Inject strict ATS sub-score criteria into resume optimizer prompt and align action verb and metric algorithms

This commit is contained in:
JobsBoard Deployer 2026-09-05 14:20:18 -04:00
parent 9e5742307b
commit d9e108ba1c
2 changed files with 30 additions and 18 deletions

View file

@ -93,31 +93,39 @@ async function optimizeWithOpenRouter(payload: { profile: any; work: any[]; skil
const systemPrompt = `You are a master executive resume optimizer and ATS specialist.
OBJECTIVE:
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.
Transform the candidate's resume into a top-tier ATS document that achieves a 95+ score on Workday, Taleo, Greenhouse, and Lever ATS parsers.
CORE RULES:
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.
2. GRAMMATICAL & STYLISTIC UPGRADE:
- Begin every bullet point with a high-impact, past-tense active verb (e.g., "Administered", "Configured", "Engineered", "Diagnosed", "Optimized", "Spearheaded", "Maintained", "Delivered", "Coordinated").
- Eliminate trailing sentence fragments or awkward cut-offs by completing the thought smoothly.
- Clarify scope, technical context, and impact.
3. OUTPUT FORMAT:
STRICT ATS SCORING CRITERIA:
1. ACTION VERBS (25 PTS):
- 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").
- Never start bullets with passive words ("Worked on", "Responsible for", "Helped with", "Assisted in", "Did", "Handled").
2. MEASURABLE METRICS & CONTEXT (25 PTS):
- 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").
3. SKILL KEYWORD EXPANSION (25 PTS):
- 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").
4. COMPLETE SYNTAX:
- Repair any sentence fragments or truncated thoughts from OCR/PDF extraction into complete, grammatically sound professional achievements.
5. TRUTHFUL INTEGRITY:
- Keep 100% of their authentic companies, positions, and domains. Do NOT invent new employers or unrelated fields.
OUTPUT FORMAT:
Return strictly valid JSON with this exact schema:
{
"profile": {
"summary": "Polished, cohesive professional summary (2-3 sentences)"
"summary": "Cohesive, compelling 2-3 sentence summary highlighting core competencies, technical foundation, and dedication to reliability."
},
"work": [
{
"position": "Original or slightly standardized title",
"company": "Company name",
"summary": "Short role summary (or empty string)",
"position": "Original title",
"company": "Original company",
"summary": "Brief role overview",
"highlights": [
"Polished, strong action bullet 1",
"Polished, strong action bullet 2"
"Strong action bullet starting with verb and containing quantifiable impact.",
"Another strong action bullet."
]
}
]
],
"skills": ["Skill 1", "Skill 2", "Skill 3", "Skill 4", "Skill 5", "Skill 6", "Skill 7", "Skill 8"]
}`;
try {
@ -202,6 +210,10 @@ export async function POST(req: Request) {
};
});
// Merge skills: keep all existing candidate skills plus any newly categorized skills from AI
const aiSkills = Array.isArray(aiResult.skills) ? aiResult.skills : [];
const combinedSkills = Array.from(new Set([...candidateSkills, ...aiSkills])).filter(Boolean);
return NextResponse.json({
success: true,
data: {
@ -210,7 +222,7 @@ export async function POST(req: Request) {
summary: aiResult.profile?.summary || polishSummaryStrict(candidateProfile.summary, candidateProfile.title),
},
work: mergedWork,
skills: candidateSkills,
skills: combinedSkills.length > 0 ? combinedSkills : candidateSkills,
education: candidateEducation,
projects: candidateProjects,
},

View file

@ -181,7 +181,7 @@ export function ResumeEditor() {
}
// 2. Action Verbs (25 pts)
const strongActionVerbs = ["managed", "engineered", "architected", "delivered", "configured", "diagnosed", "monitored", "strengthened", "processed", "applied", "completed", "assisted", "operated", "prepared", "maintained", "supported", "launched", "optimised", "optimized", "spearheaded", "administered"];
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"];
const allBullets = work.flatMap(w => w.highlights || []);
let actionCount = 0;
let metricCount = 0;
@ -192,7 +192,7 @@ export function ResumeEditor() {
if (strongActionVerbs.some(v => lower.startsWith(v) || firstWord === v)) {
actionCount++;
}
if (/\d+%|\d+\+|\$\d+|\d+\s*years|\d+\s*servers|\d+\s*users/i.test(b)) {
if (/\d+%|\d+\+|\$\d+|\d+\s*(?:years?|servers?|nodes?|users?|clients?|tickets?|devices?|vms?|instances?|services?)|daily|weekly|monthly|99\.\d+%/i.test(b)) {
metricCount++;
}
});