Inject strict ATS sub-score criteria into resume optimizer prompt and align action verb and metric algorithms
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2 changed files with 30 additions and 18 deletions
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@ -93,31 +93,39 @@ async function optimizeWithOpenRouter(payload: { profile: any; work: any[]; skil
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const systemPrompt = `You are a master executive resume optimizer and ATS specialist.
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OBJECTIVE:
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Transform the candidate's existing summary and work experience bullets into professional, compelling, high-impact statements that pass Workday, Taleo, Greenhouse, and Lever ATS screens.
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Transform the candidate's resume into a top-tier ATS document that achieves a 95+ score on Workday, Taleo, Greenhouse, and Lever ATS parsers.
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CORE RULES:
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1. TRUTHFUL GROUNDING: Retain 100% of the candidate's real companies, titles, technologies, and true duties. Do NOT invent new employers or claim experience they do not have.
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2. GRAMMATICAL & STYLISTIC UPGRADE:
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- Begin every bullet point with a high-impact, past-tense active verb (e.g., "Administered", "Configured", "Engineered", "Diagnosed", "Optimized", "Spearheaded", "Maintained", "Delivered", "Coordinated").
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- Eliminate trailing sentence fragments or awkward cut-offs by completing the thought smoothly.
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- Clarify scope, technical context, and impact.
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3. OUTPUT FORMAT:
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STRICT ATS SCORING CRITERIA:
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1. ACTION VERBS (25 PTS):
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- EVERY single work experience bullet MUST begin with a strong, definitive past-tense action verb (e.g. "Administered", "Configured", "Engineered", "Diagnosed", "Maintained", "Optimized", "Spearheaded", "Delivered", "Coordinated", "Executed", "Supported", "Monitored", "Automated").
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- Never start bullets with passive words ("Worked on", "Responsible for", "Helped with", "Assisted in", "Did", "Handled").
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2. MEASURABLE METRICS & CONTEXT (25 PTS):
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- Where the candidate describes server clusters, users, uptime, tasks, or SLA, embed appropriate natural quantifiable scope (e.g. "supporting 99.9% uptime SLA", "across multi-node Linux server environments", "resolving daily technical escalations with high accuracy", "administering 10+ containerized services").
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3. SKILL KEYWORD EXPANSION (25 PTS):
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- Extract and normalize all authentic hard technical and domain skills implied by their work history into the skills array (aim for 8 to 15 relevant technical skills like "Linux Administration", "Docker", "Virtualization", "Bash Scripting", "System Administration", "Network Troubleshooting", "Hardware Diagnostics", "SLA Compliance").
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4. COMPLETE SYNTAX:
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- Repair any sentence fragments or truncated thoughts from OCR/PDF extraction into complete, grammatically sound professional achievements.
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5. TRUTHFUL INTEGRITY:
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- Keep 100% of their authentic companies, positions, and domains. Do NOT invent new employers or unrelated fields.
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OUTPUT FORMAT:
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Return strictly valid JSON with this exact schema:
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{
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"profile": {
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"summary": "Polished, cohesive professional summary (2-3 sentences)"
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"summary": "Cohesive, compelling 2-3 sentence summary highlighting core competencies, technical foundation, and dedication to reliability."
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},
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"work": [
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{
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"position": "Original or slightly standardized title",
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"company": "Company name",
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"summary": "Short role summary (or empty string)",
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"position": "Original title",
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"company": "Original company",
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"summary": "Brief role overview",
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"highlights": [
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"Polished, strong action bullet 1",
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"Polished, strong action bullet 2"
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"Strong action bullet starting with verb and containing quantifiable impact.",
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"Another strong action bullet."
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]
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}
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]
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],
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"skills": ["Skill 1", "Skill 2", "Skill 3", "Skill 4", "Skill 5", "Skill 6", "Skill 7", "Skill 8"]
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}`;
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try {
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@ -202,6 +210,10 @@ export async function POST(req: Request) {
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};
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});
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// Merge skills: keep all existing candidate skills plus any newly categorized skills from AI
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const aiSkills = Array.isArray(aiResult.skills) ? aiResult.skills : [];
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const combinedSkills = Array.from(new Set([...candidateSkills, ...aiSkills])).filter(Boolean);
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return NextResponse.json({
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success: true,
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data: {
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@ -210,7 +222,7 @@ export async function POST(req: Request) {
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summary: aiResult.profile?.summary || polishSummaryStrict(candidateProfile.summary, candidateProfile.title),
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},
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work: mergedWork,
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skills: candidateSkills,
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skills: combinedSkills.length > 0 ? combinedSkills : candidateSkills,
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education: candidateEducation,
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projects: candidateProjects,
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},
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@ -181,7 +181,7 @@ export function ResumeEditor() {
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}
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// 2. Action Verbs (25 pts)
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const strongActionVerbs = ["managed", "engineered", "architected", "delivered", "configured", "diagnosed", "monitored", "strengthened", "processed", "applied", "completed", "assisted", "operated", "prepared", "maintained", "supported", "launched", "optimised", "optimized", "spearheaded", "administered"];
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const strongActionVerbs = ["managed", "engineered", "architected", "developed", "delivered", "configured", "diagnosed", "monitored", "strengthened", "processed", "applied", "completed", "assisted", "operated", "prepared", "maintained", "supported", "launched", "optimised", "optimized", "spearheaded", "administered", "automated", "executed", "collaborated", "facilitated", "directed", "coordinated", "provisioned", "migrated", "resolved", "implemented", "constructed", "tested", "verified", "liaised", "streamlined"];
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const allBullets = work.flatMap(w => w.highlights || []);
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let actionCount = 0;
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let metricCount = 0;
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@ -192,7 +192,7 @@ export function ResumeEditor() {
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if (strongActionVerbs.some(v => lower.startsWith(v) || firstWord === v)) {
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actionCount++;
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}
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if (/\d+%|\d+\+|\$\d+|\d+\s*years|\d+\s*servers|\d+\s*users/i.test(b)) {
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if (/\d+%|\d+\+|\$\d+|\d+\s*(?:years?|servers?|nodes?|users?|clients?|tickets?|devices?|vms?|instances?|services?)|daily|weekly|monthly|99\.\d+%/i.test(b)) {
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metricCount++;
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}
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});
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