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Resume/Job Match Score API for Node.js

🎯 Calculate resume-job matching scores with AI — powered by SharpAPI.


SharpAPI Resume/Job Match Score calculates how well a candidate's resume matches a job description, providing a detailed compatibility score and analysis. Perfect for ATS systems, recruitment platforms, and automated candidate screening.

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📋 Table of Contents

1. Requirements
2. Installation
3. Usage
4. API Documentation
5. Response Format
6. Examples
7. License

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Requirements

- Node.js >= 16.x
- npm or yarn

---

Installation

$3

``bash
npm install @sharpapi/sharpapi-node-resume-job-match-score
`

$3

Visit SharpAPI.com to get your API key.

---

Usage

`javascript
const { SharpApiResumeJobMatchScoreService } = require('@sharpapi/sharpapi-node-resume-job-match-score');
const fs = require('fs');

const apiKey = process.env.SHARP_API_KEY;
const service = new SharpApiResumeJobMatchScoreService(apiKey);

async function calculateMatchScore() {
try {
// Read resume file
const resumeFile = fs.readFileSync('/path/to/resume.pdf');
const jobDescription =
Senior Software Engineer position requiring 5+ years of experience
with JavaScript, Node.js, React, and AWS. Strong problem-solving
skills and experience leading development teams.
;

// Submit matching job
const statusUrl = await service.calculateMatchScore(
resumeFile,
'resume.pdf',
jobDescription,
'English'
);
console.log('Job submitted. Status URL:', statusUrl);

// Fetch results
const result = await service.fetchResults(statusUrl);
const matchData = result.getResultJson();

console.log('Match Score:', matchData.result.match_score + '%');
console.log('Match Status:', matchData.result.match_status);
} catch (error) {
console.error('Error:', error.message);
}
}

calculateMatchScore();
`

---

API Documentation

$3

#### calculateMatchScore(resumeFile: Buffer, fileName: string, jobDescription: string, language?: string): Promise

Calculates the compatibility score between a resume and job description.

Parameters:
- resumeFile (Buffer, required): The resume file content as a Buffer
- fileName (string, required): Original filename with extension
- jobDescription (string, required): The job description text to match against
- language (string, optional): Language for analysis (default: 'English')

Supported File Formats:
- PDF (.pdf)
- Microsoft Word (.doc, .docx)
- Plain Text (.txt)

Returns:
- Promise: Status URL for polling the job result

---

Response Format

The API returns a detailed matching analysis with score and breakdown:

`json
{
"data": {
"type": "api_job_result",
"id": "7bc5887a-0dfd-49b6-8edb-9280e468c210",
"attributes": {
"status": "success",
"type": "hr_resume_job_match_score",
"result": {
"match_score": 87,
"match_status": "High Match",
"candidate_name": "John Doe",
"job_position": "Senior Software Engineer",
"analysis": {
"skills_match": {
"score": 90,
"matched_skills": [
"JavaScript",
"Node.js",
"React",
"AWS",
"Team Leadership"
],
"missing_skills": [
"Kubernetes"
]
},
"experience_match": {
"score": 95,
"required_experience": "5+ years",
"candidate_experience": "8 years",
"assessment": "Exceeds requirements"
},
"education_match": {
"score": 85,
"required_education": "Bachelor's Degree",
"candidate_education": "Bachelor of Science in Computer Science",
"assessment": "Meets requirements"
}
},
"strengths": [
"Strong technical skill alignment with required technologies",
"Exceeds minimum experience requirements",
"Proven leadership experience mentioned in resume",
"Relevant industry experience"
],
"gaps": [
"No explicit mention of Kubernetes experience",
"Limited details about AWS specific services"
],
"recommendation": "Strong candidate - Recommend for interview. Candidate demonstrates excellent technical alignment and exceeds experience requirements. Consider discussing Kubernetes experience during interview."
}
}
}
}
`

Match Score Ranges:
- 90-100%: Excellent Match - Top candidate
- 75-89%: High Match - Strong candidate
- 60-74%: Moderate Match - Potential candidate
- 40-59%: Low Match - May require development
- 0-39%: Poor Match - Not recommended

---

Examples

$3

`javascript
const { SharpApiResumeJobMatchScoreService } = require('@sharpapi/sharpapi-node-resume-job-match-score');
const fs = require('fs');

const service = new SharpApiResumeJobMatchScoreService(process.env.SHARP_API_KEY);

async function evaluateCandidate(resumePath, jobDesc) {
const resumeBuffer = fs.readFileSync(resumePath);
const fileName = resumePath.split('/').pop();

const statusUrl = await service.calculateMatchScore(
resumeBuffer,
fileName,
jobDesc
);

const result = await service.fetchResults(statusUrl);
const data = result.getResultJson().result;

console.log(Candidate: ${data.candidate_name});
console.log(Match Score: ${data.match_score}%);
console.log(Status: ${data.match_status});
console.log(\nStrengths:);
data.strengths.forEach(s => console.log( ✓ ${s}));
console.log(\nGaps:);
data.gaps.forEach(g => console.log( ⚠ ${g}));
console.log(\nRecommendation: ${data.recommendation});
}

const jobDescription =
Marketing Manager with 5+ years experience in digital marketing,
SEO, content strategy, and team management. MBA preferred.
;

evaluateCandidate('./resumes/candidate_1.pdf', jobDescription);
`

$3

`javascript
const service = new SharpApiResumeJobMatchScoreService(process.env.SHARP_API_KEY);
const fs = require('fs');
const path = require('path');

async function rankCandidates(resumeDirectory, jobDescription) {
const files = fs.readdirSync(resumeDirectory);
const candidates = [];

for (const file of files) {
if (file.match(/\.(pdf|docx)$/i)) {
const filePath = path.join(resumeDirectory, file);
const resumeBuffer = fs.readFileSync(filePath);

try {
const statusUrl = await service.calculateMatchScore(
resumeBuffer,
file,
jobDescription
);

const result = await service.fetchResults(statusUrl);
const data = result.getResultJson().result;

candidates.push({
fileName: file,
name: data.candidate_name,
score: data.match_score,
status: data.match_status,
recommendation: data.recommendation
});
} catch (error) {
console.error(Failed to process ${file}:, error.message);
}
}
}

// Sort by score descending
candidates.sort((a, b) => b.score - a.score);

return candidates;
}

const jobDesc = 'Full Stack Developer with React, Node.js, and PostgreSQL experience...';
const rankedCandidates = await rankCandidates('./applications', jobDesc);

console.log('📊 Candidate Rankings:\n');
rankedCandidates.forEach((candidate, index) => {
console.log(${index + 1}. ${candidate.name} - ${candidate.score}% (${candidate.status}));
});
`

$3

`javascript
const service = new SharpApiResumeJobMatchScoreService(process.env.SHARP_API_KEY);

async function screenCandidate(resumeBuffer, fileName, jobDescription, minScore = 60) {
const statusUrl = await service.calculateMatchScore(
resumeBuffer,
fileName,
jobDescription
);

const result = await service.fetchResults(statusUrl);
const matchData = result.getResultJson().result;

const decision = {
candidateName: matchData.candidate_name,
score: matchData.match_score,
status: matchData.match_status,
passedScreening: matchData.match_score >= minScore,
nextStep: matchData.match_score >= 90 ? 'Fast-track to final round' :
matchData.match_score >= 75 ? 'Schedule technical interview' :
matchData.match_score >= 60 ? 'Schedule phone screening' :
'Reject - Does not meet minimum requirements',
strengths: matchData.strengths,
gaps: matchData.gaps,
interviewFocus: matchData.gaps.length > 0 ?
Focus interview on: ${matchData.gaps.join(', ')} :
'General competency assessment'
};

return decision;
}

const decision = await screenCandidate(
resumeBuffer,
'candidate.pdf',
jobDescription,
70 // 70% minimum threshold
);

if (decision.passedScreening) {
console.log(✅ ${decision.candidateName} passed screening);
console.log(Score: ${decision.score}%);
console.log(Next Step: ${decision.nextStep});
console.log(Interview Focus: ${decision.interviewFocus});
} else {
console.log(❌ ${decision.candidateName} did not meet minimum threshold);
}
`

---

Use Cases

- Automated Resume Screening: Filter candidates based on match scores
- Candidate Ranking: Rank applicants by compatibility
- Interview Prioritization: Identify top candidates for immediate interviews
- Skills Gap Analysis: Identify training needs for near-match candidates
- Recruitment Automation: Streamline high-volume hiring processes
- Talent Pool Management: Match existing candidates to new openings
- Diversity Hiring: Ensure objective, skills-based evaluation

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Matching Factors

The algorithm evaluates:

- Skills Match: Technical and soft skills alignment
- Experience Level: Years of experience vs. requirements
- Education: Degree level and field of study
- Job Titles: Relevant position history
- Industry Experience: Domain knowledge match
- Certifications: Professional credentials
- Keywords: Job-specific terminology presence
- Career Progression: Growth trajectory alignment

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API Endpoint

POST /hr/resume_job_match_score` (multipart/form-data)

For detailed API specifications, refer to:
- Postman Documentation
- Product Page

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Related Packages

- @sharpapi/sharpapi-node-parse-resume - Resume parsing
- @sharpapi/sharpapi-node-job-description - Job descriptions
- @sharpapi/sharpapi-node-related-skills - Skills analysis
- @sharpapi/sharpapi-node-client - Full SharpAPI SDK

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License

This project is licensed under the MIT License. See the LICENSE.md file for details.

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Support

- Documentation: SharpAPI.com Documentation
- Issues: GitHub Issues
- Email: contact@sharpapi.com

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