PURPOSE: Generate fix strategy for issue: ${issue_id} - ${issue_description}
TASK: • Analyze issue context • Design fix approach • Generate implementation plan
MODE: analysis
CONTEXT: @**/*.md
EXPECTED: JSON with { strategy: string, changes: [], verification_steps: [] }
RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) | Minimal invasive changes
" --tool gemini --mode analysis
Mandatory Prerequisites
CRITICAL: Read these documents before executing any action.
Core Specs (Required)
Templates (Reference)
Execution Flow
┌─────────────────────────────────────────────────────────────────────────────┐
│ Phase 0: Specification Study (强制前置 - 禁止跳过) │
│ → Read: specs/problem-taxonomy.md (问题分类) │
│ → Read: specs/tuning-strategies.md (调优策略) │
│ → Read: specs/dimension-mapping.md (维度映射规则) │
│ → Read: Target skill's SKILL.md and phases/*.md │
│ → Output: 内化规范，理解目标 skill 结构 │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-init: Initialize Tuning Session │
│ → Create work directory: .workflow/.scratchpad/skill-tuning-{timestamp} │
│ → Initialize state.json with target skill info │
│ → Create backup of target skill files │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-analyze-requirements: Requirement Analysis │
│ → Phase 1: 维度拆解 (Gemini CLI) - 单一描述 → 多个关注维度 │
│ → Phase 2: Spec 匹配 - 每个维度 → taxonomy + strategy │
│ → Phase 3: 覆盖度评估 - 以"有修复策略"为满足标准 │
│ → Phase 4: 歧义检测 - 识别多义性描述，必要时请求澄清 │
│ → Output: state.json (requirement_analysis field) │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-*: Diagnosis Actions (context/memory/dataflow/agent/docs/ │
│ token_consumption) │
│ → Execute pattern-based detection for each category │
│ → Output: state.json (diagnosis.{category} field) │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-generate-report: Consolidated Report │
│ → Generate markdown summary from state.diagnosis │
│ → Prioritize issues by severity │
│ → Output: state.json (final_report field) │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-propose-fixes: Fix Proposal Generation │
│ → Generate fix strategies for each issue │
│ → Create implementation plan │
│ → Output: state.json (proposed_fixes field) │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-apply-fix: Apply Selected Fix │
│ → User selects fix to apply │
│ → Execute fix with backup │
│ → Update state with fix result │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-verify: Verification │
│ → Re-run affected diagnosis │
│ → Check quality gates │
│ → Update iteration count │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-complete: Finalization │
│ → Set status='completed' │
│ → Final report already in state.json (final_report field) │
│ → Output: state.json (final) │
└─────────────────────────────────────────────────────────────────────────────┘
Directory Setup
const timestamp = new Date().toISOString().slice(0,19).replace(/[-:T]/g, '');
const workDir = `.workflow/.scratchpad/skill-tuning-${timestamp}`;
Bash(`mkdir -p "${workDir}/backups"`);
Output Structure
.workflow/.scratchpad/skill-tuning-{timestamp}/
├── state.json # Single source of truth (all results consolidated)
│ ├── diagnosis.* # All diagnosis results embedded
│ ├── issues[] # Found issues
│ ├── proposed_fixes[] # Fix proposals
│ └── final_report # Markdown summary (on completion)
└── backups/
└── {skill-name}-backup/ # Original skill files backup
Token Optimization: All outputs consolidated into state.json. No separate diagnosis files or report files.
State Schema
详细状态结构定义请参阅 phases/state-schema.md。
核心状态字段：
status: 工作流状态 (pending/running/completed/failed)target_skill: 目标 skill 信息diagnosis: 各维度诊断结果issues: 发现的问题列表proposed_fixes: 建议的修复方案
Reference Documents
