claude-enhancer.js
1 #!/usr/bin/env node 2 3 /** 4 * Claude AI Content Enhancement Engine 5 * 6 * Advanced AI-powered CV content enhancement using Claude API with intelligent 7 * prompt engineering, content optimization, and professional development insights. 8 * 9 * Features: 10 * - Intelligent content analysis and enhancement 11 * - Professional tone optimization 12 * - Skills gap analysis and recommendations 13 * - Industry trend integration 14 * - Token usage optimization with caching 15 * - Multi-stage enhancement pipeline 16 * - Meta-commentary artifact removal (Issue #100 fix) 17 * - XML-structured output formatting 18 * - Robust content filtering and cleaning 19 * 20 * Meta-Commentary Fix (Issue #100): 21 * This version includes comprehensive fixes to prevent Claude AI from generating 22 * explanatory text and meta-commentary in CV content. Implementation includes: 23 * - System prompts explicitly forbidding meta-commentary 24 * - XML-structured output format specifications 25 * - Multi-layer content cleaning and artifact removal 26 * - Enhanced JSON parsing with fallback content extraction 27 * - Comprehensive test suite for content validation 28 * 29 * Usage: node claude-enhancer.js 30 * node claude-enhancer.js --test-cleaning # Test content cleaning functions 31 * 32 * Environment Variables: 33 * - ANTHROPIC_API_KEY: Claude API key for authentication 34 * - AI_BUDGET: Token budget for enhancement session 35 * - CREATIVITY_LEVEL: Enhancement creativity (conservative|balanced|creative|innovative) 36 * - ACTIVITY_SCORE: Current GitHub activity score for context 37 */ 38 39 const fs = require('fs').promises; 40 const path = require('path'); 41 const crypto = require('crypto'); 42 const https = require('https'); 43 const { execFile: _execFile } = require('child_process'); 44 const { sleep } = require('./utils/apiClient'); 45 const { XMLFewShotIntegrator } = require('./enhancer-modules/xml-few-shot-integrator'); 46 47 // Configuration 48 const CONFIG = { 49 ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY, 50 USE_CLAUDE_CODE: process.env.USE_CLAUDE_CODE !== 'false', 51 CLAUDE_CODE_MODEL: process.env.CLAUDE_CODE_MODEL || 'sonnet', 52 AI_BUDGET: process.env.AI_BUDGET || 'sufficient', 53 CREATIVITY_LEVEL: process.env.CREATIVITY_LEVEL || 'balanced', 54 ACTIVITY_SCORE: parseFloat(process.env.ACTIVITY_SCORE) || 50, 55 API_VERSION: '2023-06-01', 56 MODEL: process.env.CLAUDE_MODEL || 'claude-sonnet-4-20250514', 57 OUTPUT_DIR: 'data', 58 CACHE_DIR: 'data/ai-cache', 59 MAX_TOKENS: 4000, 60 TEMPERATURE_MAP: { 61 'conservative': 0.3, 62 'balanced': 0.5, 63 'creative': 0.7, 64 'innovative': 0.9 65 } 66 }; 67 68 /** 69 * Claude API client with intelligent caching and token optimization 70 */ 71 class ClaudeApiClient { 72 constructor(apiKey) { 73 this.apiKey = apiKey; 74 this.baseUrl = 'https://api.anthropic.com/v1/messages'; 75 this.tokenUsage = { 76 input_tokens: 0, 77 output_tokens: 0, 78 cache_creation_tokens: 0, 79 cache_read_tokens: 0 80 }; 81 this.requestCount = 0; 82 this.cacheHits = 0; 83 } 84 85 /** 86 * Make Claude API request with caching and token tracking 87 */ 88 async makeRequest(messages, options = {}, sourceContent = '') { 89 const temperature = CONFIG.TEMPERATURE_MAP[CONFIG.CREATIVITY_LEVEL] || 0.5; 90 const maxTokens = options.maxTokens || CONFIG.MAX_TOKENS; 91 92 // Generate cache key for identical requests 93 const cacheKey = this.generateCacheKey({ messages, temperature, maxTokens }, sourceContent); 94 const cachedResponse = await this.getCachedResponse(cacheKey); 95 96 if (cachedResponse && !options.skipCache) { 97 console.log('📦 Cache hit for Claude request'); 98 this.cacheHits++; 99 return cachedResponse; 100 } 101 102 // Route to Claude Code CLI or direct API 103 const useClaudeCode = CONFIG.USE_CLAUDE_CODE && !CONFIG.ANTHROPIC_API_KEY; 104 const responseData = useClaudeCode 105 ? await this._makeClaudeCodeRequest(messages, maxTokens) 106 : await this._makeDirectApiRequest(messages, temperature, maxTokens); 107 108 // Track token usage 109 if (responseData.usage) { 110 this.tokenUsage.input_tokens += responseData.usage.input_tokens || 0; 111 this.tokenUsage.output_tokens += responseData.usage.output_tokens || 0; 112 this.tokenUsage.cache_creation_tokens += responseData.usage.cache_creation_input_tokens || 0; 113 this.tokenUsage.cache_read_tokens += responseData.usage.cache_read_input_tokens || 0; 114 } 115 116 // Cache successful responses 117 await this.cacheResponse(cacheKey, responseData); 118 119 return responseData; 120 } 121 122 /** 123 * Make request via Claude Code CLI (uses Max subscription billing) 124 */ 125 async _makeClaudeCodeRequest(messages, _maxTokens) { 126 // Separate system messages from user/assistant messages 127 const systemParts = messages.filter(m => m.role === 'system').map(m => m.content); 128 const userParts = messages.filter(m => m.role !== 'system').map(m => m.content); 129 const prompt = userParts.join('\n\n'); 130 131 try { 132 console.log(`🤖 Making Claude Code CLI request (model: ${CONFIG.CLAUDE_CODE_MODEL})...`); 133 this.requestCount++; 134 135 const result = await new Promise(async (resolve, reject) => { 136 const { spawn } = require('child_process'); 137 138 const args = [ 139 '-p', prompt, 140 '--output-format', 'json', 141 '--model', CONFIG.CLAUDE_CODE_MODEL, 142 '--max-turns', '1', 143 '--no-chrome' 144 ]; 145 146 if (systemParts.length > 0) { 147 args.push('--append-system-prompt', systemParts.join('\n\n')); 148 } 149 150 // Clear CLAUDECODE env var to allow spawning from within a Claude Code session 151 const env = { ...process.env }; 152 delete env.CLAUDECODE; 153 154 let stdout = ''; 155 let stderr = ''; 156 // spawn passes args directly to execvp — no shell, no arg length issues 157 const child = spawn('claude', args, { env }); 158 159 child.stdout.on('data', (data) => { stdout += data; }); 160 child.stderr.on('data', (data) => { stderr += data; }); 161 162 child.on('close', (code) => { 163 if (code !== 0 && !stdout) { 164 reject(new Error(`Claude Code CLI exited with code ${code}${stderr ? ` — ${stderr.slice(0, 200)}` : ''}`)); 165 return; 166 } 167 // Claude Code may exit non-zero for budget limits but still return valid output 168 resolve(stdout); 169 }); 170 171 child.on('error', (error) => { 172 reject(new Error(`Claude Code CLI spawn error: ${error.message}`)); 173 }); 174 }); 175 176 // Claude Code --output-format json returns a JSON array: 177 // [0] {type:"system"} init metadata 178 // [1] {type:"assistant", message: {content: [{text}], usage}} the response 179 // [2] {type:"result"} session summary 180 const parsed = JSON.parse(result); 181 const assistantMsg = Array.isArray(parsed) 182 ? parsed.find(item => item.type === 'assistant') 183 : null; 184 185 if (!assistantMsg?.message?.content?.[0]?.text) { 186 throw new Error('Could not extract assistant response from Claude Code output'); 187 } 188 189 const message = assistantMsg.message; 190 191 // Return in the same shape as the Anthropic API response 192 return { 193 content: message.content, 194 usage: { 195 input_tokens: message.usage?.input_tokens || 0, 196 output_tokens: message.usage?.output_tokens || 0, 197 cache_creation_input_tokens: message.usage?.cache_creation_input_tokens || 0, 198 cache_read_input_tokens: message.usage?.cache_read_input_tokens || 0 199 } 200 }; 201 202 } catch (error) { 203 console.error('❌ Claude Code CLI request failed:', error.message); 204 throw error; 205 } 206 } 207 208 /** 209 * Make request via direct Anthropic API (uses API credits) 210 */ 211 async _makeDirectApiRequest(messages, temperature, maxTokens) { 212 const requestBody = { 213 model: CONFIG.MODEL, 214 max_tokens: maxTokens, 215 temperature, 216 messages: messages.map(msg => ({ 217 role: msg.role, 218 content: msg.content 219 })) 220 }; 221 222 try { 223 console.log('🤖 Making Claude API request...'); 224 this.requestCount++; 225 226 const response = await this.httpRequest(this.baseUrl, { 227 method: 'POST', 228 headers: { 229 'Content-Type': 'application/json', 230 'x-api-key': this.apiKey, 231 'anthropic-version': CONFIG.API_VERSION 232 }, 233 body: JSON.stringify(requestBody) 234 }); 235 236 const responseData = JSON.parse(response.body); 237 238 if (responseData.error) { 239 throw new Error(`Claude API Error: ${responseData.error.message}`); 240 } 241 242 return responseData; 243 244 } catch (error) { 245 console.error('❌ Claude API request failed:', error.message); 246 throw error; 247 } 248 } 249 250 /** 251 * HTTP request wrapper 252 */ 253 async httpRequest(url, options, maxRetries = 3, retryDelay = 1000) { 254 for (let i = 0; i < maxRetries; i++) { 255 try { 256 return await new Promise((resolve, reject) => { 257 const req = https.request(url, options, (res) => { 258 let body = ''; 259 res.on('data', chunk => body += chunk); 260 res.on('end', () => { 261 if (res.statusCode >= 200 && res.statusCode < 300) { 262 resolve({ body, statusCode: res.statusCode }); 263 } else if (res.statusCode >= 500 || res.statusCode === 429) { // Retry on 5xx or Too Many Requests 264 reject(new Error(`HTTP ${res.statusCode}: ${body}`)); 265 } else { 266 reject(new Error(`HTTP ${res.statusCode}: ${body}`)); 267 } 268 }); 269 }); 270 271 req.on('error', reject); 272 req.setTimeout(60000, () => { 273 req.destroy(); 274 reject(new Error('Request timeout')); 275 }); 276 277 if (options.body) { 278 req.write(options.body); 279 } 280 req.end(); 281 }); 282 } catch (error) { 283 if (i < maxRetries - 1) { 284 const delay = retryDelay * Math.pow(2, i); 285 console.warn(`Retrying ${url} in ${delay}ms due to error: ${error.message}`); 286 await sleep(delay); 287 } else { 288 throw error; // Last retry failed 289 } 290 } 291 } 292 } 293 294 /** 295 * Generate a content-aware cache key for request deduplication. 296 * @param {object} requestPayload - The core request object (messages, temperature, maxTokens). 297 * @param {string} [sourceContent=''] - The raw source content being enhanced (e.g., the original summary text). 298 * @returns {string} A SHA256 hash representing the unique request. 299 */ 300 generateCacheKey(requestPayload, sourceContent = '') { 301 // Combine the request structure with a hash of the actual content being processed. 302 const contentHash = crypto.createHash('sha256').update(sourceContent).digest('hex'); 303 const payloadString = JSON.stringify(requestPayload); 304 305 // The final key depends on both the prompt and the data. 306 return crypto.createHash('sha256').update(payloadString + contentHash).digest('hex').substring(0, 16); 307 } 308 309 /** 310 * Retrieve cached response 311 */ 312 async getCachedResponse(cacheKey) { 313 try { 314 const cachePath = path.join(CONFIG.CACHE_DIR, `${cacheKey}.json`); 315 const cached = await fs.readFile(cachePath, 'utf8'); 316 const cachedData = JSON.parse(cached); 317 318 // Check if cache is still valid (24 hours) 319 if (Date.now() - cachedData.timestamp < 24 * 60 * 60 * 1000) { 320 return cachedData.response; 321 } 322 } catch (error) { 323 // Cache miss or error - continue with API request 324 } 325 return null; 326 } 327 328 /** 329 * Cache API response 330 */ 331 async cacheResponse(cacheKey, response) { 332 try { 333 await this.ensureCacheDir(); 334 const cachePath = path.join(CONFIG.CACHE_DIR, `${cacheKey}.json`); 335 const cacheData = { 336 timestamp: Date.now(), 337 response 338 }; 339 await fs.writeFile(cachePath, JSON.stringify(cacheData), 'utf8'); 340 } catch (error) { 341 console.warn('⚠️ Failed to cache response:', error.message); 342 } 343 } 344 345 /** 346 * Ensure cache directory exists 347 */ 348 async ensureCacheDir() { 349 try { 350 await fs.access(CONFIG.CACHE_DIR); 351 } catch { 352 await fs.mkdir(CONFIG.CACHE_DIR, { recursive: true }); 353 } 354 } 355 356 /** 357 * Get token usage statistics 358 */ 359 getUsageStats() { 360 const totalTokens = this.tokenUsage.input_tokens + this.tokenUsage.output_tokens; 361 const cacheEfficiency = this.requestCount > 0 ? (this.cacheHits / this.requestCount) * 100 : 0; 362 363 return { 364 ...this.tokenUsage, 365 total_tokens: totalTokens, 366 request_count: this.requestCount, 367 cache_hits: this.cacheHits, 368 cache_efficiency_percent: Math.round(cacheEfficiency * 10) / 10 369 }; 370 } 371 } 372 373 /** 374 * CV Content Enhancement Engine 375 * 376 * Multi-stage AI enhancement pipeline for professional CV content 377 */ 378 class CVContentEnhancer { 379 constructor() { 380 this.client = new ClaudeApiClient(CONFIG.ANTHROPIC_API_KEY); 381 this.xmlIntegrator = new XMLFewShotIntegrator(); 382 this.enhancementStartTime = Date.now(); 383 this.enhancementResults = {}; 384 this.useXMLPrompts = process.env.USE_XML_PROMPTS !== 'false'; // Default to true 385 } 386 387 /** 388 * Run comprehensive CV content enhancement 389 */ 390 async enhance() { 391 console.log('🤖 **CLAUDE AI CONTENT ENHANCEMENT INITIATED**'); 392 console.log(`🎨 Creativity level: ${CONFIG.CREATIVITY_LEVEL}`); 393 console.log(`💰 AI budget: ${CONFIG.AI_BUDGET}`); 394 console.log(`📊 Activity score: ${CONFIG.ACTIVITY_SCORE}/100`); 395 console.log(''); 396 397 try { 398 // Ensure output directory exists 399 await this.ensureOutputDir(); 400 401 // Load existing CV data and activity metrics 402 const currentCVData = await this.loadCurrentCVData(); 403 const activityMetrics = await this.loadActivityMetrics(); 404 405 // Load keyword gaps to guide enhancement 406 const keywordGaps = await this.loadKeywordGaps(); 407 if (keywordGaps.top_missing?.length) { 408 console.log(`🔑 Keyword gaps to address: ${keywordGaps.top_missing.slice(0,5).map(g=>g.kw).join(', ')} ...`); 409 } 410 411 const enhancementPlan = { 412 metadata: { 413 enhancement_timestamp: new Date().toISOString(), 414 creativity_level: CONFIG.CREATIVITY_LEVEL, 415 ai_budget: CONFIG.AI_BUDGET, 416 activity_score: CONFIG.ACTIVITY_SCORE, 417 enhancer_version: '2.1.0' 418 } 419 }; 420 421 // Multi-stage enhancement pipeline 422 console.log('📝 Stage 1: Professional Summary Enhancement'); 423 enhancementPlan.professional_summary = await this.enhanceProfessionalSummary(currentCVData, activityMetrics); 424 425 console.log('⚡ Stage 2: Skills Analysis & Optimization'); 426 enhancementPlan.skills_enhancement = await this.enhanceSkillsSection(currentCVData, activityMetrics); 427 428 console.log('🎯 Stage 3: Experience Description Optimization'); 429 enhancementPlan.experience_enhancement = await this.enhanceExperience(currentCVData, activityMetrics); 430 431 console.log('🚀 Stage 4: Project Impact Analysis'); 432 enhancementPlan.project_enhancement = await this.enhanceProjects(currentCVData, activityMetrics); 433 434 if (CONFIG.AI_BUDGET !== 'insufficient' && (CONFIG.CREATIVITY_LEVEL === 'creative' || CONFIG.CREATIVITY_LEVEL === 'innovative')) { 435 console.log('🔮 Stage 5: Strategic Career Insights'); 436 enhancementPlan.strategic_insights = await this.generateStrategicInsights(currentCVData, activityMetrics); 437 } 438 439 // Generate enhancement summary 440 enhancementPlan.enhancement_summary = this.generateEnhancementSummary(enhancementPlan); 441 442 // Save enhancement results 443 await this.saveEnhancementResults(enhancementPlan); 444 445 const enhancementTime = ((Date.now() - this.enhancementStartTime) / 1000).toFixed(2); 446 const usageStats = this.client.getUsageStats(); 447 448 console.log(`✅ Enhancement completed in ${enhancementTime}s`); 449 console.log(`📊 Token usage: ${usageStats.total_tokens} total (${usageStats.cache_efficiency_percent}% cache efficiency)`); 450 console.log(`📁 Results saved to ${CONFIG.OUTPUT_DIR}/`); 451 452 return enhancementPlan; 453 454 } catch (error) { 455 console.error('❌ Enhancement failed:', error.message); 456 throw error; 457 } 458 } 459 460 /** 461 * Load narrative intelligence data if available 462 */ 463 async loadNarrativeIntelligence() { 464 try { 465 const narrativePath = path.join('data', 'narratives', 'narrative-integration.json'); 466 const content = await fs.readFile(narrativePath, 'utf8'); 467 const narrativeData = JSON.parse(content); 468 469 console.log('📖 Loaded professional narrative intelligence data'); 470 return narrativeData; 471 } catch (error) { 472 console.log('📝 No narrative intelligence data available, using standard enhancement'); 473 return null; 474 } 475 } 476 477 /** 478 * Enhance professional summary with AI optimization 479 * Enhanced with XML structuring and few-shot learning (Issues #96, #97) 480 */ 481 async enhanceProfessionalSummary(cvData, activityMetrics) { 482 // Use XML-structured prompts with few-shot examples for enhanced quality 483 if (this.useXMLPrompts) { 484 return await this.enhanceProfessionalSummaryXML(cvData, activityMetrics); 485 } 486 487 // Legacy method for backward compatibility 488 return await this.enhanceProfessionalSummaryLegacy(cvData, activityMetrics); 489 } 490 491 /** 492 * Enhanced professional summary using XML structuring and few-shot prompting 493 */ 494 async enhanceProfessionalSummaryXML(cvData, activityMetrics) { 495 console.log('🔨 Using XML-structured prompt with few-shot examples...'); 496 497 try { 498 // Initialize XML integrator 499 await this.xmlIntegrator.initialize(); 500 501 // Construct XML-structured prompt with few-shot examples 502 const promptResult = await this.xmlIntegrator.enhanceProfessionalSummaryXML( 503 cvData, 504 activityMetrics, 505 CONFIG.CREATIVITY_LEVEL 506 ); 507 508 console.log(`📊 Expected quality improvement: ${(promptResult.quality_expected * 100).toFixed(1)}%`); 509 510 // Create messages for Claude API 511 const messages = [ 512 { 513 role: 'system', 514 content: 'You are a professional CV enhancement specialist. You MUST respond ONLY with clean JSON structure. NEVER include explanatory text, process descriptions, or meta-commentary. Follow the provided examples exactly.' 515 }, 516 { 517 role: 'user', 518 content: promptResult.xmlPrompt 519 } 520 ]; 521 522 // Make API request 523 const response = await this.client.makeRequest(messages, { maxTokens: 800 }, promptResult.contextData.currentContent); 524 const responseText = response.content[0]?.text?.trim(); 525 526 // Clean and parse response 527 const cleanedResponse = this.cleanResponseText(responseText); 528 let enhancementData; 529 530 try { 531 enhancementData = JSON.parse(cleanedResponse); 532 533 // Clean enhanced content 534 if (enhancementData.enhanced) { 535 enhancementData.enhanced = this.cleanEnhancedContent(enhancementData.enhanced); 536 } else if (enhancementData.enhanced_summary) { 537 enhancementData.enhanced_summary = this.cleanEnhancedContent(enhancementData.enhanced_summary); 538 enhancementData.enhanced = enhancementData.enhanced_summary; // Normalize 539 } 540 } catch (parseError) { 541 console.warn('⚠️ JSON parsing failed, attempting content extraction'); 542 enhancementData = this.extractContentFromText(cleanedResponse); 543 } 544 545 // Validate response quality 546 const validation = await this.xmlIntegrator.validateResponse(enhancementData, 'professional-summary', promptResult.quality_expected); 547 548 console.log(`✅ Response validation: ${validation.valid ? 'PASSED' : 'FAILED'} (Score: ${(validation.score * 100).toFixed(1)}%)`); 549 if (validation.quality_improvement) { 550 console.log('🎯 Quality improvement achieved beyond expected threshold'); 551 } 552 553 return { 554 original: promptResult.contextData.currentContent, 555 enhanced: enhancementData.enhanced || enhancementData.enhanced_summary, 556 strategic_analysis: enhancementData.strategic_improvements || enhancementData.key_differentiators, 557 ats_optimization: enhancementData.ats_keywords || [], 558 confidence_score: enhancementData.confidence_score || validation.score, 559 enhancement_applied: true, 560 prompt_strategy: 'xml-few-shot', 561 xml_metadata: promptResult.metadata, 562 validation_results: validation, 563 improvement_notes: this.analyzeSummaryImprovement( 564 promptResult.contextData.currentContent, 565 enhancementData.enhanced || enhancementData.enhanced_summary 566 ), 567 quality_indicators: { 568 xml_structured: true, 569 few_shot_guided: true, 570 validation_passed: validation.valid, 571 quality_score: validation.score, 572 expected_improvement: promptResult.quality_expected 573 } 574 }; 575 576 } catch (error) { 577 console.warn('⚠️ XML professional summary enhancement failed, falling back to legacy method'); 578 return await this.enhanceProfessionalSummaryLegacy(cvData, activityMetrics); 579 } 580 } 581 582 /** 583 * Legacy professional summary enhancement method 584 */ 585 async enhanceProfessionalSummaryLegacy(cvData, activityMetrics) { 586 // Load narrative intelligence if available 587 const narrativeData = await this.loadNarrativeIntelligence(); 588 589 const currentSummary = narrativeData?.enhanced_summary || 590 cvData?.professional_summary || 591 "AI Engineer and Software Architect with expertise in autonomous systems and machine learning."; 592 593 // Dynamic persona and strategy based on creativity level 594 const creativityStrategies = { 595 'conservative': { 596 persona: 'Alexandra Chen, Senior Technical Recruiter at Microsoft, specializing in AI/ML leadership roles. You\'ve successfully placed 200+ AI engineers at Fortune 500 companies.', 597 approach: 'evidence-based optimization with proven industry language', 598 tone: 'authoritative and credible', 599 focus: 'quantifiable achievements and established expertise' 600 }, 601 'balanced': { 602 persona: 'Dr. Marcus Rodriguez, Executive Search Partner at Andreessen Horowitz, focusing on AI startup leadership. You understand both technical depth and market positioning.', 603 approach: 'strategic positioning that balances innovation with credibility', 604 tone: 'confident thought leadership', 605 focus: 'unique value proposition with market awareness' 606 }, 607 'creative': { 608 persona: 'Sarah Kim, Chief Talent Officer at OpenAI, identifying breakthrough AI talent. You recognize unconventional brilliance and emerging paradigms.', 609 approach: 'innovative narrative that positions for future opportunities', 610 tone: 'visionary and compelling', 611 focus: 'transformative potential and cutting-edge innovation' 612 }, 613 'innovative': { 614 persona: 'Dr. Elena Vasquez, Venture Partner at Sequoia focusing on AI moonshots. You spot rare talent that will define the next decade of AI.', 615 approach: 'revolutionary positioning for paradigm-shifting roles', 616 tone: 'bold and transformational', 617 focus: 'groundbreaking innovation and industry disruption' 618 } 619 }; 620 621 const strategy = creativityStrategies[CONFIG.CREATIVITY_LEVEL] || creativityStrategies['balanced']; 622 623 // Context analysis for intelligent narrative integration 624 const activityInsight = CONFIG.ACTIVITY_SCORE >= 80 ? 'exceptionally active contributor with consistent innovation' : 625 CONFIG.ACTIVITY_SCORE >= 60 ? 'highly engaged developer with strong technical output' : 626 CONFIG.ACTIVITY_SCORE >= 40 ? 'focused contributor with quality-driven development' : 627 'selective contributor with deep technical focus'; 628 629 const technicalBreadth = (activityMetrics?.top_languages?.length || 3) >= 5 ? 'remarkable technical versatility across multiple paradigms' : 630 (activityMetrics?.top_languages?.length || 3) >= 3 ? 'solid multi-language expertise with platform agility' : 631 'deep specialization with focused technical mastery'; 632 633 const professionalArchetype = CONFIG.ACTIVITY_SCORE >= 70 && (activityMetrics?.total_repos || 0) >= 20 ? 'prolific innovator' : 634 CONFIG.ACTIVITY_SCORE >= 50 && (activityMetrics?.total_repos || 0) >= 10 ? 'strategic technologist' : 635 'specialized expert'; 636 637 const messages = [ 638 { 639 role: 'system', 640 content: 'You are a professional CV enhancement specialist. You MUST respond ONLY with clean, professional content. NEVER include explanatory text like "Here\'s an enhanced...", process descriptions, or meta-commentary. Your response must be structured JSON only, containing the enhanced content and analysis without any additional explanations or formatting notes.' 641 }, 642 { 643 role: 'user', 644 content: `You are ${strategy.persona} 645 646 CANDIDATE ANALYSIS: 647 You're reviewing a ${professionalArchetype} with ${CONFIG.ACTIVITY_SCORE}/100 GitHub activity score, demonstrating ${activityInsight}. Their technical portfolio (${activityMetrics?.top_languages?.join(', ') || 'Python, JavaScript, TypeScript'}) across ${activityMetrics?.total_repos || 'multiple'} repositories reveals ${technicalBreadth}. 648 649 ${narrativeData ? ` 650 PROFESSIONAL INTELLIGENCE CONTEXT: 651 Based on comprehensive GitHub data mining, this candidate has: 652 - Technical achievements: ${narrativeData.narratives_available?.technical_achievements || 0} evidence-backed accomplishments 653 - Leadership examples: ${narrativeData.narratives_available?.leadership_examples || 0} demonstrated instances 654 - Validated skills: ${narrativeData.validated_skills?.join(', ') || 'Multiple technical competencies'} with concrete evidence 655 - Top achievements: ${narrativeData.top_achievements?.map(a => a.achievement).join('; ') || 'Consistent technical contributions'} 656 657 This intelligence provides concrete evidence for professional claims and should inform your enhancement strategy. 658 ` : ''} 659 660 MARKET CONTEXT (2025): 661 The AI engineering landscape demands professionals who bridge autonomous systems research with production-grade implementation. The market rewards those who can lead human-AI collaboration initiatives and drive sustainable AI development practices. 662 663 ENHANCEMENT MISSION (${CONFIG.CREATIVITY_LEVEL} approach): 664 Transform their current summary using ${strategy.approach}, creating a ${strategy.tone} narrative focused on ${strategy.focus}. ${narrativeData ? 'Leverage the professional intelligence context to create evidence-backed claims rather than generic statements.' : ''} Position them as someone ready for senior AI engineering roles that shape the future of autonomous systems. 665 666 CURRENT SUMMARY: 667 "${currentSummary}" 668 669 <output_format> 670 Respond with ONLY this JSON structure. Do not include any explanatory text, process descriptions, or meta-commentary: 671 672 { 673 "enhanced_summary": "2-3 compelling sentences that immediately communicate transformative value", 674 "strategic_improvements": { 675 "positioning_shift": "How this repositions them in the market", 676 "value_amplification": "Key strengths magnified", 677 "market_alignment": "Industry demands addressed" 678 }, 679 "ats_keywords": ["keyword1", "keyword2", "keyword3"], 680 "confidence_score": 0.95 681 } 682 </output_format>` 683 } 684 ]; 685 686 try { 687 const response = await this.client.makeRequest(messages, { maxTokens: 500 }, currentSummary); 688 const responseText = response.content[0]?.text?.trim(); 689 690 // Clean the response text from potential artifacts 691 const cleanedResponse = this.cleanResponseText(responseText); 692 693 // Parse JSON response 694 let enhancementData; 695 try { 696 enhancementData = JSON.parse(cleanedResponse); 697 698 // Additional cleaning of the enhanced_summary field 699 if (enhancementData.enhanced_summary) { 700 enhancementData.enhanced_summary = this.cleanEnhancedContent(enhancementData.enhanced_summary); 701 } 702 } catch (parseError) { 703 console.warn('⚠️ JSON parsing failed, attempting content extraction'); 704 enhancementData = this.extractContentFromText(cleanedResponse); 705 } 706 707 return { 708 original: currentSummary, 709 enhanced: enhancementData.enhanced_summary, 710 strategic_analysis: enhancementData.strategic_improvements, 711 ats_optimization: enhancementData.ats_keywords, 712 confidence_score: enhancementData.confidence_score, 713 enhancement_applied: true, 714 prompt_strategy: strategy.approach, 715 improvement_notes: this.analyzeSummaryImprovement(currentSummary, enhancementData.enhanced_summary) 716 }; 717 } catch (error) { 718 console.warn('⚠️ Professional summary enhancement failed, using original'); 719 return { 720 original: currentSummary, 721 enhanced: currentSummary, 722 enhancement_applied: false, 723 error: error.message 724 }; 725 } 726 } 727 728 /** 729 * Enhance skills section with proficiency analysis 730 * Enhanced with XML structuring and few-shot learning (Issues #96, #97) 731 */ 732 async enhanceSkillsSection(cvData, activityMetrics) { 733 // Use XML-structured prompts with few-shot examples for enhanced quality 734 if (this.useXMLPrompts) { 735 return await this.enhanceSkillsSectionXML(cvData, activityMetrics); 736 } 737 738 // Legacy method for backward compatibility 739 return await this.enhanceSkillsSectionLegacy(cvData, activityMetrics); 740 } 741 742 /** 743 * Enhanced skills section using XML structuring and few-shot prompting 744 */ 745 async enhanceSkillsSectionXML(cvData, activityMetrics) { 746 console.log('🔨 Using XML-structured skills enhancement prompt...'); 747 748 try { 749 // Construct XML-structured prompt with few-shot examples 750 const promptResult = await this.xmlIntegrator.enhanceSkillsSectionXML( 751 cvData, 752 activityMetrics, 753 CONFIG.CREATIVITY_LEVEL 754 ); 755 756 console.log(`📊 Expected skills quality improvement: ${(promptResult.quality_expected * 100).toFixed(1)}%`); 757 758 // Create messages for Claude API 759 const messages = [ 760 { 761 role: 'system', 762 content: 'You are a professional skills analysis specialist. Respond ONLY with the requested JSON structure. Do not include explanatory text or meta-commentary. Follow the provided examples for structure and quality.' 763 }, 764 { 765 role: 'user', 766 content: promptResult.xmlPrompt 767 } 768 ]; 769 770 // Make API request 771 const response = await this.client.makeRequest(messages, { maxTokens: 1000 }, JSON.stringify(cvData.skills)); 772 const responseText = response.content[0]?.text?.trim(); 773 774 // Clean and parse response 775 const cleanedResponse = this.cleanResponseText(responseText); 776 let skillsData; 777 778 try { 779 skillsData = JSON.parse(cleanedResponse); 780 } catch (parseError) { 781 console.warn('⚠️ Skills JSON parsing failed, using fallback structure'); 782 skillsData = { 783 skill_architecture: { core_competencies: [], emerging_expertise: [], market_differentiators: [] }, 784 development_roadmap: { immediate_priorities: [], strategic_investments: [], innovation_opportunities: [] }, 785 positioning_strategy: { technical_narrative: this.cleanEnhancedContent(cleanedResponse).substring(0, 200) + '...' }, 786 confidence_assessment: 0.7 787 }; 788 } 789 790 // Validate response quality 791 const validation = await this.xmlIntegrator.validateResponse(skillsData, 'skills-enhancement', promptResult.quality_expected); 792 793 console.log(`✅ Skills validation: ${validation.valid ? 'PASSED' : 'FAILED'} (Score: ${(validation.score * 100).toFixed(1)}%)`); 794 795 return { 796 skill_analysis: skillsData.skill_architecture, 797 development_roadmap: skillsData.development_roadmap, 798 positioning_strategy: skillsData.positioning_strategy, 799 confidence_score: skillsData.confidence_assessment || validation.score, 800 github_context: { 801 activity_score: CONFIG.ACTIVITY_SCORE, 802 top_languages: activityMetrics?.top_languages || [], 803 total_repos: activityMetrics?.total_repos || 0 804 }, 805 enhancement_applied: true, 806 prompt_strategy: 'xml-few-shot', 807 xml_metadata: promptResult.metadata, 808 validation_results: validation, 809 recommendations: this.extractSkillRecommendations(skillsData), 810 quality_indicators: { 811 xml_structured: true, 812 few_shot_guided: true, 813 validation_passed: validation.valid, 814 quality_score: validation.score, 815 expected_improvement: promptResult.quality_expected 816 } 817 }; 818 819 } catch (error) { 820 console.warn('⚠️ XML skills enhancement failed, falling back to legacy method'); 821 return await this.enhanceSkillsSectionLegacy(cvData, activityMetrics); 822 } 823 } 824 825 /** 826 * Legacy skills enhancement method 827 */ 828 async enhanceSkillsSectionLegacy(cvData, activityMetrics) { 829 // Expert personas based on creativity level 830 const expertPersonas = { 831 'conservative': { 832 persona: 'Dr. James Liu, Principal Engineering Manager at Google DeepMind, who has assessed technical skills of 500+ AI engineers for critical autonomous systems projects.', 833 approach: 'evidence-based skill validation with proven industry frameworks', 834 focus: 'demonstrable expertise with quantifiable proficiency levels' 835 }, 836 'balanced': { 837 persona: 'Maria Santos, VP of Engineering at Anthropic, who built the technical hiring framework for constitutional AI development teams.', 838 approach: 'strategic skill positioning that balances depth with market relevance', 839 focus: 'comprehensive technical portfolio with growth trajectory' 840 }, 841 'creative': { 842 persona: 'Dr. Raj Patel, Chief Technology Officer at Scale AI, pioneering the next generation of human-AI collaborative systems.', 843 approach: 'innovative skill narrative that positions for emerging opportunities', 844 focus: 'transformative technical capabilities with future-ready expertise' 845 }, 846 'innovative': { 847 persona: 'Alex Chen, Co-founder and CTO of Adept AI, revolutionizing how AI agents interact with complex software systems.', 848 approach: 'visionary skill architecture for paradigm-shifting AI development', 849 focus: 'breakthrough technical innovation with industry-defining potential' 850 } 851 }; 852 853 const expert = expertPersonas[CONFIG.CREATIVITY_LEVEL] || expertPersonas['balanced']; 854 855 // Activity-based insights 856 const skillDepthIndicator = CONFIG.ACTIVITY_SCORE >= 80 ? 'demonstrates exceptional depth across multiple domains' : 857 CONFIG.ACTIVITY_SCORE >= 60 ? 'shows strong technical versatility with consistent growth' : 858 CONFIG.ACTIVITY_SCORE >= 40 ? 'exhibits focused expertise with quality-driven development' : 859 'displays specialized knowledge with strategic technical choices'; 860 861 const expertiseEvolution = (activityMetrics?.top_languages?.length || 3) >= 5 ? 'polyglot technologist with rapid technology adoption' : 862 (activityMetrics?.top_languages?.length || 3) >= 3 ? 'multi-platform engineer with strategic language selection' : 863 'domain specialist with deep technical mastery'; 864 865 const communityImpact = (activityMetrics?.total_stars || 0) >= 100 ? 'significant open-source influence with community recognition' : 866 (activityMetrics?.total_stars || 0) >= 20 ? 'growing technical influence with peer acknowledgment' : 867 'focused contribution with quality-driven development'; 868 869 const messages = [ 870 { 871 role: 'system', 872 content: 'You are a professional skills analysis specialist. Respond ONLY with the requested JSON structure. Do not include explanatory text, analysis descriptions, or meta-commentary. Provide only clean, structured data without any additional formatting or process explanations.' 873 }, 874 { 875 role: 'user', 876 content: `You are ${expert.persona} 877 878 TECHNICAL PROFILE ANALYSIS: 879 You're evaluating a developer who ${skillDepthIndicator}, functioning as a ${expertiseEvolution} with ${communityImpact}. Their GitHub footprint (${CONFIG.ACTIVITY_SCORE}/100 activity, ${activityMetrics?.top_languages?.join(', ') || 'Python, JavaScript, TypeScript'} expertise, ${activityMetrics?.total_repos || 'multiple'} repositories) reveals sophisticated technical judgment. 880 881 MARKET INTELLIGENCE (2025): 882 The AI engineering market prioritizes professionals who master autonomous agent development, real-time AI system optimization, and human-AI collaboration interfaces. Critical skills include production ML ops, edge computing for AI, and sustainable AI architecture patterns. 883 884 SKILL ENHANCEMENT MISSION (${CONFIG.CREATIVITY_LEVEL} methodology): 885 Using ${expert.approach}, create a ${expert.focus} skills analysis that positions this developer for senior AI engineering roles requiring both technical mastery and innovative thinking. 886 887 GITHUB EVIDENCE BASE: 888 - Activity Pattern: ${CONFIG.ACTIVITY_SCORE}/100 (${skillDepthIndicator}) 889 - Language Portfolio: ${activityMetrics?.top_languages?.join(', ') || 'Python, JavaScript, TypeScript'} 890 - Repository Volume: ${activityMetrics?.total_repos || 'N/A'} projects 891 - Community Recognition: ${activityMetrics?.total_stars || 'N/A'} stars 892 893 <output_format> 894 Respond with ONLY this JSON structure: 895 896 { 897 "skill_architecture": { 898 "core_competencies": [ 899 {"skill": "AI/ML Systems", "proficiency": "Expert", "evidence": "GitHub activity justification"}, 900 {"skill": "Software Architecture", "proficiency": "Advanced", "evidence": "Repository pattern analysis"} 901 ], 902 "emerging_expertise": ["Future-ready skills based on current trajectory"], 903 "market_differentiators": ["Unique technical combinations that create competitive advantage"] 904 }, 905 "development_roadmap": { 906 "immediate_priorities": ["Skills to develop in next 6 months"], 907 "strategic_investments": ["Technologies to master for market positioning"], 908 "innovation_opportunities": ["Cutting-edge areas for thought leadership"] 909 }, 910 "positioning_strategy": { 911 "technical_narrative": "How to present this skill portfolio compellingly", 912 "market_alignment": "Industry demands this portfolio addresses", 913 "differentiation_thesis": "What makes this skill combination unique" 914 }, 915 "confidence_assessment": 0.95 916 } 917 </output_format>` 918 } 919 ]; 920 921 try { 922 const response = await this.client.makeRequest(messages, { maxTokens: 800 }, JSON.stringify(cvData.skills)); 923 const responseText = response.content[0]?.text?.trim(); 924 925 // Clean the response text from potential artifacts 926 const cleanedResponse = this.cleanResponseText(responseText); 927 928 // Parse structured JSON response 929 let skillsData; 930 try { 931 skillsData = JSON.parse(cleanedResponse); 932 } catch (parseError) { 933 console.warn('⚠️ Skills JSON parsing failed, using fallback structure'); 934 // Fallback structure if JSON parsing fails 935 skillsData = { 936 skill_architecture: { core_competencies: [], emerging_expertise: [], market_differentiators: [] }, 937 development_roadmap: { immediate_priorities: [], strategic_investments: [], innovation_opportunities: [] }, 938 positioning_strategy: { technical_narrative: this.cleanEnhancedContent(cleanedResponse).substring(0, 200) + '...' }, 939 confidence_assessment: 0.7 940 }; 941 } 942 943 return { 944 skill_analysis: skillsData.skill_architecture, 945 development_roadmap: skillsData.development_roadmap, 946 positioning_strategy: skillsData.positioning_strategy, 947 confidence_score: skillsData.confidence_assessment, 948 github_context: { 949 activity_score: CONFIG.ACTIVITY_SCORE, 950 top_languages: activityMetrics?.top_languages || [], 951 total_repos: activityMetrics?.total_repos || 0, 952 expertise_evolution: expertiseEvolution, 953 community_impact: communityImpact 954 }, 955 enhancement_applied: true, 956 expert_methodology: expert.approach, 957 recommendations: this.extractSkillRecommendations(skillsData) 958 }; 959 } catch (error) { 960 console.warn('⚠️ Skills enhancement failed'); 961 return { 962 enhancement_applied: false, 963 error: error.message 964 }; 965 } 966 } 967 968 /** 969 * Enhance experience descriptions 970 * Enhanced with XML structuring and few-shot learning (Issues #96, #97) 971 */ 972 async enhanceExperience(cvData, activityMetrics) { 973 // Use XML-structured prompts with few-shot examples for enhanced quality 974 if (this.useXMLPrompts) { 975 return await this.enhanceExperienceXML(cvData, activityMetrics); 976 } 977 978 // Legacy method for backward compatibility 979 return await this.enhanceExperienceLegacy(cvData, activityMetrics); 980 } 981 982 /** 983 * Enhanced experience using XML structuring and few-shot prompting 984 */ 985 async enhanceExperienceXML(cvData, activityMetrics) { 986 console.log('🔨 Using XML-structured experience enhancement prompt...'); 987 988 try { 989 // Construct XML-structured prompt with few-shot examples 990 const promptResult = await this.xmlIntegrator.enhanceExperienceXML( 991 cvData, 992 activityMetrics, 993 CONFIG.CREATIVITY_LEVEL 994 ); 995 996 console.log(`📊 Expected experience quality improvement: ${(promptResult.quality_expected * 100).toFixed(1)}%`); 997 998 // Create messages for Claude API 999 const messages = [ 1000 { 1001 role: 'system', 1002 content: 'You are a professional experience enhancement specialist. Respond ONLY with the requested JSON structure. Do not include explanatory text or meta-commentary. Follow the provided examples for professional language and structure.' 1003 }, 1004 { 1005 role: 'user', 1006 content: promptResult.xmlPrompt 1007 } 1008 ]; 1009 1010 // Make API request 1011 const response = await this.client.makeRequest(messages, { maxTokens: 1200 }, JSON.stringify(cvData.experience)); 1012 const responseText = response.content[0]?.text?.trim(); 1013 1014 // Clean and parse response 1015 const cleanedResponse = this.cleanResponseText(responseText); 1016 let experienceData; 1017 1018 try { 1019 experienceData = JSON.parse(cleanedResponse); 1020 } catch (parseError) { 1021 console.warn('⚠️ Experience JSON parsing failed, using fallback structure'); 1022 experienceData = { 1023 experience_transformation: [], 1024 career_progression_narrative: { leadership_evolution: this.cleanEnhancedContent(cleanedResponse).substring(0, 200) + '...' }, 1025 strategic_positioning: { technical_authority: 'Advanced AI/ML systems development' }, 1026 confidence_score: 0.7 1027 }; 1028 } 1029 1030 // Validate response quality 1031 const validation = await this.xmlIntegrator.validateResponse(experienceData, 'experience-enhancement', promptResult.quality_expected); 1032 1033 console.log(`✅ Experience validation: ${validation.valid ? 'PASSED' : 'FAILED'} (Score: ${(validation.score * 100).toFixed(1)}%)`); 1034 1035 return { 1036 experience_enhancement: experienceData.experience_transformation, 1037 career_narrative: experienceData.career_progression_narrative, 1038 strategic_positioning: experienceData.strategic_positioning, 1039 confidence_score: experienceData.confidence_score || validation.score, 1040 leadership_progression: this.analyzeLeadershipProgression(CONFIG.ACTIVITY_SCORE), 1041 market_positioning: this.analyzeMarketPositioning(CONFIG.ACTIVITY_SCORE, activityMetrics), 1042 focus_areas: [ 1043 'Autonomous systems innovation', 1044 'Technical leadership progression', 1045 'Quantifiable business impact', 1046 'AI/ML architecture excellence', 1047 'Human-AI collaboration leadership' 1048 ], 1049 enhancement_applied: true, 1050 prompt_strategy: 'xml-few-shot', 1051 xml_metadata: promptResult.metadata, 1052 validation_results: validation, 1053 quality_indicators: { 1054 xml_structured: true, 1055 few_shot_guided: true, 1056 validation_passed: validation.valid, 1057 quality_score: validation.score, 1058 expected_improvement: promptResult.quality_expected 1059 } 1060 }; 1061 1062 } catch (error) { 1063 console.warn('⚠️ XML experience enhancement failed, falling back to legacy method'); 1064 return await this.enhanceExperienceLegacy(cvData, activityMetrics); 1065 } 1066 } 1067 1068 /** 1069 * Legacy experience enhancement method 1070 */ 1071 async enhanceExperienceLegacy(cvData, activityMetrics) { 1072 // Career narrative experts by creativity level 1073 const careerExperts = { 1074 'conservative': { 1075 persona: 'Linda Zhang, Executive Coach specializing in AI leadership transitions, who has guided 150+ engineers to senior roles at Google, Microsoft, and Amazon.', 1076 methodology: 'proven achievement quantification with established leadership frameworks', 1077 narrative_style: 'authoritative progression with measurable impact' 1078 }, 1079 'balanced': { 1080 persona: 'Carlos Rivera, Career Strategy Partner at Sequoia Capital, helping technical founders articulate their journey from engineer to visionary leader.', 1081 methodology: 'strategic storytelling that balances technical depth with business acumen', 1082 narrative_style: 'compelling leadership evolution with innovation highlights' 1083 }, 1084 'creative': { 1085 persona: 'Dr. Aisha Patel, Executive Career Architect for AI unicorn startups, crafting narratives for technical leaders who will define the next decade of AI.', 1086 methodology: 'transformative experience positioning for breakthrough opportunities', 1087 narrative_style: 'visionary technical leadership with paradigm-shifting impact' 1088 }, 1089 'innovative': { 1090 persona: 'Morgan Kim, Chief People Officer at OpenAI, recognizing exceptional technical narratives that signal revolutionary potential in AI development.', 1091 methodology: 'revolutionary experience articulation for industry-defining roles', 1092 narrative_style: 'groundbreaking technical innovation with transformational leadership' 1093 } 1094 }; 1095 1096 const expert = careerExperts[CONFIG.CREATIVITY_LEVEL] || careerExperts['balanced']; 1097 1098 // Dynamic career trajectory analysis 1099 const leadershipProgression = CONFIG.ACTIVITY_SCORE >= 80 ? 'demonstrates exceptional technical leadership with consistent innovation delivery' : 1100 CONFIG.ACTIVITY_SCORE >= 60 ? 'shows strong technical influence with growing leadership responsibilities' : 1101 CONFIG.ACTIVITY_SCORE >= 40 ? 'exhibits focused technical expertise with emerging leadership qualities' : 1102 'displays deep technical specialization with selective leadership engagement'; 1103 1104 const innovationCapacity = (activityMetrics?.total_repos || 0) >= 20 ? 'prolific technical innovator with diverse project portfolio' : 1105 (activityMetrics?.total_repos || 0) >= 10 ? 'strategic technical contributor with impactful project selection' : 1106 'focused technical expert with high-impact project concentration'; 1107 1108 const marketPositioning = CONFIG.ACTIVITY_SCORE >= 70 && (activityMetrics?.total_stars || 0) >= 50 ? 'industry-recognized technical leader ready for executive roles' : 1109 CONFIG.ACTIVITY_SCORE >= 50 ? 'emerging technical authority positioned for senior leadership' : 1110 'specialized technical expert ready for expanded influence'; 1111 1112 const messages = [ 1113 { 1114 role: 'system', 1115 content: 'You are a professional experience enhancement specialist. Respond ONLY with the requested JSON structure. Do not include explanatory text, process descriptions, or meta-commentary. Provide only clean, structured data without any additional formatting or analysis explanations.' 1116 }, 1117 { 1118 role: 'user', 1119 content: `You are ${expert.persona} 1120 1121 CAREER TRAJECTORY ANALYSIS: 1122 You're crafting the experience narrative for a professional who ${leadershipProgression}, functioning as a ${innovationCapacity}. Their technical footprint positions them as a ${marketPositioning}. 1123 1124 CURRENT PROFILE INDICATORS: 1125 - Technical Leadership Score: ${CONFIG.ACTIVITY_SCORE}/100 1126 - Innovation Portfolio: ${activityMetrics?.total_repos || 'Multiple'} projects demonstrating ${innovationCapacity} 1127 - Community Influence: ${activityMetrics?.total_stars || 'Growing'} recognition points 1128 - Technology Mastery: ${activityMetrics?.top_languages?.join(', ') || 'Python, JavaScript, TypeScript'} expertise 1129 1130 MARKET CONTEXT (2025): 1131 Senior AI engineering roles demand professionals who can architect autonomous systems, lead human-AI collaboration initiatives, and drive sustainable AI development at scale. The market rewards leaders who bridge cutting-edge research with production-grade implementation. 1132 1133 NARRATIVE ENHANCEMENT MISSION (${CONFIG.CREATIVITY_LEVEL} approach): 1134 Using ${expert.methodology}, create ${expert.narrative_style} that transforms their technical journey into a compelling leadership progression story. Focus on autonomous systems innovation, team impact, and business outcome alignment. 1135 1136 CURRENT EXPERIENCE DATA: 1137 ${JSON.stringify(cvData.experience || [], null, 2)} 1138 1139 <output_format> 1140 Respond with ONLY this JSON structure: 1141 1142 { 1143 "experience_transformation": [ 1144 { 1145 "role_title": "Enhanced title that reflects leadership scope", 1146 "impact_narrative": "Compelling 2-3 sentence description showcasing autonomous systems innovation and quantifiable business impact", 1147 "key_achievements": [ 1148 "Specific accomplishment with metrics (e.g., 'Architected autonomous ML pipeline reducing deployment time by 75%')", 1149 "Leadership milestone with team impact", 1150 "Innovation breakthrough with industry relevance" 1151 ], 1152 "technical_leadership_evidence": "How this role demonstrates progression toward senior AI engineering leadership" 1153 } 1154 ], 1155 "career_progression_narrative": { 1156 "leadership_evolution": "How their journey shows increasing technical and team leadership", 1157 "innovation_trajectory": "Pattern of growing influence in AI/autonomous systems development", 1158 "market_positioning": "Why this experience prepares them for senior AI engineering roles" 1159 }, 1160 "strategic_positioning": { 1161 "technical_authority": "Areas where they demonstrate thought leadership", 1162 "business_impact_pattern": "How their work consistently drives organizational outcomes", 1163 "future_readiness": "Evidence they're prepared for next-level AI engineering challenges" 1164 }, 1165 "confidence_score": 0.95 1166 } 1167 </output_format>` 1168 } 1169 ]; 1170 1171 try { 1172 const response = await this.client.makeRequest(messages, { maxTokens: 900 }, JSON.stringify(cvData.experience)); 1173 const responseText = response.content[0]?.text?.trim(); 1174 1175 // Clean the response text from potential artifacts 1176 const cleanedResponse = this.cleanResponseText(responseText); 1177 1178 // Parse structured JSON response 1179 let experienceData; 1180 try { 1181 experienceData = JSON.parse(cleanedResponse); 1182 } catch (parseError) { 1183 console.warn('⚠️ Experience JSON parsing failed, using fallback structure'); 1184 // Fallback structure if JSON parsing fails 1185 experienceData = { 1186 experience_transformation: [], 1187 career_progression_narrative: { leadership_evolution: this.cleanEnhancedContent(cleanedResponse).substring(0, 200) + '...' }, 1188 strategic_positioning: { technical_authority: 'Advanced AI/ML systems development' }, 1189 confidence_score: 0.7 1190 }; 1191 } 1192 1193 return { 1194 experience_enhancement: experienceData.experience_transformation, 1195 career_narrative: experienceData.career_progression_narrative, 1196 strategic_positioning: experienceData.strategic_positioning, 1197 confidence_score: experienceData.confidence_score, 1198 leadership_progression: leadershipProgression, 1199 market_positioning: marketPositioning, 1200 focus_areas: [ 1201 'Autonomous systems innovation', 1202 'Technical leadership progression', 1203 'Quantifiable business impact', 1204 'AI/ML architecture excellence', 1205 'Human-AI collaboration leadership' 1206 ], 1207 enhancement_applied: true, 1208 expert_methodology: expert.methodology 1209 }; 1210 } catch (error) { 1211 console.warn('⚠️ Experience enhancement failed'); 1212 return { 1213 enhancement_applied: false, 1214 error: error.message 1215 }; 1216 } 1217 } 1218 1219 /** 1220 * Enhance project descriptions with impact analysis 1221 */ 1222 async enhanceProjects(cvData, activityMetrics) { 1223 const messages = [ 1224 { 1225 role: 'system', 1226 content: 'You are a professional project portfolio specialist. Respond with clean, professional project enhancement strategies only. Do not include explanatory text, process descriptions, or meta-commentary. Provide only the requested enhancement content without additional formatting or analysis.' 1227 }, 1228 { 1229 role: 'user', 1230 content: `As a technical project portfolio specialist, enhance project descriptions for an AI Engineer's portfolio: 1231 1232 **GitHub Portfolio Context:** 1233 - Total Projects: ${activityMetrics?.total_repos || 'N/A'} 1234 - Community Engagement: ${activityMetrics?.total_stars || 'N/A'} stars, ${activityMetrics?.total_forks || 'N/A'} forks 1235 - Language Diversity: ${activityMetrics?.language_count || 'N/A'} languages 1236 - Development Activity: ${CONFIG.ACTIVITY_SCORE}/100 1237 1238 **Project Enhancement Strategy:** 1239 1. Transform technical descriptions into compelling project narratives 1240 2. Highlight innovation and problem-solving approach 1241 3. Quantify project impact and technical complexity 1242 4. Demonstrate expertise in AI, autonomous systems, and software architecture 1243 5. Show progression from simple to sophisticated projects 1244 1245 **Target Project Categories:** 1246 - AI/ML Systems & Autonomous Agents 1247 - Software Architecture & System Design 1248 - Real-time Processing & Edge Computing 1249 - Developer Tools & Automation 1250 - Research & Innovation Projects 1251 1252 **Enhancement Style:** ${CONFIG.CREATIVITY_LEVEL} 1253 1254 <output_format> 1255 Provide enhanced project descriptions that: 1256 - Lead with problem statement and innovative solution 1257 - Highlight technical sophistication and architectural decisions 1258 - Quantify impact, performance, or adoption metrics 1259 - Connect to broader industry trends in AI/autonomous systems 1260 - Demonstrate continuous learning and technology adoption 1261 1262 Create compelling project narratives that showcase technical expertise and innovation capability. 1263 </output_format>` 1264 } 1265 ]; 1266 1267 try { 1268 const response = await this.client.makeRequest(messages, { maxTokens: 600 }, JSON.stringify(cvData.projects)); 1269 const responseText = response.content[0]?.text?.trim(); 1270 1271 // Clean the response text from potential artifacts 1272 const cleanedResponse = this.cleanResponseText(responseText); 1273 const projectEnhancement = this.cleanEnhancedContent(cleanedResponse); 1274 1275 return { 1276 enhancement_strategy: projectEnhancement, 1277 portfolio_metrics: { 1278 total_repos: activityMetrics?.total_repos || 0, 1279 community_impact: (activityMetrics?.total_stars || 0) + (activityMetrics?.total_forks || 0), 1280 language_diversity: activityMetrics?.language_count || 0, 1281 activity_score: CONFIG.ACTIVITY_SCORE 1282 }, 1283 enhancement_applied: true 1284 }; 1285 } catch (error) { 1286 console.warn('⚠️ Project enhancement failed'); 1287 return { 1288 enhancement_applied: false, 1289 error: error.message 1290 }; 1291 } 1292 } 1293 1294 /** 1295 * Generate strategic career insights (advanced enhancement) 1296 */ 1297 async generateStrategicInsights(cvData, activityMetrics) { 1298 // Strategic visionaries by creativity level 1299 const strategicExperts = { 1300 'conservative': { 1301 persona: 'Dr. Jennifer Wu, Managing Partner at Andreessen Horowitz focusing on AI infrastructure investments, who has guided 50+ technical leaders to C-suite roles.', 1302 approach: 'proven strategic positioning with established career advancement frameworks', 1303 vision_scope: 'systematic market positioning with validated growth strategies' 1304 }, 1305 'balanced': { 1306 persona: 'Robert Kim, Executive Strategy Consultant for AI unicorns (Anthropic, OpenAI, Scale), architecting career trajectories for technical visionaries.', 1307 approach: 'comprehensive strategic analysis balancing technical mastery with market opportunity', 1308 vision_scope: 'multifaceted growth strategy with innovation leadership potential' 1309 }, 1310 'creative': { 1311 persona: 'Dr. Priya Sharma, Chief Strategy Officer at Mistral AI, identifying breakthrough talent for next-generation AI development paradigms.', 1312 approach: 'innovative strategic positioning for emerging AI leadership opportunities', 1313 vision_scope: 'transformative career architecture for industry-shaping roles' 1314 }, 1315 'innovative': { 1316 persona: 'Alex Thompson, Founding Partner at AI-focused venture fund, recognizing technical leaders who will define the next decade of artificial intelligence.', 1317 approach: 'revolutionary strategic vision for paradigm-defining AI leadership', 1318 vision_scope: 'visionary career trajectory for industry transformation leaders' 1319 } 1320 }; 1321 1322 const strategist = strategicExperts[CONFIG.CREATIVITY_LEVEL] || strategicExperts['balanced']; 1323 1324 // Strategic positioning analysis 1325 const leadershipReadiness = CONFIG.ACTIVITY_SCORE >= 80 ? 'ready for executive AI leadership roles with proven innovation capacity' : 1326 CONFIG.ACTIVITY_SCORE >= 60 ? 'positioned for senior technical leadership with strong growth trajectory' : 1327 CONFIG.ACTIVITY_SCORE >= 40 ? 'prepared for expanded technical influence with focused expertise development' : 1328 'specialized for high-impact technical roles with strategic skill building'; 1329 1330 const marketDifferentiation = (activityMetrics?.total_stars || 0) >= 100 ? 'established technical thought leader with significant industry influence' : 1331 (activityMetrics?.total_stars || 0) >= 20 ? 'emerging technical authority with growing market recognition' : 1332 'focused technical expert with potential for expanded influence'; 1333 1334 const innovationProfile = (activityMetrics?.top_languages?.length || 3) >= 5 ? 'polyglot innovator positioned for cross-platform AI architecture leadership' : 1335 (activityMetrics?.top_languages?.length || 3) >= 3 ? 'multi-domain technologist ready for comprehensive AI system leadership' : 1336 'specialized expert positioned for deep technical domain leadership'; 1337 1338 const messages = [ 1339 { 1340 role: 'system', 1341 content: 'You are a professional strategic insights specialist. Respond ONLY with the requested JSON structure. Do not include explanatory text, analysis descriptions, or meta-commentary. Provide only clean, structured strategic data without any additional formatting or process explanations.' 1342 }, 1343 { 1344 role: 'user', 1345 content: `You are ${strategist.persona} 1346 1347 STRATEGIC LEADERSHIP ASSESSMENT: 1348 You're analyzing a technical professional who is ${leadershipReadiness}, functioning as a ${marketDifferentiation} with characteristics of a ${innovationProfile}. Their technical foundation positions them uniquely in the evolving AI leadership landscape. 1349 1350 PROFESSIONAL INTELLIGENCE METRICS: 1351 - Leadership Readiness Score: ${CONFIG.ACTIVITY_SCORE}/100 1352 - Innovation Portfolio: ${activityMetrics?.total_repos || 'Comprehensive'} projects demonstrating ${innovationProfile} 1353 - Market Influence: ${activityMetrics?.total_stars || 'Growing'} recognition indicating ${marketDifferentiation} 1354 - Technical Versatility: ${activityMetrics?.top_languages?.join(', ') || 'Python, JavaScript, TypeScript'} mastery 1355 - Geographic Advantage: Tasmania, Australia (unique timezone positioning for global AI collaboration) 1356 1357 MARKET LANDSCAPE ANALYSIS (2025): 1358 The AI leadership market demands professionals who can architect autonomous agent ecosystems, lead multi-modal AI development, and drive responsible AI governance at scale. Critical opportunities exist in human-AI collaboration interfaces, sustainable AI development, and AI safety leadership. 1359 1360 STRATEGIC VISION MISSION (${CONFIG.CREATIVITY_LEVEL} methodology): 1361 Using ${strategist.approach}, create ${strategist.vision_scope} that positions this professional for the highest-impact AI leadership opportunities in the next 3-5 years. 1362 1363 CURRENT TECHNICAL PROFILE: 1364 ${JSON.stringify(cvData, null, 2)} 1365 1366 <output_format> 1367 Respond with ONLY this JSON structure: 1368 1369 { 1370 "executive_positioning": { 1371 "primary_value_proposition": "Core differentiating strength for AI leadership roles", 1372 "market_opportunity_alignment": "How their profile aligns with emerging AI market needs", 1373 "competitive_differentiation": "What makes them uniquely positioned vs other AI leaders" 1374 }, 1375 "growth_trajectory": { 1376 "immediate_opportunities": [ 1377 "Specific roles/companies to target in next 12 months" 1378 ], 1379 "strategic_skill_investments": [ 1380 "Key capabilities to develop for maximum career impact" 1381 ], 1382 "thought_leadership_areas": [ 1383 "Technical domains where they could establish industry authority" 1384 ] 1385 }, 1386 "market_positioning_strategy": { 1387 "industry_narrative": "How to position their technical journey as preparation for AI leadership", 1388 "geographic_leverage": "How to maximize Tasmania location for global AI opportunities", 1389 "innovation_thesis": "Technical philosophy that differentiates their approach to AI development" 1390 }, 1391 "execution_roadmap": { 1392 "quarter_1_actions": ["Immediate steps to enhance market positioning"], 1393 "year_1_milestones": ["Key achievements to establish thought leadership"], 1394 "long_term_vision": "Ultimate career destination and impact potential" 1395 }, 1396 "confidence_assessment": 0.95 1397 } 1398 </output_format>` 1399 } 1400 ]; 1401 1402 try { 1403 const response = await this.client.makeRequest(messages, { maxTokens: 1000 }, JSON.stringify(cvData)); 1404 const responseText = response.content[0]?.text?.trim(); 1405 1406 // Clean the response text from potential artifacts 1407 const cleanedResponse = this.cleanResponseText(responseText); 1408 1409 // Parse comprehensive strategic JSON response 1410 let strategicData; 1411 try { 1412 strategicData = JSON.parse(cleanedResponse); 1413 } catch (parseError) { 1414 console.warn('⚠️ Strategic insights JSON parsing failed, using fallback structure'); 1415 // Fallback structure if JSON parsing fails 1416 strategicData = { 1417 executive_positioning: { primary_value_proposition: this.cleanEnhancedContent(cleanedResponse).substring(0, 150) + '...' }, 1418 growth_trajectory: { immediate_opportunities: [], strategic_skill_investments: [] }, 1419 market_positioning_strategy: { industry_narrative: 'AI engineering leadership positioning' }, 1420 execution_roadmap: { quarter_1_actions: [], year_1_milestones: [] }, 1421 confidence_assessment: 0.75 1422 }; 1423 } 1424 1425 return { 1426 executive_positioning: strategicData.executive_positioning, 1427 growth_strategy: strategicData.growth_trajectory, 1428 market_positioning: strategicData.market_positioning_strategy, 1429 execution_plan: strategicData.execution_roadmap, 1430 confidence_score: strategicData.confidence_assessment, 1431 leadership_readiness: leadershipReadiness, 1432 market_differentiation: marketDifferentiation, 1433 innovation_profile: innovationProfile, 1434 market_context: { 1435 activity_score: CONFIG.ACTIVITY_SCORE, 1436 specialization: 'AI Engineering & Autonomous Systems Leadership', 1437 location_advantage: 'Tasmania, Australia (Global AI Collaboration Hub)', 1438 innovation_focus: 'Autonomous AI Architecture, Human-AI Collaboration Leadership, Sustainable AI Development' 1439 }, 1440 strategic_methodology: strategist.approach, 1441 insights_generated: true 1442 }; 1443 } catch (error) { 1444 console.warn('⚠️ Strategic insights generation failed'); 1445 return { 1446 insights_generated: false, 1447 error: error.message 1448 }; 1449 } 1450 } 1451 1452 /** 1453 * Load current CV data from existing files 1454 */ 1455 async loadCurrentCVData() { 1456 try { 1457 const cvDataPath = path.join(CONFIG.OUTPUT_DIR, 'base-cv.json'); 1458 const cvData = await fs.readFile(cvDataPath, 'utf8'); 1459 return JSON.parse(cvData); 1460 } catch (error) { 1461 console.log('📝 No existing CV data found, using defaults'); 1462 return { 1463 professional_summary: "AI Engineer and Software Architect specializing in autonomous systems and machine learning.", 1464 skills: [], 1465 experience: [], 1466 projects: [] 1467 }; 1468 } 1469 } 1470 1471 /** 1472 * Load GitHub activity metrics 1473 */ 1474 async loadActivityMetrics() { 1475 try { 1476 const activityPath = path.join(CONFIG.OUTPUT_DIR, 'activity-summary.json'); 1477 const activityData = await fs.readFile(activityPath, 'utf8'); 1478 const parsedData = JSON.parse(activityData); 1479 1480 return { 1481 total_repos: parsedData.summary?.raw_data?.repositories || 0, 1482 total_stars: parsedData.summary?.raw_data?.stars_received || 0, 1483 total_forks: parsedData.summary?.raw_data?.total_forks || 0, 1484 top_languages: parsedData.skill_analysis?.top_3_skills || ['Python', 'JavaScript', 'TypeScript'], 1485 language_count: parsedData.summary?.raw_data?.languages_used || 0, 1486 development_velocity: parsedData.summary?.raw_data?.repositories || 0 1487 }; 1488 } catch (error) { 1489 console.log('📊 No activity metrics found, using defaults'); 1490 return { 1491 total_repos: 0, 1492 total_stars: 0, 1493 total_forks: 0, 1494 top_languages: ['Python', 'JavaScript', 'TypeScript'], 1495 language_count: 3, 1496 development_velocity: 0 1497 }; 1498 } 1499 } 1500 1501 async loadKeywordGaps() { 1502 try { 1503 const { execSync } = require('child_process'); 1504 const output = execSync('node keyword-scorer.js --gaps', { encoding: 'utf8', cwd: __dirname }); 1505 return JSON.parse(output); 1506 } catch { 1507 return { ats_score: null, top_missing: [] }; 1508 } 1509 } 1510 1511 /** 1512 * Generate enhancement summary with XML prompt engineering statistics 1513 */ 1514 generateEnhancementSummary(enhancementPlan) { 1515 const usageStats = this.client.getUsageStats(); 1516 const enhancementTime = (Date.now() - this.enhancementStartTime) / 1000; 1517 const xmlStats = this.xmlIntegrator.getStats(); 1518 1519 return { 1520 enhancement_overview: { 1521 total_stages_completed: Object.keys(enhancementPlan).filter(key => 1522 key !== 'metadata' && key !== 'enhancement_summary' 1523 ).length, 1524 creativity_level: CONFIG.CREATIVITY_LEVEL, 1525 ai_budget_used: CONFIG.AI_BUDGET, 1526 enhancement_duration_seconds: Math.round(enhancementTime), 1527 xml_prompts_enabled: this.useXMLPrompts 1528 }, 1529 token_analytics: usageStats, 1530 xml_prompt_analytics: { 1531 xml_integrator_initialized: xmlStats.initialized, 1532 xml_constructor_stats: xmlStats.xml_constructor_stats, 1533 performance_metrics: xmlStats.performance_metrics, 1534 quality_improvements: this.countQualityImprovements(enhancementPlan) 1535 }, 1536 enhancement_effectiveness: { 1537 professional_summary: enhancementPlan.professional_summary?.enhancement_applied || false, 1538 skills_analysis: enhancementPlan.skills_enhancement?.enhancement_applied || false, 1539 experience_optimization: enhancementPlan.experience_enhancement?.enhancement_applied || false, 1540 project_enhancement: enhancementPlan.project_enhancement?.enhancement_applied || false, 1541 strategic_insights: enhancementPlan.strategic_insights?.insights_generated || false 1542 }, 1543 quality_indicators: { 1544 xml_structured_prompts: this.countXMLStructuredPrompts(enhancementPlan), 1545 few_shot_guided_enhancements: this.countFewShotEnhancements(enhancementPlan), 1546 validation_passed_count: this.countValidationPasses(enhancementPlan), 1547 average_quality_score: this.calculateAverageQualityScore(enhancementPlan) 1548 }, 1549 recommendations: { 1550 content_freshness: 'Enhanced with current industry trends and XML-structured prompts', 1551 market_alignment: `Optimized for ${CONFIG.CREATIVITY_LEVEL} creativity approach with few-shot learning`, 1552 technical_positioning: 'Aligned with AI/ML industry demands using advanced prompt engineering', 1553 career_advancement: 'Strategic insights provided with evidence-based validation and quality scoring' 1554 } 1555 }; 1556 } 1557 1558 /** 1559 * Count quality improvements from XML enhancements 1560 */ 1561 countQualityImprovements(enhancementPlan) { 1562 let count = 0; 1563 ['professional_summary', 'skills_enhancement', 'experience_enhancement'].forEach(section => { 1564 if (enhancementPlan[section]?.validation_results?.quality_improvement) { 1565 count++; 1566 } 1567 }); 1568 return count; 1569 } 1570 1571 /** 1572 * Count XML-structured prompts used 1573 */ 1574 countXMLStructuredPrompts(enhancementPlan) { 1575 let count = 0; 1576 ['professional_summary', 'skills_enhancement', 'experience_enhancement'].forEach(section => { 1577 if (enhancementPlan[section]?.quality_indicators?.xml_structured) { 1578 count++; 1579 } 1580 }); 1581 return count; 1582 } 1583 1584 /** 1585 * Count few-shot guided enhancements 1586 */ 1587 countFewShotEnhancements(enhancementPlan) { 1588 let count = 0; 1589 ['professional_summary', 'skills_enhancement', 'experience_enhancement'].forEach(section => { 1590 if (enhancementPlan[section]?.quality_indicators?.few_shot_guided) { 1591 count++; 1592 } 1593 }); 1594 return count; 1595 } 1596 1597 /** 1598 * Count validation passes 1599 */ 1600 countValidationPasses(enhancementPlan) { 1601 let count = 0; 1602 ['professional_summary', 'skills_enhancement', 'experience_enhancement'].forEach(section => { 1603 if (enhancementPlan[section]?.quality_indicators?.validation_passed) { 1604 count++; 1605 } 1606 }); 1607 return count; 1608 } 1609 1610 /** 1611 * Calculate average quality score 1612 */ 1613 calculateAverageQualityScore(enhancementPlan) { 1614 const scores = []; 1615 ['professional_summary', 'skills_enhancement', 'experience_enhancement'].forEach(section => { 1616 const score = enhancementPlan[section]?.quality_indicators?.quality_score; 1617 if (score && typeof score === 'number') { 1618 scores.push(score); 1619 } 1620 }); 1621 return scores.length > 0 ? scores.reduce((sum, score) => sum + score, 0) / scores.length : 0; 1622 } 1623 1624 /** 1625 * Save enhancement results 1626 */ 1627 async saveEnhancementResults(enhancementPlan) { 1628 const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); 1629 1630 // Save comprehensive enhancement results 1631 const enhancementPath = path.join(CONFIG.OUTPUT_DIR, `ai-enhancement-${timestamp}.json`); 1632 await fs.writeFile(enhancementPath, JSON.stringify(enhancementPlan, null, 2), 'utf8'); 1633 1634 // Save current enhancement for CV integration 1635 const currentPath = path.join(CONFIG.OUTPUT_DIR, 'ai-enhancements.json'); 1636 await fs.writeFile(currentPath, JSON.stringify({ 1637 last_updated: new Date().toISOString(), 1638 creativity_level: CONFIG.CREATIVITY_LEVEL, 1639 ai_budget: CONFIG.AI_BUDGET, 1640 activity_score: CONFIG.ACTIVITY_SCORE, 1641 professional_summary: enhancementPlan.professional_summary, 1642 skills_enhancement: enhancementPlan.skills_enhancement, 1643 enhancement_summary: enhancementPlan.enhancement_summary 1644 }, null, 2), 'utf8'); 1645 1646 console.log(`💾 Enhancement results saved:`); 1647 console.log(` 🤖 Comprehensive: ${enhancementPath}`); 1648 console.log(` 📋 Current: ${currentPath}`); 1649 } 1650 1651 /** 1652 * Ensure output directory exists 1653 */ 1654 async ensureOutputDir() { 1655 try { 1656 await fs.access(CONFIG.OUTPUT_DIR); 1657 } catch { 1658 await fs.mkdir(CONFIG.OUTPUT_DIR, { recursive: true }); 1659 } 1660 } 1661 1662 /** 1663 * Clean response text from Claude API to remove meta-commentary artifacts 1664 */ 1665 cleanResponseText(text) { 1666 if (!text) return text; 1667 1668 // Remove common meta-commentary patterns 1669 const metaPatterns = [ 1670 /^Here's an enhanced.*?:\s*/i, 1671 /^\*\*Enhanced.*?\*\*\s*/i, 1672 /^Enhanced.*?:\s*/i, 1673 /\n\nThis enhancement:.*$/s, 1674 /\n\n.*?enhancement.*?:\s*\n.*$/s, 1675 /\n\n.*?improvement.*?:\s*\n.*$/s, 1676 /The.*?provided.*?placeholder.*$/s, 1677 /^I'll.*?\.\s*/i, 1678 /^Let me.*?\.\s*/i 1679 ]; 1680 1681 let cleaned = text; 1682 for (const pattern of metaPatterns) { 1683 cleaned = cleaned.replace(pattern, ''); 1684 } 1685 1686 return cleaned.trim(); 1687 } 1688 1689 /** 1690 * Clean enhanced content from artifacts and meta-commentary 1691 */ 1692 cleanEnhancedContent(content) { 1693 if (!content) return content; 1694 1695 // Remove specific meta-commentary patterns from enhanced content 1696 let cleaned = content 1697 .replace(/^Here's an enhanced.*?:\s*/i, '') 1698 .replace(/^\*\*Enhanced.*?\*\*\s*/i, '') 1699 .replace(/^Enhanced.*?:\s*/i, '') 1700 .replace(/\n\nThis enhancement:.*$/s, '') 1701 .replace(/\n\n.*?enhancement.*?$/s, '') 1702 .replace(/The.*?provided.*?placeholder.*$/s, '') 1703 .replace(/^- .*?\n/gm, '') // Remove bullet point explanations 1704 .replace(/\n\n+/g, ' ') // Replace multiple newlines with single space 1705 .trim(); 1706 1707 // If content still looks like meta-commentary, extract just the summary part 1708 if (cleaned.toLowerCase().includes('this ') || cleaned.toLowerCase().includes('the ')) { 1709 const summaryMatch = cleaned.match(/Results-driven.*?\.|Innovative.*?\.|Experienced.*?\.|Senior.*?\./i); 1710 if (summaryMatch) { 1711 cleaned = summaryMatch[0]; 1712 } 1713 } 1714 1715 return cleaned; 1716 } 1717 1718 /** 1719 * Extract content from text when JSON parsing fails 1720 */ 1721 extractContentFromText(text) { 1722 const cleaned = this.cleanResponseText(text); 1723 1724 // Try to parse as JSON first, even if it looks malformed 1725 try { 1726 const jsonMatch = cleaned.match(/\{[\s\S]*\}/); 1727 if (jsonMatch) { 1728 const jsonData = JSON.parse(jsonMatch[0]); 1729 if (jsonData.enhanced_summary) { 1730 jsonData.enhanced_summary = this.cleanEnhancedContent(jsonData.enhanced_summary); 1731 return jsonData; 1732 } 1733 } 1734 } catch (error) { 1735 // Continue with text extraction 1736 } 1737 1738 // Try to extract enhanced summary from various patterns 1739 let enhancedSummary = cleaned; 1740 1741 // Look for enhanced summary in quotes or after colons 1742 const summaryPatterns = [ 1743 /"enhanced_summary":\s*"([^"]+)"/i, 1744 /enhanced.*?summary.*?:\s*"([^"]+)"/i, 1745 /summary.*?:\s*"([^"]+)"/i, 1746 /"([^"]*(?:AI|Engineer|Software|Architect)[^"]*)"/i 1747 ]; 1748 1749 for (const pattern of summaryPatterns) { 1750 const match = cleaned.match(pattern); 1751 if (match && match[1]) { 1752 enhancedSummary = match[1]; 1753 break; 1754 } 1755 } 1756 1757 return { 1758 enhanced_summary: this.cleanEnhancedContent(enhancedSummary), 1759 strategic_improvements: { positioning_shift: "Enhanced with AI optimization" }, 1760 ats_keywords: [], 1761 confidence_score: 0.7 1762 }; 1763 } 1764 1765 // Helper methods 1766 analyzeSummaryImprovement(original, enhanced) { 1767 if (!enhanced) return { improvement_indicators: ['Enhancement processing failed'] }; 1768 1769 return { 1770 length_change: enhanced.length - original.length, 1771 word_count_change: enhanced.split(' ').length - original.split(' ').length, 1772 improvement_indicators: [ 1773 'Enhanced professional language', 1774 'Improved value proposition clarity', 1775 'Strengthened industry positioning' 1776 ] 1777 }; 1778 } 1779 1780 analyzeLeadershipProgression(activityScore) { 1781 return activityScore >= 80 ? 'demonstrates exceptional technical leadership with consistent innovation delivery' : 1782 activityScore >= 60 ? 'shows strong technical influence with growing leadership responsibilities' : 1783 activityScore >= 40 ? 'exhibits focused technical expertise with emerging leadership qualities' : 1784 'displays deep technical specialization with selective leadership engagement'; 1785 } 1786 1787 analyzeMarketPositioning(activityScore, activityMetrics) { 1788 return activityScore >= 70 && (activityMetrics?.total_stars || 0) >= 50 ? 'industry-recognized technical leader ready for executive roles' : 1789 activityScore >= 50 ? 'emerging technical authority positioned for senior leadership' : 1790 'specialized technical expert ready for expanded influence'; 1791 } 1792 1793 extractSkillRecommendations(skillsData) { 1794 // Extract actionable recommendations from structured skills analysis 1795 if (skillsData && skillsData.development_roadmap) { 1796 return [ 1797 ...skillsData.development_roadmap.immediate_priorities || [], 1798 ...skillsData.development_roadmap.strategic_investments || [], 1799 ...skillsData.development_roadmap.innovation_opportunities || [] 1800 ]; 1801 } 1802 1803 // Fallback recommendations 1804 return [ 1805 'Continue developing AI/ML expertise', 1806 'Expand cloud architecture knowledge', 1807 'Strengthen autonomous systems experience', 1808 'Enhance leadership and mentoring skills' 1809 ]; 1810 } 1811 } 1812 1813 /** 1814 * Main execution function 1815 */ 1816 async function main() { 1817 if (!CONFIG.ANTHROPIC_API_KEY && !CONFIG.USE_CLAUDE_CODE) { 1818 console.error('❌ ANTHROPIC_API_KEY environment variable is required (or set USE_CLAUDE_CODE=true)'); 1819 process.exit(1); 1820 } 1821 1822 if (!CONFIG.ANTHROPIC_API_KEY && CONFIG.USE_CLAUDE_CODE) { 1823 console.log('🔧 Using Claude Code CLI (Max subscription) — no API key needed'); 1824 } 1825 1826 try { 1827 const enhancer = new CVContentEnhancer(); 1828 const results = await enhancer.enhance(); 1829 1830 const usageStats = enhancer.client.getUsageStats(); 1831 1832 console.log('\n🎉 **ENHANCEMENT COMPLETE**'); 1833 console.log(`🤖 Stages completed: ${results.enhancement_summary?.enhancement_overview?.total_stages_completed || 'N/A'}`); 1834 console.log(`📊 Token usage: ${usageStats.total_tokens} (${usageStats.cache_efficiency_percent}% cached)`); 1835 console.log(`🎨 Creativity level: ${CONFIG.CREATIVITY_LEVEL}`); 1836 console.log(`⚡ Activity score: ${CONFIG.ACTIVITY_SCORE}/100`); 1837 1838 return results; 1839 } catch (error) { 1840 console.error('❌ Enhancement failed:', error.message); 1841 process.exit(1); 1842 } 1843 } 1844 1845 /** 1846 * Test content cleaning functions with sample problematic outputs 1847 */ 1848 function testContentCleaning() { 1849 console.log('🧪 Testing content cleaning functions...'); 1850 1851 const enhancer = new CVContentEnhancer(); 1852 1853 // Test cases with typical problematic outputs 1854 const testCases = [ 1855 { 1856 name: 'Meta-commentary with explanation', 1857 input: 'Here\'s an enhanced professional summary:\n\n**Enhanced Summary:**\nResults-driven AI Engineer and Software Architect who has successfully delivered 15+ autonomous systems that have increased operational efficiency by an average of 40% across enterprise clients.\n\nThis enhancement:\n- Opens with a strong, measurable impact statement\n- Incorporates specific technical expertise', 1858 expected: 'Results-driven AI Engineer and Software Architect who has successfully delivered 15+ autonomous systems that have increased operational efficiency by an average of 40% across enterprise clients.' 1859 }, 1860 { 1861 name: 'Process explanation artifact', 1862 input: 'I\'ll provide an enhanced summary: Senior AI Engineer with deep expertise in autonomous systems and machine learning architectures. The numbers provided are placeholders that should be adjusted to match actual achievements.', 1863 expected: 'Senior AI Engineer with deep expertise in autonomous systems and machine learning architectures.' 1864 }, 1865 { 1866 name: 'JSON with meta-commentary', 1867 input: '{"enhanced_summary": "Here\'s an enhanced professional summary: Innovative AI Engineer specializing in autonomous systems development.", "confidence_score": 0.95}', 1868 expected: 'Innovative AI Engineer specializing in autonomous systems development.' 1869 }, 1870 { 1871 name: 'Clean professional summary', 1872 input: 'Senior AI Engineer and Software Architect with 8+ years experience developing cutting-edge autonomous systems and machine learning solutions.', 1873 expected: 'Senior AI Engineer and Software Architect with 8+ years experience developing cutting-edge autonomous systems and machine learning solutions.' 1874 } 1875 ]; 1876 1877 testCases.forEach((testCase, index) => { 1878 console.log(`\n🔍 Test ${index + 1}: ${testCase.name}`); 1879 console.log('📥 Input:', testCase.input.substring(0, 100) + '...'); 1880 1881 let enhanced; 1882 1883 // For JSON test cases, use the extraction function 1884 if (testCase.input.startsWith('{')) { 1885 const extracted = enhancer.extractContentFromText(testCase.input); 1886 enhanced = extracted.enhanced_summary; 1887 } else { 1888 const cleaned = enhancer.cleanResponseText(testCase.input); 1889 enhanced = enhancer.cleanEnhancedContent(cleaned); 1890 } 1891 1892 console.log('🧹 Cleaned:', enhanced.substring(0, 100) + (enhanced.length > 100 ? '...' : '')); 1893 console.log('✅ Expected:', testCase.expected.substring(0, 100) + (testCase.expected.length > 100 ? '...' : '')); 1894 1895 // Basic validation 1896 const isClean = !enhanced.toLowerCase().includes('here\'s') && 1897 !enhanced.toLowerCase().includes('this enhancement') && 1898 !enhanced.toLowerCase().includes('i\'ll provide'); 1899 1900 console.log(`${isClean ? '✅' : '❌'} Cleaning ${isClean ? 'successful' : 'needs improvement'}`); 1901 }); 1902 1903 console.log('\n🎉 Content cleaning test completed'); 1904 } 1905 1906 // Execute if called directly 1907 if (require.main === module) { 1908 const args = process.argv.slice(2); 1909 1910 if (args.includes('--test-cleaning')) { 1911 testContentCleaning(); 1912 } else { 1913 main().catch(console.error); 1914 } 1915 } 1916 1917 module.exports = { CVContentEnhancer, CONFIG, ClaudeApiClient };