mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
feat: 添加令牌消耗诊断功能,优化输出和状态管理
This commit is contained in:
@@ -70,6 +70,7 @@ Based on comprehensive analysis, skill-tuning addresses **core skill issues** an
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| **P2** | Agent Coordination | Fragile call chains, merge complexity | error_wrapping, result_validation |
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| **P3** | Context Explosion | Token accumulation, multi-turn bloat | sliding_window, context_summarization |
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| **P4** | Long-tail Forgetting | Early constraint loss | constraint_injection, checkpoint_restore |
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| **P5** | Token Consumption | Verbose prompts, excessive state, redundant I/O | prompt_compression, lazy_loading, output_minimization |
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### General Optimization Areas (按需分析 via Gemini CLI)
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@@ -202,47 +203,27 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
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│ → Initialize state.json with target skill info │
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│ → Create backup of target skill files │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-analyze-requirements: Requirement Analysis (NEW) │
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│ action-analyze-requirements: Requirement Analysis │
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│ → Phase 1: 维度拆解 (Gemini CLI) - 单一描述 → 多个关注维度 │
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│ → Phase 2: Spec 匹配 - 每个维度 → taxonomy + strategy │
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│ → Phase 3: 覆盖度评估 - 以"有修复策略"为满足标准 │
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│ → Phase 4: 歧义检测 - 识别多义性描述,必要时请求澄清 │
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│ → Output: requirement-analysis.json, 自动优化 focus_areas │
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│ → Output: state.json (requirement_analysis field) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-diagnose-context: Context Explosion Analysis │
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│ → Scan for token accumulation patterns │
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│ → Detect multi-turn dialogue growth │
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│ → Output: context-diagnosis.json │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-diagnose-memory: Long-tail Forgetting Analysis │
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│ → Trace constraint propagation through phases │
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│ → Detect early instruction loss │
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│ → Output: memory-diagnosis.json │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-diagnose-dataflow: Data Flow Analysis │
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│ → Map state transitions between phases │
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│ → Detect format inconsistencies │
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│ → Output: dataflow-diagnosis.json │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-diagnose-agent: Agent Coordination Analysis │
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│ → Analyze agent call patterns │
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│ → Detect result passing issues │
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│ → Output: agent-diagnosis.json │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-diagnose-docs: Documentation Structure Analysis (Optional) │
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│ → Detect definition duplicates across files │
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│ → Detect conflicting definitions │
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│ → Output: docs-diagnosis.json │
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│ action-diagnose-*: Diagnosis Actions (context/memory/dataflow/agent/docs/ │
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│ token_consumption) │
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│ → Execute pattern-based detection for each category │
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│ → Output: state.json (diagnosis.{category} field) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-generate-report: Consolidated Report │
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│ → Merge all diagnosis results │
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│ → Generate markdown summary from state.diagnosis │
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│ → Prioritize issues by severity │
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│ → Output: tuning-report.md │
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│ → Output: state.json (final_report field) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-propose-fixes: Fix Proposal Generation │
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│ → Generate fix strategies for each issue │
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│ → Create implementation plan │
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│ → Output: fix-proposals.json │
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│ → Output: state.json (proposed_fixes field) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-apply-fix: Apply Selected Fix │
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│ → User selects fix to apply │
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@@ -255,9 +236,9 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
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│ → Update iteration count │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ action-complete: Finalization │
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│ → Generate final report │
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│ → Cleanup temporary files │
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│ → Output: tuning-summary.md │
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│ → Set status='completed' │
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│ → Final report already in state.json (final_report field) │
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│ → Output: state.json (final) │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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@@ -267,31 +248,25 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
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const timestamp = new Date().toISOString().slice(0,19).replace(/[-:T]/g, '');
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const workDir = `.workflow/.scratchpad/skill-tuning-${timestamp}`;
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Bash(`mkdir -p "${workDir}/diagnosis"`);
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// Simplified: Only backups dir needed, diagnosis results go into state.json
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Bash(`mkdir -p "${workDir}/backups"`);
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Bash(`mkdir -p "${workDir}/fixes"`);
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```
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## Output Structure
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```
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.workflow/.scratchpad/skill-tuning-{timestamp}/
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├── state.json # Session state (orchestrator-managed)
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├── diagnosis/
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│ ├── context-diagnosis.json # Context explosion analysis
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│ ├── memory-diagnosis.json # Long-tail forgetting analysis
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│ ├── dataflow-diagnosis.json # Data flow analysis
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│ ├── agent-diagnosis.json # Agent coordination analysis
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│ └── docs-diagnosis.json # Documentation structure analysis (optional)
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├── backups/
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│ └── {skill-name}-backup/ # Original skill files backup
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├── fixes/
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│ ├── fix-proposals.json # Proposed fixes
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│ └── applied-fixes.json # Applied fix history
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├── tuning-report.md # Consolidated diagnosis report
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└── tuning-summary.md # Final summary
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├── state.json # Single source of truth (all results consolidated)
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│ ├── diagnosis.* # All diagnosis results embedded
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│ ├── issues[] # Found issues
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│ ├── proposed_fixes[] # Fix proposals
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│ └── final_report # Markdown summary (on completion)
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└── backups/
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└── {skill-name}-backup/ # Original skill files backup
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```
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> **Token Optimization**: All outputs consolidated into state.json. No separate diagnosis files or report files.
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## State Schema
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详细状态结构定义请参阅 [phases/state-schema.md](phases/state-schema.md)。
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@@ -316,6 +291,7 @@ Bash(`mkdir -p "${workDir}/fixes"`);
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| [phases/actions/action-diagnose-dataflow.md](phases/actions/action-diagnose-dataflow.md) | Data flow diagnosis |
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| [phases/actions/action-diagnose-agent.md](phases/actions/action-diagnose-agent.md) | Agent coordination diagnosis |
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| [phases/actions/action-diagnose-docs.md](phases/actions/action-diagnose-docs.md) | Documentation structure diagnosis |
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| [phases/actions/action-diagnose-token-consumption.md](phases/actions/action-diagnose-token-consumption.md) | Token consumption diagnosis |
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| [phases/actions/action-generate-report.md](phases/actions/action-generate-report.md) | Report generation |
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| [phases/actions/action-propose-fixes.md](phases/actions/action-propose-fixes.md) | Fix proposal |
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| [phases/actions/action-apply-fix.md](phases/actions/action-apply-fix.md) | Fix application |
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@@ -0,0 +1,200 @@
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# Action: Diagnose Token Consumption
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Analyze target skill for token consumption inefficiencies and output optimization opportunities.
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## Purpose
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Detect patterns that cause excessive token usage:
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- Verbose prompts without compression
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- Large state objects with unnecessary fields
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- Full content passing instead of references
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- Unbounded arrays without sliding windows
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- Redundant file I/O (write-then-read patterns)
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## Detection Patterns
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| Pattern ID | Name | Detection Logic | Severity |
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|------------|------|-----------------|----------|
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| TKN-001 | Verbose Prompts | Prompt files > 4KB or high static/variable ratio | medium |
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| TKN-002 | Excessive State Fields | State schema > 15 top-level keys | medium |
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| TKN-003 | Full Content Passing | `Read()` result embedded directly in prompt | high |
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| TKN-004 | Unbounded Arrays | `.push`/`concat` without `.slice(-N)` | high |
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| TKN-005 | Redundant Write→Read | `Write(file)` followed by `Read(file)` | medium |
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## Execution Steps
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```javascript
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async function diagnoseTokenConsumption(state, workDir) {
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const issues = [];
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const evidence = [];
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const skillPath = state.target_skill.path;
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// 1. Scan for verbose prompts (TKN-001)
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const mdFiles = Glob(`${skillPath}/**/*.md`);
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for (const file of mdFiles) {
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const content = Read(file);
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if (content.length > 4000) {
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evidence.push({
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file: file,
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pattern: 'TKN-001',
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severity: 'medium',
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context: `File size: ${content.length} chars (threshold: 4000)`
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});
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}
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}
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// 2. Check state schema field count (TKN-002)
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const stateSchema = Glob(`${skillPath}/**/state-schema.md`)[0];
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if (stateSchema) {
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const schemaContent = Read(stateSchema);
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const fieldMatches = schemaContent.match(/^\s*\w+:/gm) || [];
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if (fieldMatches.length > 15) {
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evidence.push({
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file: stateSchema,
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pattern: 'TKN-002',
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severity: 'medium',
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context: `State has ${fieldMatches.length} fields (threshold: 15)`
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});
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}
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}
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// 3. Detect full content passing (TKN-003)
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const fullContentPattern = /Read\([^)]+\)\s*[\+,]|`\$\{.*Read\(/g;
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for (const file of mdFiles) {
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const content = Read(file);
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const matches = content.match(fullContentPattern);
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if (matches) {
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evidence.push({
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file: file,
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pattern: 'TKN-003',
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severity: 'high',
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context: `Full content passing detected: ${matches[0]}`
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});
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}
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}
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// 4. Detect unbounded arrays (TKN-004)
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const unboundedPattern = /\.(push|concat)\([^)]+\)(?!.*\.slice)/g;
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for (const file of mdFiles) {
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const content = Read(file);
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const matches = content.match(unboundedPattern);
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if (matches) {
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evidence.push({
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file: file,
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pattern: 'TKN-004',
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severity: 'high',
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context: `Unbounded array growth: ${matches[0]}`
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});
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}
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}
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// 5. Detect write-then-read patterns (TKN-005)
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const writeReadPattern = /Write\([^)]+\)[\s\S]{0,100}Read\([^)]+\)/g;
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for (const file of mdFiles) {
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const content = Read(file);
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const matches = content.match(writeReadPattern);
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if (matches) {
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evidence.push({
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file: file,
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pattern: 'TKN-005',
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severity: 'medium',
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context: `Write-then-read pattern detected`
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});
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}
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}
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// Calculate severity
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const highCount = evidence.filter(e => e.severity === 'high').length;
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const mediumCount = evidence.filter(e => e.severity === 'medium').length;
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let severity = 'none';
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if (highCount > 0) severity = 'high';
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else if (mediumCount > 2) severity = 'medium';
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else if (mediumCount > 0) severity = 'low';
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return {
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status: 'completed',
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issues_found: evidence.length,
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severity: severity,
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execution_time_ms: Date.now() - startTime,
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details: {
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patterns_checked: ['TKN-001', 'TKN-002', 'TKN-003', 'TKN-004', 'TKN-005'],
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patterns_matched: [...new Set(evidence.map(e => e.pattern))],
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evidence: evidence,
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recommendations: generateRecommendations(evidence)
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}
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};
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}
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function generateRecommendations(evidence) {
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const recs = [];
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const patterns = [...new Set(evidence.map(e => e.pattern))];
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if (patterns.includes('TKN-001')) {
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recs.push('Apply prompt_compression: Extract static instructions to templates, use placeholders');
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}
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if (patterns.includes('TKN-002')) {
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recs.push('Apply state_field_reduction: Remove debug/cache fields, consolidate related fields');
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}
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if (patterns.includes('TKN-003')) {
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recs.push('Apply lazy_loading: Pass file paths instead of content, let agents read if needed');
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}
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if (patterns.includes('TKN-004')) {
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recs.push('Apply sliding_window: Add .slice(-N) to array operations to bound growth');
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}
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if (patterns.includes('TKN-005')) {
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recs.push('Apply output_minimization: Use in-memory data passing, eliminate temporary files');
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}
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return recs;
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}
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```
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## Output
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Write diagnosis result to `${workDir}/diagnosis/token-consumption-diagnosis.json`:
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```json
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{
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"status": "completed",
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"issues_found": 3,
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"severity": "medium",
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"execution_time_ms": 1500,
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"details": {
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"patterns_checked": ["TKN-001", "TKN-002", "TKN-003", "TKN-004", "TKN-005"],
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"patterns_matched": ["TKN-001", "TKN-003"],
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"evidence": [
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{
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"file": "phases/orchestrator.md",
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"pattern": "TKN-001",
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"severity": "medium",
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"context": "File size: 5200 chars (threshold: 4000)"
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}
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],
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"recommendations": [
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"Apply prompt_compression: Extract static instructions to templates"
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]
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}
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}
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```
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## State Update
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```javascript
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updateState({
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diagnosis: {
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...state.diagnosis,
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token_consumption: diagnosisResult
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}
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});
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```
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## Fix Strategies Mapping
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| Pattern | Strategy | Implementation |
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|---------|----------|----------------|
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| TKN-001 | prompt_compression | Extract static text to variables, use template inheritance |
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| TKN-002 | state_field_reduction | Audit and consolidate fields, remove non-essential data |
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| TKN-003 | lazy_loading | Pass paths instead of content, agents load when needed |
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| TKN-004 | sliding_window | Add `.slice(-N)` after push/concat operations |
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| TKN-005 | output_minimization | Use return values instead of file relay |
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@@ -93,7 +93,7 @@ function selectNextAction(state) {
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}
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// 4. Run diagnosis in order (only if not completed)
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const diagnosisOrder = ['context', 'memory', 'dataflow', 'agent', 'docs'];
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const diagnosisOrder = ['context', 'memory', 'dataflow', 'agent', 'docs', 'token_consumption'];
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for (const diagType of diagnosisOrder) {
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if (state.diagnosis[diagType] === null) {
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@@ -221,6 +221,7 @@ async function runOrchestrator(workDir) {
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console.log(`[Loop ${iteration}] Executing: ${actionId}`);
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// 3. Update state: current action
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// FIX CTX-001: sliding window for action_history (keep last 10)
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updateState({
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current_action: actionId,
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action_history: [...state.action_history, {
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@@ -229,13 +230,24 @@ async function runOrchestrator(workDir) {
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completed_at: null,
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result: null,
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output_files: []
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}]
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}].slice(-10) // Sliding window: prevent unbounded growth
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});
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// 4. Execute action
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try {
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const actionPrompt = Read(`phases/actions/${actionId}.md`);
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const stateJson = JSON.stringify(state, null, 2);
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// FIX CTX-003: Pass state path + key fields only instead of full state
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const stateKeyInfo = {
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status: state.status,
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iteration_count: state.iteration_count,
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issues_by_severity: state.issues_by_severity,
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quality_gate: state.quality_gate,
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current_action: state.current_action,
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completed_actions: state.completed_actions,
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user_issue_description: state.user_issue_description,
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target_skill: { name: state.target_skill.name, path: state.target_skill.path }
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};
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const stateKeyJson = JSON.stringify(stateKeyInfo, null, 2);
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const result = await Task({
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subagent_type: 'universal-executor',
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@@ -245,8 +257,12 @@ async function runOrchestrator(workDir) {
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You are executing action "${actionId}" for skill-tuning workflow.
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Work directory: ${workDir}
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[STATE]
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${stateJson}
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[STATE KEY INFO]
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${stateKeyJson}
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[FULL STATE PATH]
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${workDir}/state.json
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(Read full state from this file if you need additional fields)
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[ACTION INSTRUCTIONS]
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${actionPrompt}
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@@ -295,6 +311,7 @@ After completing the action:
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console.log(`[Loop ${iteration}] Error in ${actionId}: ${error.message}`);
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// Error handling
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// FIX CTX-002: sliding window for errors (keep last 5)
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updateState({
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current_action: null,
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errors: [...state.errors, {
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@@ -302,7 +319,7 @@ After completing the action:
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message: error.message,
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timestamp: new Date().toISOString(),
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recoverable: true
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}],
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}].slice(-5), // Sliding window: prevent unbounded growth
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error_count: state.error_count + 1
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});
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}
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@@ -31,6 +31,7 @@ interface TuningState {
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dataflow: DiagnosisResult | null;
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agent: DiagnosisResult | null;
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docs: DocsDiagnosisResult | null; // 文档结构诊断
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token_consumption: DiagnosisResult | null; // Token消耗诊断
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};
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// === Issues Found ===
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@@ -69,6 +70,9 @@ interface TuningState {
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work_dir: string;
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backup_dir: string;
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||||
// === Final Report (consolidated output) ===
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final_report: string | null; // Markdown summary generated on completion
|
||||
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||||
// === Requirement Analysis (新增) ===
|
||||
requirement_analysis: RequirementAnalysis | null;
|
||||
}
|
||||
@@ -176,7 +180,7 @@ interface Evidence {
|
||||
|
||||
interface Issue {
|
||||
id: string; // e.g., "ISS-001"
|
||||
type: 'context_explosion' | 'memory_loss' | 'dataflow_break' | 'agent_failure';
|
||||
type: 'context_explosion' | 'memory_loss' | 'dataflow_break' | 'agent_failure' | 'token_consumption';
|
||||
severity: 'critical' | 'high' | 'medium' | 'low';
|
||||
priority: number; // 1 = highest
|
||||
location: {
|
||||
@@ -214,6 +218,10 @@ type FixStrategy =
|
||||
| 'schema_enforcement' // Add data contract validation
|
||||
| 'orchestrator_refactor' // Refactor agent coordination
|
||||
| 'state_centralization' // Centralize state management
|
||||
| 'prompt_compression' // Extract static text, use templates
|
||||
| 'lazy_loading' // Pass paths instead of content
|
||||
| 'output_minimization' // Return minimal structured JSON
|
||||
| 'state_field_reduction' // Audit and consolidate state fields
|
||||
| 'custom'; // Custom fix
|
||||
|
||||
interface FileChange {
|
||||
@@ -270,7 +278,8 @@ interface ErrorEntry {
|
||||
"memory": null,
|
||||
"dataflow": null,
|
||||
"agent": null,
|
||||
"docs": null
|
||||
"docs": null,
|
||||
"token_consumption": null
|
||||
},
|
||||
"issues": [],
|
||||
"issues_by_severity": {
|
||||
@@ -294,6 +303,7 @@ interface ErrorEntry {
|
||||
"max_errors": 3,
|
||||
"work_dir": null,
|
||||
"backup_dir": null,
|
||||
"final_report": null,
|
||||
"requirement_analysis": null
|
||||
}
|
||||
```
|
||||
|
||||
@@ -93,6 +93,14 @@
|
||||
"detection_focus": null,
|
||||
"needs_gemini_analysis": true,
|
||||
"priority_order": [1, 2, 3, 4]
|
||||
},
|
||||
"token_consumption": {
|
||||
"pattern_ids": ["TKN-001", "TKN-002", "TKN-003", "TKN-004", "TKN-005"],
|
||||
"severity_hint": "medium",
|
||||
"strategies": ["prompt_compression", "lazy_loading", "output_minimization", "state_field_reduction", "sliding_window"],
|
||||
"risk_levels": ["low", "low", "low", "low", "low"],
|
||||
"detection_focus": "Verbose prompts, excessive state fields, full content passing, unbounded arrays, redundant I/O",
|
||||
"priority_order": [1, 2, 3, 4, 5]
|
||||
}
|
||||
},
|
||||
"keywords": {
|
||||
@@ -149,7 +157,12 @@
|
||||
"中间文件": "authoring_principles_violation",
|
||||
"临时文件": "authoring_principles_violation",
|
||||
"文件中转": "authoring_principles_violation",
|
||||
"state膨胀": "authoring_principles_violation"
|
||||
"state膨胀": "authoring_principles_violation",
|
||||
"token消耗": "token_consumption",
|
||||
"token优化": "token_consumption",
|
||||
"产出简化": "token_consumption",
|
||||
"冗长": "token_consumption",
|
||||
"精简": "token_consumption"
|
||||
},
|
||||
"english": {
|
||||
"token": "context_explosion",
|
||||
@@ -198,7 +211,12 @@
|
||||
"feedback": "user_experience",
|
||||
"intermediate": "authoring_principles_violation",
|
||||
"temp": "authoring_principles_violation",
|
||||
"relay": "authoring_principles_violation"
|
||||
"relay": "authoring_principles_violation",
|
||||
"verbose": "token_consumption",
|
||||
"minimize": "token_consumption",
|
||||
"compress": "token_consumption",
|
||||
"simplify": "token_consumption",
|
||||
"reduction": "token_consumption"
|
||||
}
|
||||
},
|
||||
"category_labels": {
|
||||
@@ -213,6 +231,7 @@
|
||||
"output_quality": "Output Quality",
|
||||
"user_experience": "User Experience",
|
||||
"authoring_principles_violation": "Authoring Principles Violation",
|
||||
"token_consumption": "Token Consumption",
|
||||
"custom": "Custom"
|
||||
},
|
||||
"category_labels_chinese": {
|
||||
@@ -227,6 +246,7 @@
|
||||
"output_quality": "Output Quality",
|
||||
"user_experience": "User Experience",
|
||||
"authoring_principles_violation": "Authoring Principles Violation",
|
||||
"token_consumption": "Token Consumption Optimization",
|
||||
"custom": "Other Issues"
|
||||
},
|
||||
"category_descriptions": {
|
||||
@@ -241,12 +261,13 @@
|
||||
"output_quality": "Output validation or completeness issues",
|
||||
"user_experience": "Interaction or feedback clarity issues",
|
||||
"authoring_principles_violation": "Violation of skill authoring principles",
|
||||
"token_consumption": "Excessive token usage from verbose prompts, large state objects, or redundant I/O patterns",
|
||||
"custom": "Requires custom analysis"
|
||||
},
|
||||
"fix_priority_order": {
|
||||
"P0": ["dataflow_break", "authoring_principles_violation"],
|
||||
"P1": ["agent_failure"],
|
||||
"P2": ["context_explosion"],
|
||||
"P2": ["context_explosion", "token_consumption"],
|
||||
"P3": ["memory_loss"]
|
||||
},
|
||||
"cross_category_dependencies": {
|
||||
|
||||
@@ -180,7 +180,35 @@ Classification of skill execution issues with detection patterns and severity cr
|
||||
|
||||
---
|
||||
|
||||
### 6. Documentation Conflict (P6)
|
||||
### 6. Token Consumption (P6)
|
||||
|
||||
**Definition**: Excessive token usage from verbose prompts, large state objects, or inefficient I/O patterns.
|
||||
|
||||
**Root Causes**:
|
||||
- Long static prompts without compression
|
||||
- State schema with too many fields
|
||||
- Full content embedding instead of path references
|
||||
- Arrays growing unbounded without sliding windows
|
||||
- Write-then-read file relay patterns
|
||||
|
||||
**Detection Patterns**:
|
||||
|
||||
| Pattern ID | Regex/Check | Description |
|
||||
|------------|-------------|-------------|
|
||||
| TKN-001 | File size > 4KB | Verbose prompt files |
|
||||
| TKN-002 | State fields > 15 | Excessive state schema |
|
||||
| TKN-003 | `/Read\([^)]+\)\s*[\+,]/` | Full content passing |
|
||||
| TKN-004 | `/.push\|concat(?!.*\.slice)/` | Unbounded array growth |
|
||||
| TKN-005 | `/Write\([^)]+\)[\s\S]{0,100}Read\([^)]+\)/` | Write-then-read pattern |
|
||||
|
||||
**Impact Levels**:
|
||||
- **High**: Multiple TKN-003/TKN-004 issues causing significant token waste
|
||||
- **Medium**: Several verbose files or state bloat
|
||||
- **Low**: Minor optimization opportunities
|
||||
|
||||
---
|
||||
|
||||
### 7. Documentation Conflict (P7)
|
||||
|
||||
**Definition**: 同一概念在不同文件中定义不一致,导致行为不可预测和文档误导。
|
||||
|
||||
@@ -262,6 +290,7 @@ function calculateIssueSeverity(issue) {
|
||||
| Long-tail Forgetting | constraint_injection, state_constraints_field, checkpoint | 1, 2, 3 |
|
||||
| Data Flow Disruption | state_centralization, schema_enforcement, field_normalization | 1, 2, 3 |
|
||||
| Agent Coordination | error_wrapping, result_validation, flatten_nesting | 1, 2, 3 |
|
||||
| **Token Consumption** | prompt_compression, lazy_loading, output_minimization, state_field_reduction | 1, 2, 3, 4 |
|
||||
| **Documentation Redundancy** | consolidate_to_ssot, centralize_mapping_config | 1, 2 |
|
||||
| **Documentation Conflict** | reconcile_conflicting_definitions | 1 |
|
||||
|
||||
|
||||
@@ -1308,3 +1308,230 @@ async function checkpoint(name, summary, options) {
|
||||
return response;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Token Consumption Strategies
|
||||
|
||||
Strategies for reducing token usage and simplifying skill outputs.
|
||||
|
||||
---
|
||||
|
||||
### Strategy: prompt_compression
|
||||
|
||||
**Purpose**: Reduce verbose prompts by extracting static text and using templates.
|
||||
|
||||
**Implementation**:
|
||||
```javascript
|
||||
// Before: Long inline prompt
|
||||
const prompt = `
|
||||
You are an expert code analyzer specializing in identifying patterns.
|
||||
Your role is to examine the provided code and identify any issues.
|
||||
Please follow these detailed instructions carefully:
|
||||
1. Read the code thoroughly
|
||||
2. Identify any anti-patterns
|
||||
3. Check for security vulnerabilities
|
||||
... (continues for many lines)
|
||||
|
||||
Code to analyze:
|
||||
${fullCodeContent}
|
||||
`;
|
||||
|
||||
// After: Compressed with template reference
|
||||
const PROMPT_TEMPLATE_PATH = 'templates/analyzer-prompt.md';
|
||||
|
||||
const prompt = `
|
||||
[TEMPLATE: ${PROMPT_TEMPLATE_PATH}]
|
||||
[CODE_PATH: ${codePath}]
|
||||
[FOCUS: patterns, security]
|
||||
`;
|
||||
|
||||
// Or use key instructions only
|
||||
const prompt = `
|
||||
Analyze ${codePath} for: patterns, security, performance.
|
||||
Return JSON: { issues: [], severity: string }
|
||||
`;
|
||||
```
|
||||
|
||||
**Risk**: Low
|
||||
**Verification**: Compare token count before/after compression
|
||||
|
||||
---
|
||||
|
||||
### Strategy: lazy_loading
|
||||
|
||||
**Purpose**: Pass file paths instead of full content, let consumers load if needed.
|
||||
|
||||
**Implementation**:
|
||||
```javascript
|
||||
// Before: Full content in prompt
|
||||
const content = Read(filePath);
|
||||
const prompt = `Analyze this content:\n${content}`;
|
||||
|
||||
// After: Path reference with lazy loading instruction
|
||||
const prompt = `
|
||||
Analyze file at: ${filePath}
|
||||
(Read the file content if you need to examine it)
|
||||
Return: { summary: string, issues: [] }
|
||||
`;
|
||||
|
||||
// For agent calls
|
||||
const result = await Task({
|
||||
subagent_type: 'universal-executor',
|
||||
prompt: `
|
||||
[FILE_PATH]: ${dataPath}
|
||||
[TASK]: Analyze the file and extract key metrics.
|
||||
[NOTE]: Read the file only if needed for your analysis.
|
||||
`
|
||||
});
|
||||
```
|
||||
|
||||
**Risk**: Low
|
||||
**Verification**: Verify agents can still access required data
|
||||
|
||||
---
|
||||
|
||||
### Strategy: output_minimization
|
||||
|
||||
**Purpose**: Configure agents to return minimal, structured output instead of verbose text.
|
||||
|
||||
**Implementation**:
|
||||
```javascript
|
||||
// Before: Verbose output expectation
|
||||
const prompt = `
|
||||
Analyze the code and provide a detailed report including:
|
||||
- Executive summary
|
||||
- Detailed findings with explanations
|
||||
- Code examples for each issue
|
||||
- Recommendations with rationale
|
||||
...
|
||||
`;
|
||||
|
||||
// After: Minimal structured output
|
||||
const prompt = `
|
||||
Analyze the code. Return ONLY this JSON:
|
||||
{
|
||||
"status": "pass|review|fail",
|
||||
"issues": [{ "id": string, "severity": string, "file": string, "line": number }],
|
||||
"summary": "one sentence"
|
||||
}
|
||||
Do not include explanations or code examples.
|
||||
`;
|
||||
|
||||
// Validation
|
||||
const result = JSON.parse(agentOutput);
|
||||
if (!result.status || !Array.isArray(result.issues)) {
|
||||
throw new Error('Invalid output format');
|
||||
}
|
||||
```
|
||||
|
||||
**Risk**: Low
|
||||
**Verification**: JSON.parse succeeds, output size reduced
|
||||
|
||||
---
|
||||
|
||||
### Strategy: state_field_reduction
|
||||
|
||||
**Purpose**: Audit and consolidate state fields to minimize serialization overhead.
|
||||
|
||||
**Implementation**:
|
||||
```typescript
|
||||
// Before: Bloated state
|
||||
interface State {
|
||||
status: string;
|
||||
target: TargetInfo;
|
||||
user_input: string;
|
||||
parsed_input: ParsedInput; // Remove - temporary
|
||||
intermediate_result: any; // Remove - not persisted
|
||||
debug_info: DebugInfo; // Remove - debugging only
|
||||
analysis_cache: any; // Remove - session cache
|
||||
full_history: HistoryEntry[]; // Remove - unbounded
|
||||
step1_output: any; // Remove - intermediate
|
||||
step2_output: any; // Remove - intermediate
|
||||
step3_output: any; // Remove - intermediate
|
||||
final_result: FinalResult;
|
||||
error_log: string[]; // Remove - debugging
|
||||
metrics: Metrics; // Remove - optional
|
||||
}
|
||||
|
||||
// After: Minimal state (≤15 fields)
|
||||
interface State {
|
||||
status: 'pending' | 'running' | 'completed' | 'failed';
|
||||
target: { name: string; path: string };
|
||||
input_summary: string; // Summarized user input
|
||||
result_path: string; // Path to final result
|
||||
quality_gate: 'pass' | 'fail';
|
||||
error?: string; // Only if failed
|
||||
}
|
||||
```
|
||||
|
||||
**Audit Checklist**:
|
||||
```javascript
|
||||
function auditStateFields(stateSchema) {
|
||||
const removeCandidates = [];
|
||||
|
||||
for (const [key, type] of Object.entries(stateSchema)) {
|
||||
// Identify removal candidates
|
||||
if (key.startsWith('debug_')) removeCandidates.push(key);
|
||||
if (key.endsWith('_cache')) removeCandidates.push(key);
|
||||
if (key.endsWith('_temp')) removeCandidates.push(key);
|
||||
if (key.includes('intermediate')) removeCandidates.push(key);
|
||||
if (key.includes('step') && key.includes('output')) removeCandidates.push(key);
|
||||
}
|
||||
|
||||
return {
|
||||
total_fields: Object.keys(stateSchema).length,
|
||||
remove_candidates: removeCandidates,
|
||||
estimated_reduction: removeCandidates.length
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
**Risk**: Medium (ensure no essential data removed)
|
||||
**Verification**: State field count ≤ 15, all essential data preserved
|
||||
|
||||
---
|
||||
|
||||
### Strategy: in_memory_consolidation
|
||||
|
||||
**Purpose**: Consolidate outputs into single file, eliminate redundant report files.
|
||||
|
||||
**Implementation**:
|
||||
```javascript
|
||||
// Before: Multiple output files
|
||||
Write(`${workDir}/diagnosis-report.md`, reportMarkdown);
|
||||
Write(`${workDir}/diagnosis-summary.json`, summaryJson);
|
||||
Write(`${workDir}/state.json`, JSON.stringify(state));
|
||||
Write(`${workDir}/tuning-report.md`, tuningReport);
|
||||
Write(`${workDir}/tuning-summary.md`, finalSummary);
|
||||
|
||||
// After: Single source of truth
|
||||
const consolidatedState = {
|
||||
...state,
|
||||
final_report: {
|
||||
summary: summaryJson,
|
||||
details_available_in_state: true,
|
||||
generated_at: new Date().toISOString()
|
||||
}
|
||||
};
|
||||
Write(`${workDir}/state.json`, JSON.stringify(consolidatedState, null, 2));
|
||||
|
||||
// Report can be rendered from state on-demand
|
||||
function renderReport(state) {
|
||||
return `
|
||||
# Tuning Report: ${state.target_skill.name}
|
||||
Status: ${state.status}
|
||||
Quality: ${state.quality_gate}
|
||||
Issues: ${state.issues.length}
|
||||
...
|
||||
`;
|
||||
}
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
- Single file to read/write
|
||||
- No data duplication
|
||||
- On-demand rendering
|
||||
|
||||
**Risk**: Low
|
||||
**Verification**: Only state.json exists as output, rendering works correctly
|
||||
|
||||
Reference in New Issue
Block a user