mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
synced 2026-09-19 08:45:47 +08:00
1e3fbb5ea8
* SwarmMemoryManager Refactoring Plan * fix: add missing directories to npm package (v1.9.3) Added templates/, .claude/helpers/, and docs/reference/ to package.json files array. These were missing from the npm distribution, causing `aqe init` to fail with "Template file not found" errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * checkpoint improvements * fix: critical memory/learning system fixes for v1.9.4 ## Critical Fixes - Vector embeddings now stored correctly (was storing NULL) - SQL parameter style fixed for agentdb's SqlJsDatabase wrapper - HNSW index schema mismatch resolved with pattern_id column - Hooks now save to correct database (.agentic-qe/agentdb.db) ## New Features - Added 5 SwarmMemoryManager methods for learning data retrieval: - getBestAction() - Q-learning best action selection - getRecentLearningExperiences() - Recent experience retrieval - getLearningExperiencesByTaskType() - Task-filtered experiences - getHighRewardExperiences() - Successful experience extraction - getLearningStats() - Aggregate learning statistics - Added comprehensive integration test for memory/learning loop - Added RuVector pattern store integration (experimental) ## Technical Details - Vector embeddings: 384 dimensions × 4 bytes = 1,536 bytes per pattern - All 12 integration tests pass - HNSW index: 150x faster vector search enabled 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: move ARM64-only ruvector packages to optionalDependencies Fixes CI failures caused by platform-specific dependencies: - @ruvector/node-linux-arm64-gnu (ARM64 only) - ruvector-core-linux-arm64-gnu (ARM64 only) These packages caused npm ci to fail on x64 CI runners with: "EBADPLATFORM - wanted linux/arm64, current linux/x64" Solution: Move ARM64 packages to optionalDependencies and add x64 variants for cross-platform compatibility. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore: regenerate package-lock.json with x64 ruvector packages Updates lockfile to include optional x64 platform packages for CI compatibility: - @ruvector/node-linux-x64-gnu@0.1.17 - ruvector-core-linux-x64-gnu@0.1.15 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: replace fake QUIC with proper implementation and clean up skills manifest QUIC Transport: - Remove fake HTTP/2-based QUICTransport.ts that pretended to be QUIC - Add proper QUIC implementation via Rust/WASM (@agentic-flow/quic-wasm) - Add WebSocket fallback transport for when WASM unavailable - Add dedicated type declarations for optional WASM module - Features: 0-RTT connection, stream multiplexing, TLS 1.3 built-in Skills Manifest: - Rewrite skills-manifest.json with only QE skills (41 skills) - Remove Claude Flow platform skills (agentdb-*, flow-nexus-*, github-*, etc.) - Organize into proper categories: qe-core, testing-methodologies, etc. Bug Fixes: - Fix UnifiedMemoryCoordinator.ts TypeScript errors - Use Logger.getInstance() instead of private constructor - Use swarmMemory.query() instead of non-existent list() method - Use correct close() method signatures for backends 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add QX Partner agent, compact subagents, fix CLAUDE.md dynamic counts - Add QX Partner agent for Quality Experience analysis combining QA + UX perspectives - Compact 9 subagent definitions to ~80 lines each (88% reduction) - Fix CLAUDE.md template to dynamically count agents (including subagents) and skills - Move CLAUDE.md creation after parallel phases for accurate counts - Remove 'Production-Ready' claim from README and CHANGELOG - Clean up backup files and move docs to proper directory Initialization now correctly reports: - 31 QE Agents (20 main + 11 subagents) - 53 QE Skills - 8 Slash Commands 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: resolve CI failures and security alert #36 - Replace MD5 with SHA-256 for ETag calculation (security alert #36) - Fix CRLF line endings in shell scripts causing parse errors - Remove 'local' keyword from for-loop in test-integration-batched.sh - Use dynamic process.cwd() instead of hardcoded path in jest.config.js - Enhance quality-gate.ts to parse test logs when results file missing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: Complete Agentic QE Fleet v2.1 implementation (closes #101) ## Implemented Features ### Error Recovery Enhancement (Task 1.3) - CircuitBreaker class with state management (CLOSED/OPEN/HALF_OPEN) - RetryStrategy with exponential/linear/constant backoff - RecoveryOrchestrator with 6 recovery strategies - Transport fallback mechanisms ### Dynamic Skill Loader (Task 2.2) - Metadata-only initial loading (98% token reduction) - On-demand full skill retrieval - LRU caching with configurable TTL (30min default) - Preload suggestions for critical workflows ### Swarm Learning Optimizer (Task 4.1) - Topology recommendation engine (hierarchical/mesh/ring/star) - Agent allocation with capability matching and load balancing - Bottleneck detection (agent/memory/coordination/IO) - Real-time performance adjustments - Added getLatestRecommendation() method ### Workflow Orchestrator (Task 4.2) - AdaptiveScheduler with dynamic strategy selection - Priority-based task queue with age boosting - Task dependency resolution - Work stealing and backpressure handling - Added getQueueDepth() and getStatus() methods ### Unified Memory Coordinator (Task 4.3) - Consolidated memory managers (SQLite, AgentDB, Vector, JSON) - Automatic failover chain with health monitoring - Batch operations and namespace isolation - Periodic sync between backends ### SwarmCoordinator Improvements - Real CPU utilization tracking via process.cpuUsage() - Queue depth from WorkflowOrchestrator - Optimizer recommendations in feedback data - Calculated average complexity from metrics ## Additional Changes - Migration documentation and test restructuring plan - GitHub issue templates and workflows - Test suite improvements and cleanup 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: optimize 38 QE skills with agent-focused format (#102) - Add enhanced YAML frontmatter (tokenEstimate, agents, tags, etc.) - Add <default_to_action> blocks for immediate actionable guidance - Add Quick Reference Cards with tables for at-a-glance info - Add Agent Integration examples using Task() pattern - Add Agent Coordination Hints (memory namespace + fleet coordination) - Condense content optimized for agent consumption - Reduce total token footprint by ~65% across skills Skills updated: - Core QE: agentic-quality-engineering, holistic-testing-pact - Testing Types: api-testing, chaos-engineering, compatibility, compliance, contract, database, exploratory, localization, mobile, mutation, performance, regression, security, visual-testing - Methodologies: tdd-london-chicago, xp-practices, context-driven, risk-based, shift-left, shift-right - Analysis: code-review-quality, quality-metrics, test-design-techniques - Strategy: test-automation-strategy, test-data-management, test-environment-management, test-reporting-analytics - CI/CD: cicd-pipeline-qe-orchestrator, accessibility-testing - Communication: brutal-honesty-review, bug-reporting-excellence, consultancy-practices, sherlock-review, six-thinking-hats, technical-writing - Refactoring: refactoring-patterns Also includes: package.json updates, docs/optimization/, docs/templates/, scripts/validate-skill-optimization.ts Skipped: testability-scoring (pending PR from @fndlalit) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * release: v2.1.0 - Comprehensive QX Analysis & Skills Optimization ## Summary - Comprehensive QX Analysis with 23+ heuristics (PR #104 by @fndlalit) - 38 QE Skills optimized with agent-focused format (PR #102) - Testability Scoring skill v2.1 with optimized format - QX Partner Agent v2.1 with enhanced capabilities - Fix MCP SDK compatibility for @modelcontextprotocol/sdk bump (PR #105) ## Changes - .claude/agents/qx-partner.md: Updated to v2.1 with domain-specific detection - .claude/skills/testability-scoring/SKILL.md: Optimized format (59% token reduction) - src/mcp/server.ts: Removed deprecated 'description' field for MCP SDK compatibility - package.json: Bumped version to 2.1.0 - README.md: Updated version and release notes - CHANGELOG.md: Added v2.1.0 release notes 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
820 lines
24 KiB
TypeScript
820 lines
24 KiB
TypeScript
#!/usr/bin/env ts-node
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/**
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* Skill Optimization Validation Script
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*
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* Validates Agentic QE skills against optimization standards for Claude 4.5.
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* Checks for required sections, token estimates, agent associations, and generates
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* optimization reports with before/after metrics.
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*
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* Usage:
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* npm run validate-skill [skill-path] # Single skill
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* npm run validate-all-skills # All 41 skills
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* npm run validate-skill -- --report # Generate detailed report
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* npm run validate-skill -- --fix # Auto-fix common issues
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*
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* @version 1.0.0
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* @date 2025-12-02
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*/
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import * as fs from 'fs';
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import * as path from 'path';
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import * as yaml from 'js-yaml';
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// ============================================================================
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// Type Definitions
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// ============================================================================
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interface SkillMetadata {
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name: string;
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description: string;
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category: string;
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priority: 'critical' | 'high' | 'medium' | 'low';
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tokenEstimate: number;
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agents: string[];
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implementation_status: 'draft' | 'baseline' | 'optimized' | 'production';
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optimization_version?: string;
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last_optimized?: string;
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dependencies?: string[];
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quick_reference_card?: boolean;
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tags?: string[];
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}
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interface ValidationResult {
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skillPath: string;
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skillName: string;
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passed: boolean;
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errors: ValidationError[];
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warnings: ValidationWarning[];
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metrics: SkillMetrics;
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sections: SectionChecks;
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optimization: OptimizationMetrics;
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}
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interface ValidationError {
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type: string;
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message: string;
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severity: 'critical' | 'high' | 'medium';
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fixable: boolean;
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}
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interface ValidationWarning {
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type: string;
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message: string;
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recommendation: string;
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}
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interface SkillMetrics {
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estimatedTokens: number;
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actualWordCount: number;
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sectionCount: number;
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codeBlockCount: number;
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hasQuickReference: boolean;
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hasAgentCoordination: boolean;
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hasDefaultToAction: boolean;
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}
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interface SectionChecks {
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yamlFrontmatter: boolean;
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defaultToAction: boolean;
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quickReferenceCard: boolean;
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agentCoordination: boolean;
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coreContent: boolean;
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examples: boolean;
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relatedSkills: boolean;
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}
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interface OptimizationMetrics {
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status: string;
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baselineTokens?: number;
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currentTokens: number;
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reductionPercent?: number;
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targetMet: boolean;
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lastOptimized?: string;
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}
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interface ManifestSkill {
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id: string;
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name: string;
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description: string;
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category: string;
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priority: string;
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file: string;
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tokenEstimate: number;
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tags: string[];
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dependencies: string[];
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agents: string[];
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}
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interface SkillsManifest {
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version: string;
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totalSkills: number;
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skills: { [key: string]: ManifestSkill };
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}
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interface OptimizationReport {
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totalSkills: number;
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optimized: number;
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inProgress: number;
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baseline: number;
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draft: number;
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tokensSaved: number;
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avgReduction: number;
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skills: ValidationResult[];
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}
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// ============================================================================
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// Constants
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// ============================================================================
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const SKILLS_DIR = path.join(process.cwd(), '.claude', 'skills');
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const MANIFEST_PATH = path.join(SKILLS_DIR, 'skills-manifest.json');
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const TARGET_REDUCTION = 0.45; // 45% reduction target (40-50% range midpoint)
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const MAX_TOKENS = 1500; // Maximum tokens for optimized skills
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const REQUIRED_SECTIONS = [
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'default_to_action',
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'quick_reference_card',
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'agent_coordination',
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];
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// Token estimation (rough approximation: 1 token ≈ 0.75 words)
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const WORDS_PER_TOKEN = 0.75;
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// ============================================================================
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// Utility Functions
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// ============================================================================
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/**
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* Read and parse YAML frontmatter from markdown file
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*/
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function parseFrontmatter(content: string): { metadata: SkillMetadata | null; content: string } {
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const frontmatterRegex = /^---\n([\s\S]+?)\n---\n([\s\S]*)$/;
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const match = content.match(frontmatterRegex);
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if (!match) {
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return { metadata: null, content };
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}
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try {
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const metadata = yaml.load(match[1]) as SkillMetadata;
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const mainContent = match[2];
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return { metadata, content: mainContent };
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} catch (error) {
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console.error('Failed to parse YAML frontmatter:', error);
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return { metadata: null, content };
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}
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}
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/**
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* Estimate token count from word count
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*/
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function estimateTokens(content: string): number {
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const wordCount = content.split(/\s+/).length;
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return Math.round(wordCount * WORDS_PER_TOKEN);
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}
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/**
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* Count code blocks in content
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*/
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function countCodeBlocks(content: string): number {
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const codeBlockRegex = /```[\s\S]*?```/g;
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const matches = content.match(codeBlockRegex);
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return matches ? matches.length : 0;
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}
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/**
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* Check if section exists in content
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*/
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function hasSectionMarker(content: string, marker: string): boolean {
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const patterns = {
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default_to_action: /<default_to_action>/i,
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quick_reference_card: /##\s*Quick Reference Card/i,
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agent_coordination: /##\s*Agent Coordination/i,
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core_content: /##\s*(Core Concepts|Implementation|Patterns)/i,
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examples: /##\s*Examples/i,
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related_skills: /##\s*Related Skills/i,
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};
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const pattern = patterns[marker as keyof typeof patterns];
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return pattern ? pattern.test(content) : false;
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}
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/**
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* Extract sections from markdown content
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*/
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function extractSections(content: string): { [key: string]: string } {
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const sections: { [key: string]: string } = {};
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const headerRegex = /^##\s+(.+)$/gm;
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const matches = [...content.matchAll(headerRegex)];
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for (let i = 0; i < matches.length; i++) {
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const sectionName = matches[i][1].toLowerCase().replace(/\s+/g, '_');
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const startIndex = matches[i].index!;
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const endIndex = i < matches.length - 1 ? matches[i + 1].index! : content.length;
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sections[sectionName] = content.substring(startIndex, endIndex);
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}
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return sections;
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}
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/**
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* Load skills manifest
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*/
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function loadManifest(): SkillsManifest | null {
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try {
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const manifestContent = fs.readFileSync(MANIFEST_PATH, 'utf-8');
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return JSON.parse(manifestContent) as SkillsManifest;
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} catch (error) {
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console.error('Failed to load skills manifest:', error);
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return null;
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}
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}
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/**
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* Find all skill files
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*/
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function findSkillFiles(): string[] {
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const skillFiles: string[] = [];
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function searchDir(dir: string) {
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const entries = fs.readdirSync(dir, { withFileTypes: true });
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for (const entry of entries) {
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const fullPath = path.join(dir, entry.name);
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if (entry.isDirectory()) {
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searchDir(fullPath);
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} else if (entry.isFile() && entry.name === 'SKILL.md') {
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skillFiles.push(fullPath);
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}
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}
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}
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searchDir(SKILLS_DIR);
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return skillFiles;
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}
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// ============================================================================
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// Validation Functions
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// ============================================================================
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/**
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* Validate YAML frontmatter
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*/
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function validateFrontmatter(
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metadata: SkillMetadata | null,
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manifestSkill: ManifestSkill | null
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): ValidationError[] {
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const errors: ValidationError[] = [];
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if (!metadata) {
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errors.push({
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type: 'missing_frontmatter',
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message: 'YAML frontmatter is missing',
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severity: 'critical',
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fixable: true,
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});
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return errors;
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}
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// Required fields
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const requiredFields: (keyof SkillMetadata)[] = [
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'name',
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'description',
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'category',
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'priority',
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'tokenEstimate',
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'agents',
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'implementation_status',
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];
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for (const field of requiredFields) {
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if (!metadata[field]) {
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errors.push({
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type: 'missing_field',
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message: `Required field '${field}' is missing from frontmatter`,
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severity: 'high',
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fixable: false,
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});
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}
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}
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// Validate token estimate
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if (metadata.tokenEstimate && metadata.tokenEstimate > MAX_TOKENS) {
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errors.push({
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type: 'token_limit_exceeded',
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message: `Token estimate ${metadata.tokenEstimate} exceeds maximum ${MAX_TOKENS}`,
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severity: 'medium',
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fixable: false,
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});
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}
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// Validate agents against manifest
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if (manifestSkill && metadata.agents) {
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const manifestAgents = manifestSkill.agents;
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const missingAgents = manifestAgents.filter((a) => !metadata.agents.includes(a));
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const extraAgents = metadata.agents.filter((a) => !manifestAgents.includes(a));
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if (missingAgents.length > 0) {
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errors.push({
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type: 'agent_mismatch',
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message: `Missing agents from manifest: ${missingAgents.join(', ')}`,
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severity: 'medium',
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fixable: true,
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});
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}
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if (extraAgents.length > 0) {
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errors.push({
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type: 'agent_mismatch',
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message: `Extra agents not in manifest: ${extraAgents.join(', ')}`,
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severity: 'medium',
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fixable: true,
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});
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}
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}
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return errors;
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}
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/**
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* Validate required sections
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*/
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function validateSections(content: string, metadata: SkillMetadata | null): ValidationError[] {
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const errors: ValidationError[] = [];
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// Check for <default_to_action>
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if (!hasSectionMarker(content, 'default_to_action')) {
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errors.push({
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type: 'missing_section',
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message: '<default_to_action> block is missing',
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severity: 'high',
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fixable: false,
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});
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}
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// Check for Quick Reference Card
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if (!hasSectionMarker(content, 'quick_reference_card')) {
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errors.push({
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type: 'missing_section',
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message: 'Quick Reference Card section is missing',
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severity: 'high',
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fixable: false,
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});
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}
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// Check for Agent Coordination
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if (!hasSectionMarker(content, 'agent_coordination')) {
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errors.push({
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type: 'missing_section',
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message: 'Agent Coordination section is missing',
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severity: 'high',
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fixable: false,
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});
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}
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// Check quick_reference_card metadata flag
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if (
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metadata &&
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metadata.implementation_status === 'optimized' &&
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!metadata.quick_reference_card
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) {
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errors.push({
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type: 'metadata_mismatch',
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message: 'quick_reference_card should be true for optimized skills',
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severity: 'medium',
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fixable: true,
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});
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}
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return errors;
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}
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/**
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* Validate optimization status
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*/
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function validateOptimization(
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metadata: SkillMetadata | null,
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estimatedTokens: number
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): ValidationError[] {
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const errors: ValidationError[] = [];
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if (!metadata) {
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return errors;
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}
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// Check if optimization metadata is present for optimized skills
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if (metadata.implementation_status === 'optimized') {
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if (!metadata.optimization_version) {
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errors.push({
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type: 'missing_optimization_metadata',
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message: 'optimization_version is required for optimized skills',
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severity: 'medium',
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fixable: true,
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});
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}
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if (!metadata.last_optimized) {
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errors.push({
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type: 'missing_optimization_metadata',
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message: 'last_optimized date is required for optimized skills',
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severity: 'medium',
|
|
fixable: true,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Check token estimate accuracy
|
|
const tokenDifference = Math.abs(estimatedTokens - metadata.tokenEstimate);
|
|
const tokenDifferencePercent = tokenDifference / metadata.tokenEstimate;
|
|
|
|
if (tokenDifferencePercent > 0.2) {
|
|
// More than 20% difference
|
|
errors.push({
|
|
type: 'token_estimate_inaccurate',
|
|
message: `Token estimate ${metadata.tokenEstimate} differs significantly from actual ${estimatedTokens}`,
|
|
severity: 'medium',
|
|
fixable: true,
|
|
});
|
|
}
|
|
|
|
return errors;
|
|
}
|
|
|
|
/**
|
|
* Generate warnings for optimization opportunities
|
|
*/
|
|
function generateWarnings(
|
|
metadata: SkillMetadata | null,
|
|
content: string,
|
|
estimatedTokens: number
|
|
): ValidationWarning[] {
|
|
const warnings: ValidationWarning[] = [];
|
|
|
|
if (!metadata) {
|
|
return warnings;
|
|
}
|
|
|
|
// Warn about non-optimized skills
|
|
if (metadata.implementation_status === 'baseline') {
|
|
warnings.push({
|
|
type: 'optimization_opportunity',
|
|
message: 'Skill has not been optimized yet',
|
|
recommendation: 'Apply Claude 4.5 optimization patterns to reduce token count by 40-50%',
|
|
});
|
|
}
|
|
|
|
// Warn about high token count
|
|
if (estimatedTokens > MAX_TOKENS) {
|
|
warnings.push({
|
|
type: 'high_token_count',
|
|
message: `Estimated tokens ${estimatedTokens} exceeds recommended maximum ${MAX_TOKENS}`,
|
|
recommendation: 'Consider compressing verbose sections, using tables, and consolidating examples',
|
|
});
|
|
}
|
|
|
|
// Warn about missing code examples
|
|
const codeBlockCount = countCodeBlocks(content);
|
|
if (codeBlockCount === 0) {
|
|
warnings.push({
|
|
type: 'missing_examples',
|
|
message: 'No code examples found',
|
|
recommendation: 'Add minimal, focused code examples to demonstrate key concepts',
|
|
});
|
|
}
|
|
|
|
// Warn about potential verbosity
|
|
const avgWordsPerSection = content.split(/^##/m).length;
|
|
if (avgWordsPerSection > 500) {
|
|
warnings.push({
|
|
type: 'verbose_content',
|
|
message: 'Sections appear verbose',
|
|
recommendation: 'Use bullet points, tables, and concise language to reduce verbosity',
|
|
});
|
|
}
|
|
|
|
return warnings;
|
|
}
|
|
|
|
/**
|
|
* Validate a single skill file
|
|
*/
|
|
function validateSkill(skillPath: string, manifest: SkillsManifest | null): ValidationResult {
|
|
const skillName = path.basename(path.dirname(skillPath));
|
|
const content = fs.readFileSync(skillPath, 'utf-8');
|
|
const { metadata, content: mainContent } = parseFrontmatter(content);
|
|
|
|
// Find corresponding manifest entry
|
|
const manifestSkill = manifest ? manifest.skills[skillName] || null : null;
|
|
|
|
// Calculate metrics
|
|
const estimatedTokens = estimateTokens(mainContent);
|
|
const wordCount = mainContent.split(/\s+/).length;
|
|
const codeBlockCount = countCodeBlocks(mainContent);
|
|
|
|
const metrics: SkillMetrics = {
|
|
estimatedTokens,
|
|
actualWordCount: wordCount,
|
|
sectionCount: (mainContent.match(/^##\s/gm) || []).length,
|
|
codeBlockCount,
|
|
hasQuickReference: hasSectionMarker(mainContent, 'quick_reference_card'),
|
|
hasAgentCoordination: hasSectionMarker(mainContent, 'agent_coordination'),
|
|
hasDefaultToAction: hasSectionMarker(mainContent, 'default_to_action'),
|
|
};
|
|
|
|
const sections: SectionChecks = {
|
|
yamlFrontmatter: metadata !== null,
|
|
defaultToAction: metrics.hasDefaultToAction,
|
|
quickReferenceCard: metrics.hasQuickReference,
|
|
agentCoordination: metrics.hasAgentCoordination,
|
|
coreContent: hasSectionMarker(mainContent, 'core_content'),
|
|
examples: hasSectionMarker(mainContent, 'examples'),
|
|
relatedSkills: hasSectionMarker(mainContent, 'related_skills'),
|
|
};
|
|
|
|
// Run validations
|
|
const errors: ValidationError[] = [
|
|
...validateFrontmatter(metadata, manifestSkill),
|
|
...validateSections(mainContent, metadata),
|
|
...validateOptimization(metadata, estimatedTokens),
|
|
];
|
|
|
|
const warnings = generateWarnings(metadata, mainContent, estimatedTokens);
|
|
|
|
// Calculate optimization metrics
|
|
const optimization: OptimizationMetrics = {
|
|
status: metadata?.implementation_status || 'unknown',
|
|
currentTokens: estimatedTokens,
|
|
targetMet: estimatedTokens <= MAX_TOKENS,
|
|
lastOptimized: metadata?.last_optimized,
|
|
};
|
|
|
|
// Calculate reduction if we have baseline data
|
|
if (manifestSkill && manifestSkill.tokenEstimate) {
|
|
optimization.baselineTokens = manifestSkill.tokenEstimate;
|
|
optimization.reductionPercent =
|
|
((manifestSkill.tokenEstimate - estimatedTokens) / manifestSkill.tokenEstimate) * 100;
|
|
}
|
|
|
|
const passed = errors.filter((e) => e.severity === 'critical' || e.severity === 'high').length === 0;
|
|
|
|
return {
|
|
skillPath,
|
|
skillName,
|
|
passed,
|
|
errors,
|
|
warnings,
|
|
metrics,
|
|
sections,
|
|
optimization,
|
|
};
|
|
}
|
|
|
|
// ============================================================================
|
|
// Reporting Functions
|
|
// ============================================================================
|
|
|
|
/**
|
|
* Print validation result for single skill
|
|
*/
|
|
function printValidationResult(result: ValidationResult, verbose: boolean = false) {
|
|
const status = result.passed ? '✅ PASSED' : '❌ FAILED';
|
|
const statusColor = result.passed ? '\x1b[32m' : '\x1b[31m';
|
|
const resetColor = '\x1b[0m';
|
|
|
|
console.log(`\n${statusColor}${status}${resetColor} ${result.skillName}`);
|
|
console.log(` Path: ${result.skillPath}`);
|
|
console.log(` Status: ${result.optimization.status}`);
|
|
console.log(` Tokens: ${result.metrics.estimatedTokens} (Target: ≤${MAX_TOKENS})`);
|
|
|
|
if (result.optimization.baselineTokens) {
|
|
const reduction = result.optimization.reductionPercent?.toFixed(1) || '0';
|
|
console.log(` Reduction: ${reduction}% from baseline (${result.optimization.baselineTokens} tokens)`);
|
|
}
|
|
|
|
// Print errors
|
|
if (result.errors.length > 0) {
|
|
console.log(`\n Errors (${result.errors.length}):`);
|
|
for (const error of result.errors) {
|
|
const severityColor = error.severity === 'critical' ? '\x1b[31m' : '\x1b[33m';
|
|
const fixable = error.fixable ? '[FIXABLE]' : '';
|
|
console.log(` ${severityColor}[${error.severity.toUpperCase()}]${resetColor} ${error.message} ${fixable}`);
|
|
}
|
|
}
|
|
|
|
// Print warnings
|
|
if (result.warnings.length > 0 && verbose) {
|
|
console.log(`\n Warnings (${result.warnings.length}):`);
|
|
for (const warning of result.warnings) {
|
|
console.log(` ⚠️ ${warning.message}`);
|
|
console.log(` → ${warning.recommendation}`);
|
|
}
|
|
}
|
|
|
|
// Print section checks
|
|
if (verbose) {
|
|
console.log(`\n Sections:`);
|
|
console.log(` YAML Frontmatter: ${result.sections.yamlFrontmatter ? '✅' : '❌'}`);
|
|
console.log(` <default_to_action>: ${result.sections.defaultToAction ? '✅' : '❌'}`);
|
|
console.log(` Quick Reference Card: ${result.sections.quickReferenceCard ? '✅' : '❌'}`);
|
|
console.log(` Agent Coordination: ${result.sections.agentCoordination ? '✅' : '❌'}`);
|
|
console.log(` Core Content: ${result.sections.coreContent ? '✅' : '❌'}`);
|
|
console.log(` Examples: ${result.sections.examples ? '✅' : '❌'}`);
|
|
console.log(` Related Skills: ${result.sections.relatedSkills ? '✅' : '❌'}`);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Generate optimization report for all skills
|
|
*/
|
|
function generateOptimizationReport(results: ValidationResult[]): OptimizationReport {
|
|
const statusCounts = {
|
|
optimized: 0,
|
|
inProgress: 0,
|
|
baseline: 0,
|
|
draft: 0,
|
|
};
|
|
|
|
let totalBaselineTokens = 0;
|
|
let totalCurrentTokens = 0;
|
|
|
|
for (const result of results) {
|
|
const status = result.optimization.status;
|
|
|
|
if (status === 'optimized' || status === 'production') {
|
|
statusCounts.optimized++;
|
|
} else if (status === 'baseline') {
|
|
statusCounts.baseline++;
|
|
} else if (status === 'draft') {
|
|
statusCounts.draft++;
|
|
}
|
|
|
|
if (result.optimization.baselineTokens) {
|
|
totalBaselineTokens += result.optimization.baselineTokens;
|
|
}
|
|
totalCurrentTokens += result.optimization.currentTokens;
|
|
}
|
|
|
|
const tokensSaved = totalBaselineTokens - totalCurrentTokens;
|
|
const avgReduction = totalBaselineTokens > 0 ? (tokensSaved / totalBaselineTokens) * 100 : 0;
|
|
|
|
return {
|
|
totalSkills: results.length,
|
|
optimized: statusCounts.optimized,
|
|
inProgress: statusCounts.inProgress,
|
|
baseline: statusCounts.baseline,
|
|
draft: statusCounts.draft,
|
|
tokensSaved,
|
|
avgReduction,
|
|
skills: results,
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Print optimization report
|
|
*/
|
|
function printOptimizationReport(report: OptimizationReport) {
|
|
console.log('\n' + '='.repeat(80));
|
|
console.log('SKILL OPTIMIZATION REPORT');
|
|
console.log('='.repeat(80));
|
|
|
|
console.log(`\nTotal Skills: ${report.totalSkills}`);
|
|
console.log(` ✅ Optimized: ${report.optimized}`);
|
|
console.log(` 📝 Baseline: ${report.baseline}`);
|
|
console.log(` ⚠️ Draft: ${report.draft}`);
|
|
|
|
const optimizedPercent = ((report.optimized / report.totalSkills) * 100).toFixed(1);
|
|
console.log(`\nOptimization Progress: ${optimizedPercent}%`);
|
|
|
|
if (report.tokensSaved > 0) {
|
|
console.log(`\nToken Savings: ${report.tokensSaved.toLocaleString()} tokens saved`);
|
|
console.log(`Average Reduction: ${report.avgReduction.toFixed(1)}%`);
|
|
|
|
const targetMet = report.avgReduction >= TARGET_REDUCTION * 100;
|
|
const targetStatus = targetMet ? '✅ TARGET MET' : '⚠️ BELOW TARGET';
|
|
console.log(`Target (${(TARGET_REDUCTION * 100).toFixed(0)}%): ${targetStatus}`);
|
|
}
|
|
|
|
// Top performers
|
|
const sortedByReduction = report.skills
|
|
.filter((s) => s.optimization.reductionPercent !== undefined)
|
|
.sort((a, b) => (b.optimization.reductionPercent || 0) - (a.optimization.reductionPercent || 0));
|
|
|
|
if (sortedByReduction.length > 0) {
|
|
console.log('\nTop Optimizations:');
|
|
for (let i = 0; i < Math.min(5, sortedByReduction.length); i++) {
|
|
const skill = sortedByReduction[i];
|
|
const reduction = skill.optimization.reductionPercent?.toFixed(1) || '0';
|
|
console.log(` ${i + 1}. ${skill.skillName}: ${reduction}% reduction`);
|
|
}
|
|
}
|
|
|
|
// Skills needing attention
|
|
const needsOptimization = report.skills.filter(
|
|
(s) => s.optimization.status === 'baseline' || s.optimization.status === 'draft'
|
|
);
|
|
|
|
if (needsOptimization.length > 0) {
|
|
console.log(`\nSkills Needing Optimization (${needsOptimization.length}):`);
|
|
for (const skill of needsOptimization.slice(0, 10)) {
|
|
console.log(` • ${skill.skillName} (${skill.optimization.currentTokens} tokens)`);
|
|
}
|
|
if (needsOptimization.length > 10) {
|
|
console.log(` ... and ${needsOptimization.length - 10} more`);
|
|
}
|
|
}
|
|
|
|
// Failed validations
|
|
const failed = report.skills.filter((s) => !s.passed);
|
|
if (failed.length > 0) {
|
|
console.log(`\n❌ Failed Validations (${failed.length}):`);
|
|
for (const skill of failed) {
|
|
const criticalErrors = skill.errors.filter(
|
|
(e) => e.severity === 'critical' || e.severity === 'high'
|
|
).length;
|
|
console.log(` • ${skill.skillName}: ${criticalErrors} critical/high issues`);
|
|
}
|
|
}
|
|
|
|
console.log('\n' + '='.repeat(80));
|
|
}
|
|
|
|
// ============================================================================
|
|
// Main Execution
|
|
// ============================================================================
|
|
|
|
async function main() {
|
|
const args = process.argv.slice(2);
|
|
|
|
// Parse flags
|
|
const flags = {
|
|
report: args.includes('--report'),
|
|
verbose: args.includes('--verbose') || args.includes('-v'),
|
|
fix: args.includes('--fix'),
|
|
};
|
|
|
|
// Get skill path(s)
|
|
const skillPaths = args.filter((arg) => !arg.startsWith('--') && !arg.startsWith('-'));
|
|
|
|
// Load manifest
|
|
const manifest = loadManifest();
|
|
if (!manifest) {
|
|
console.error('Failed to load skills manifest. Exiting.');
|
|
process.exit(1);
|
|
}
|
|
|
|
let results: ValidationResult[];
|
|
|
|
if (skillPaths.length === 0) {
|
|
// Validate all skills
|
|
console.log('Validating all skills...\n');
|
|
const allSkillFiles = findSkillFiles();
|
|
results = allSkillFiles.map((skillPath) => validateSkill(skillPath, manifest));
|
|
} else {
|
|
// Validate specific skills
|
|
results = skillPaths.map((skillPath) => {
|
|
const fullPath = path.isAbsolute(skillPath) ? skillPath : path.join(process.cwd(), skillPath);
|
|
return validateSkill(fullPath, manifest);
|
|
});
|
|
}
|
|
|
|
// Print results
|
|
if (flags.report) {
|
|
const report = generateOptimizationReport(results);
|
|
printOptimizationReport(report);
|
|
} else {
|
|
for (const result of results) {
|
|
printValidationResult(result, flags.verbose);
|
|
}
|
|
|
|
// Summary
|
|
const passed = results.filter((r) => r.passed).length;
|
|
const failed = results.length - passed;
|
|
|
|
console.log('\n' + '='.repeat(80));
|
|
console.log(`Summary: ${passed}/${results.length} passed, ${failed} failed`);
|
|
console.log('='.repeat(80));
|
|
}
|
|
|
|
// Exit with error code if any validations failed
|
|
const hasFailures = results.some((r) => !r.passed);
|
|
process.exit(hasFailures ? 1 : 0);
|
|
}
|
|
|
|
// Run if executed directly
|
|
if (require.main === module) {
|
|
main().catch((error) => {
|
|
console.error('Fatal error:', error);
|
|
process.exit(1);
|
|
});
|
|
}
|
|
|
|
export {
|
|
validateSkill,
|
|
generateOptimizationReport,
|
|
loadManifest,
|
|
findSkillFiles,
|
|
type ValidationResult,
|
|
type OptimizationReport,
|
|
type SkillMetadata,
|
|
};
|