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:
catlog22
2026-01-14 21:40:00 +08:00
parent 266f6f11ec
commit f9c1216eec
7 changed files with 540 additions and 60 deletions

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@@ -70,6 +70,7 @@ Based on comprehensive analysis, skill-tuning addresses **core skill issues** an
| **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)
@@ -202,47 +203,27 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
│ → Initialize state.json with target skill info │
│ → Create backup of target skill files │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-analyze-requirements: Requirement Analysis (NEW)
│ 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: requirement-analysis.json, 自动优化 focus_areas
│ → Output: state.json (requirement_analysis field)
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-context: Context Explosion Analysis
→ Scan for token accumulation patterns
│ → Detect multi-turn dialogue growth
│ → Output: context-diagnosis.json
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-memory: Long-tail Forgetting Analysis │
│ → Trace constraint propagation through phases │
│ → Detect early instruction loss │
│ → Output: memory-diagnosis.json │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-dataflow: Data Flow Analysis │
│ → Map state transitions between phases │
│ → Detect format inconsistencies │
│ → Output: dataflow-diagnosis.json │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-agent: Agent Coordination Analysis │
│ → Analyze agent call patterns │
│ → Detect result passing issues │
│ → Output: agent-diagnosis.json │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-diagnose-docs: Documentation Structure Analysis (Optional) │
│ → Detect definition duplicates across files │
│ → Detect conflicting definitions │
│ → Output: docs-diagnosis.json │
│ 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 │
│ → Merge all diagnosis results
│ → Generate markdown summary from state.diagnosis
│ → Prioritize issues by severity │
│ → Output: tuning-report.md
│ → Output: state.json (final_report field)
├─────────────────────────────────────────────────────────────────────────────┤
│ action-propose-fixes: Fix Proposal Generation │
│ → Generate fix strategies for each issue │
│ → Create implementation plan │
│ → Output: fix-proposals.json
│ → Output: state.json (proposed_fixes field)
├─────────────────────────────────────────────────────────────────────────────┤
│ action-apply-fix: Apply Selected Fix │
│ → User selects fix to apply │
@@ -255,9 +236,9 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
│ → Update iteration count │
├─────────────────────────────────────────────────────────────────────────────┤
│ action-complete: Finalization │
│ → Generate final report
│ → Cleanup temporary files
│ → Output: tuning-summary.md
│ → Set status='completed'
│ → Final report already in state.json (final_report field)
│ → Output: state.json (final)
└─────────────────────────────────────────────────────────────────────────────┘
```
@@ -267,31 +248,25 @@ RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) |
const timestamp = new Date().toISOString().slice(0,19).replace(/[-:T]/g, '');
const workDir = `.workflow/.scratchpad/skill-tuning-${timestamp}`;
Bash(`mkdir -p "${workDir}/diagnosis"`);
// Simplified: Only backups dir needed, diagnosis results go into state.json
Bash(`mkdir -p "${workDir}/backups"`);
Bash(`mkdir -p "${workDir}/fixes"`);
```
## Output Structure
```
.workflow/.scratchpad/skill-tuning-{timestamp}/
├── state.json # Session state (orchestrator-managed)
├── diagnosis/
│ ├── context-diagnosis.json # Context explosion analysis
│ ├── memory-diagnosis.json # Long-tail forgetting analysis
── dataflow-diagnosis.json # Data flow analysis
│ ├── agent-diagnosis.json # Agent coordination analysis
└── docs-diagnosis.json # Documentation structure analysis (optional)
├── backups/
│ └── {skill-name}-backup/ # Original skill files backup
├── fixes/
│ ├── fix-proposals.json # Proposed fixes
│ └── applied-fixes.json # Applied fix history
├── tuning-report.md # Consolidated diagnosis report
└── tuning-summary.md # Final summary
├── state.json # Single source of truth (all results consolidated)
├── diagnosis.* # All diagnosis results embedded
│ ├── issues[] # Found issues
│ ├── proposed_fixes[] # Fix proposals
── final_report # Markdown summary (on completion)
└── backups/
└── {skill-name}-backup/ # Original skill files backup
```
> **Token Optimization**: All outputs consolidated into state.json. No separate diagnosis files or report files.
## State Schema
详细状态结构定义请参阅 [phases/state-schema.md](phases/state-schema.md)。
@@ -316,6 +291,7 @@ Bash(`mkdir -p "${workDir}/fixes"`);
| [phases/actions/action-diagnose-dataflow.md](phases/actions/action-diagnose-dataflow.md) | Data flow diagnosis |
| [phases/actions/action-diagnose-agent.md](phases/actions/action-diagnose-agent.md) | Agent coordination diagnosis |
| [phases/actions/action-diagnose-docs.md](phases/actions/action-diagnose-docs.md) | Documentation structure diagnosis |
| [phases/actions/action-diagnose-token-consumption.md](phases/actions/action-diagnose-token-consumption.md) | Token consumption diagnosis |
| [phases/actions/action-generate-report.md](phases/actions/action-generate-report.md) | Report generation |
| [phases/actions/action-propose-fixes.md](phases/actions/action-propose-fixes.md) | Fix proposal |
| [phases/actions/action-apply-fix.md](phases/actions/action-apply-fix.md) | Fix application |

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@@ -0,0 +1,200 @@
# Action: Diagnose Token Consumption
Analyze target skill for token consumption inefficiencies and output optimization opportunities.
## Purpose
Detect patterns that cause excessive token usage:
- Verbose prompts without compression
- Large state objects with unnecessary fields
- Full content passing instead of references
- Unbounded arrays without sliding windows
- Redundant file I/O (write-then-read patterns)
## Detection Patterns
| Pattern ID | Name | Detection Logic | Severity |
|------------|------|-----------------|----------|
| TKN-001 | Verbose Prompts | Prompt files > 4KB or high static/variable ratio | medium |
| TKN-002 | Excessive State Fields | State schema > 15 top-level keys | medium |
| TKN-003 | Full Content Passing | `Read()` result embedded directly in prompt | high |
| TKN-004 | Unbounded Arrays | `.push`/`concat` without `.slice(-N)` | high |
| TKN-005 | Redundant Write→Read | `Write(file)` followed by `Read(file)` | medium |
## Execution Steps
```javascript
async function diagnoseTokenConsumption(state, workDir) {
const issues = [];
const evidence = [];
const skillPath = state.target_skill.path;
// 1. Scan for verbose prompts (TKN-001)
const mdFiles = Glob(`${skillPath}/**/*.md`);
for (const file of mdFiles) {
const content = Read(file);
if (content.length > 4000) {
evidence.push({
file: file,
pattern: 'TKN-001',
severity: 'medium',
context: `File size: ${content.length} chars (threshold: 4000)`
});
}
}
// 2. Check state schema field count (TKN-002)
const stateSchema = Glob(`${skillPath}/**/state-schema.md`)[0];
if (stateSchema) {
const schemaContent = Read(stateSchema);
const fieldMatches = schemaContent.match(/^\s*\w+:/gm) || [];
if (fieldMatches.length > 15) {
evidence.push({
file: stateSchema,
pattern: 'TKN-002',
severity: 'medium',
context: `State has ${fieldMatches.length} fields (threshold: 15)`
});
}
}
// 3. Detect full content passing (TKN-003)
const fullContentPattern = /Read\([^)]+\)\s*[\+,]|`\$\{.*Read\(/g;
for (const file of mdFiles) {
const content = Read(file);
const matches = content.match(fullContentPattern);
if (matches) {
evidence.push({
file: file,
pattern: 'TKN-003',
severity: 'high',
context: `Full content passing detected: ${matches[0]}`
});
}
}
// 4. Detect unbounded arrays (TKN-004)
const unboundedPattern = /\.(push|concat)\([^)]+\)(?!.*\.slice)/g;
for (const file of mdFiles) {
const content = Read(file);
const matches = content.match(unboundedPattern);
if (matches) {
evidence.push({
file: file,
pattern: 'TKN-004',
severity: 'high',
context: `Unbounded array growth: ${matches[0]}`
});
}
}
// 5. Detect write-then-read patterns (TKN-005)
const writeReadPattern = /Write\([^)]+\)[\s\S]{0,100}Read\([^)]+\)/g;
for (const file of mdFiles) {
const content = Read(file);
const matches = content.match(writeReadPattern);
if (matches) {
evidence.push({
file: file,
pattern: 'TKN-005',
severity: 'medium',
context: `Write-then-read pattern detected`
});
}
}
// Calculate severity
const highCount = evidence.filter(e => e.severity === 'high').length;
const mediumCount = evidence.filter(e => e.severity === 'medium').length;
let severity = 'none';
if (highCount > 0) severity = 'high';
else if (mediumCount > 2) severity = 'medium';
else if (mediumCount > 0) severity = 'low';
return {
status: 'completed',
issues_found: evidence.length,
severity: severity,
execution_time_ms: Date.now() - startTime,
details: {
patterns_checked: ['TKN-001', 'TKN-002', 'TKN-003', 'TKN-004', 'TKN-005'],
patterns_matched: [...new Set(evidence.map(e => e.pattern))],
evidence: evidence,
recommendations: generateRecommendations(evidence)
}
};
}
function generateRecommendations(evidence) {
const recs = [];
const patterns = [...new Set(evidence.map(e => e.pattern))];
if (patterns.includes('TKN-001')) {
recs.push('Apply prompt_compression: Extract static instructions to templates, use placeholders');
}
if (patterns.includes('TKN-002')) {
recs.push('Apply state_field_reduction: Remove debug/cache fields, consolidate related fields');
}
if (patterns.includes('TKN-003')) {
recs.push('Apply lazy_loading: Pass file paths instead of content, let agents read if needed');
}
if (patterns.includes('TKN-004')) {
recs.push('Apply sliding_window: Add .slice(-N) to array operations to bound growth');
}
if (patterns.includes('TKN-005')) {
recs.push('Apply output_minimization: Use in-memory data passing, eliminate temporary files');
}
return recs;
}
```
## Output
Write diagnosis result to `${workDir}/diagnosis/token-consumption-diagnosis.json`:
```json
{
"status": "completed",
"issues_found": 3,
"severity": "medium",
"execution_time_ms": 1500,
"details": {
"patterns_checked": ["TKN-001", "TKN-002", "TKN-003", "TKN-004", "TKN-005"],
"patterns_matched": ["TKN-001", "TKN-003"],
"evidence": [
{
"file": "phases/orchestrator.md",
"pattern": "TKN-001",
"severity": "medium",
"context": "File size: 5200 chars (threshold: 4000)"
}
],
"recommendations": [
"Apply prompt_compression: Extract static instructions to templates"
]
}
}
```
## State Update
```javascript
updateState({
diagnosis: {
...state.diagnosis,
token_consumption: diagnosisResult
}
});
```
## Fix Strategies Mapping
| Pattern | Strategy | Implementation |
|---------|----------|----------------|
| TKN-001 | prompt_compression | Extract static text to variables, use template inheritance |
| TKN-002 | state_field_reduction | Audit and consolidate fields, remove non-essential data |
| TKN-003 | lazy_loading | Pass paths instead of content, agents load when needed |
| TKN-004 | sliding_window | Add `.slice(-N)` after push/concat operations |
| TKN-005 | output_minimization | Use return values instead of file relay |

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@@ -93,7 +93,7 @@ function selectNextAction(state) {
}
// 4. Run diagnosis in order (only if not completed)
const diagnosisOrder = ['context', 'memory', 'dataflow', 'agent', 'docs'];
const diagnosisOrder = ['context', 'memory', 'dataflow', 'agent', 'docs', 'token_consumption'];
for (const diagType of diagnosisOrder) {
if (state.diagnosis[diagType] === null) {
@@ -221,6 +221,7 @@ async function runOrchestrator(workDir) {
console.log(`[Loop ${iteration}] Executing: ${actionId}`);
// 3. Update state: current action
// FIX CTX-001: sliding window for action_history (keep last 10)
updateState({
current_action: actionId,
action_history: [...state.action_history, {
@@ -229,13 +230,24 @@ async function runOrchestrator(workDir) {
completed_at: null,
result: null,
output_files: []
}]
}].slice(-10) // Sliding window: prevent unbounded growth
});
// 4. Execute action
try {
const actionPrompt = Read(`phases/actions/${actionId}.md`);
const stateJson = JSON.stringify(state, null, 2);
// FIX CTX-003: Pass state path + key fields only instead of full state
const stateKeyInfo = {
status: state.status,
iteration_count: state.iteration_count,
issues_by_severity: state.issues_by_severity,
quality_gate: state.quality_gate,
current_action: state.current_action,
completed_actions: state.completed_actions,
user_issue_description: state.user_issue_description,
target_skill: { name: state.target_skill.name, path: state.target_skill.path }
};
const stateKeyJson = JSON.stringify(stateKeyInfo, null, 2);
const result = await Task({
subagent_type: 'universal-executor',
@@ -245,8 +257,12 @@ async function runOrchestrator(workDir) {
You are executing action "${actionId}" for skill-tuning workflow.
Work directory: ${workDir}
[STATE]
${stateJson}
[STATE KEY INFO]
${stateKeyJson}
[FULL STATE PATH]
${workDir}/state.json
(Read full state from this file if you need additional fields)
[ACTION INSTRUCTIONS]
${actionPrompt}
@@ -295,6 +311,7 @@ After completing the action:
console.log(`[Loop ${iteration}] Error in ${actionId}: ${error.message}`);
// Error handling
// FIX CTX-002: sliding window for errors (keep last 5)
updateState({
current_action: null,
errors: [...state.errors, {
@@ -302,7 +319,7 @@ After completing the action:
message: error.message,
timestamp: new Date().toISOString(),
recoverable: true
}],
}].slice(-5), // Sliding window: prevent unbounded growth
error_count: state.error_count + 1
});
}

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@@ -31,6 +31,7 @@ interface TuningState {
dataflow: DiagnosisResult | null;
agent: DiagnosisResult | null;
docs: DocsDiagnosisResult | null; // 文档结构诊断
token_consumption: DiagnosisResult | null; // Token消耗诊断
};
// === Issues Found ===
@@ -69,6 +70,9 @@ interface TuningState {
work_dir: string;
backup_dir: string;
// === Final Report (consolidated output) ===
final_report: string | null; // Markdown summary generated on completion
// === 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
}
```

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@@ -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": {

View File

@@ -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 |

View File

@@ -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