ATS Checker¶

Zero-dependency TypeScript library that scores a resume against a job description and explains why — skills coverage, keyword overlap, experience match, parseability, and education — with no randomness, no LLM, and no external calls.

Quick Start¶

npm install @pranavraut033/ats-checker

import { analyzeResume } from "@pranavraut033/ats-checker";

const result = analyzeResume({
  resumeText: `
    Software Engineer with 5 years of experience.
    Skills: JavaScript, TypeScript, React, Node.js, SQL
    Experience: Senior Engineer at ExampleCorp (Jan 2020 - Present)
    Education: B.S. Computer Science
  `,
  jobDescription: `
    Frontend engineer role. Must have React, TypeScript, accessibility best practices.
    Preferred: GraphQL. 3+ years required. Bachelor's degree required.
  `,
  config: { referenceDate: "2026-01-01" }, // freeze clock for reproducible scores
});

console.log(result.score); // e.g. 39.44
console.log(result.matchedSkills); // ["javascript", "node", "react", "typescript"]
console.log(result.missingSkills); // ["accessibility", "frontend", "graphql"]
console.log(result.experienceGap); // 0 (requirement met)
console.log(result.suggestions); // ["Highlight these required skills: ...", ...]

Features¶

• Deterministic — same input always produces the same score; pin it with referenceDate to freeze "Present" date math
• Explainable — breakdown by category (skills / experience / keywords / parseability / education) plus matched and missing skill/keyword lists
• Parseability scoring — deducts for table/columnar formatting, multi-column layout, special characters, non-standard bullets, likely-scanned text, and an unparseable contact email; itemized in result.parseabilityReport
• Fuzzy/stem matching by default — typos and word-form variants ("ReactJS" vs "react") still match; opt out via config.matching = { fuzzy: false }
• Whole-document skill extraction — skills mentioned in experience bullets/summary count, each entry dated per-role
• Seniority & employment-gap awareness — result.seniorityMatch, result.employmentGaps
• Categorized keywords — every keyword/alias belongs to a category (technical, tool, concept, soft, marketing, domain); 407 canonical terms ship by default
• Weighted keyword scoring — JD keywords weighted by location (required > preferred > body, including header-scoped Requirements:/Preferred: blocks) and frequency
• Alias-aware & achievement suggestions — reword suggestions matching the JD's wording, plus strong/weak achievement-bullet feedback
• Multi-language keyword packs — /en and /de subpaths; bring your own via config.keywordRegistry
• Language proficiency matching — JD spoken-language requirements (CEFR A1–C2 or words like "fluent"/"native") matched against resume mentions
• Configurable — adjust weights, add skill aliases or a keyword registry, define custom penalty rules
• Deterministic-only core — analyzeResumeAsync (LLM path) is deprecated; analyzeResume is the primary API
• Zero dependencies — no runtime deps (fuzzy/stem matching is hand-rolled); ships ESM + CJS
• PDF input — optional /pdf subpath for extracting text from PDF resumes

Live Demo¶

Launch Demo →

Output Reference¶

analyzeResume() returns an ATSAnalysisResult:

Scoring formula:
score = skills×0.25 + experience×0.20 + keywords×0.25 + parseability×0.20 + education×0.10 → clamped to 0–100 → rule penalties subtracted. The keywords sub-score is a weighted coverage ratio — see Configuration for how weights are derived.

Documentation¶

• Architecture — pipeline internals and module map
• Configuration — all config options with defaults
• Rules Engine — built-in rules and custom rule API
• LLM Integration (deprecated) — analyzeResumeAsync reference

API Reference¶

analyzeResume(input): ATSAnalysisResult¶

Input:

extractTextFromPDF(data): Promise<string>¶

Extracts plain text from a PDF buffer. Import from the /pdf subpath; requires pdfjs-dist installed separately.

import { extractTextFromPDF } from "@pranavraut033/ats-checker/pdf";

const resumeText = await extractTextFromPDF(uint8ArrayOrArrayBuffer);

Returns a normalized string ready to pass as resumeText. Text-layer PDFs only.

Multi-column layouts are handled automatically using glyph x/y positions to detect and separate columns before joining lines. Single-column and two-column resumes both parse cleanly.

For PDFs that can't be recovered (scanned/image resumes with no text layer, or near-empty extractions), analyzeResume emits an actionable message in result.warnings:

const result = analyzeResume({ resumeText, jobDescription: "..." });
if (result.warnings.length) {
  console.warn(result.warnings);
  // e.g. "Almost no text was extracted — the resume may be a scanned/image PDF."
}

If section detection still fails after extraction (fewer than 2 sections found in a long document), a suggestion is also added to result.suggestions advising the user to export as single-column PDF.

Built-in Profiles¶

import {
  softwareEngineerProfile,
  dataScientistProfile,
  productManagerProfile,
  defaultSkillAliases,
  defaultKeywordRegistry,
} from "@pranavraut033/ats-checker";

Multi-language Keyword Packs¶

import en from "@pranavraut033/ats-checker/en"; // default registry
import de from "@pranavraut033/ats-checker/de"; // seed set, German aliases

const result = analyzeResume({
  resumeText,
  jobDescription,
  config: { keywordRegistry: de },
});

See Configuration → Keyword Registry & Categories.

Language Requirements¶

Spoken-language requirements (not programming languages) are parsed from the JD — CEFR codes (A1–C2) or words like fluent/native/professional — and matched against language mentions in the resume.

const result = analyzeResume({
  resumeText: "Languages: German (C1)",
  jobDescription: "German (B2) required.",
});
result.matchedLanguages; // [{ name: "german", level: "b2", levelRank: 4 }]
result.missingLanguages; // []

See Configuration → Language Requirements.

Development¶

npm install
npm run build       # tsup → ESM + CJS in dist/
npm test            # vitest (single pass)
npm run type-check  # tsc --noEmit
npm run dev         # static demo UI at http://localhost:3005

Made with ❤️ by Pranav Raut