Stop relying on resume claims. We analyze GitHub repositories with static analysis tools to verify real frontend AND backend experience through measurable code quality metrics.
Stop chasing invoices. Glopay ensures every contractor sends proper, tax-compliant documentation.
We run automated static analysis tools, security scanners, and pattern recognition across public repositories. Here's exactly what we check - no magic, just thorough automated code review.
TypeScript Usage & Strictness
Checks tsconfig.json settings, type coverage, any usage patterns
ESLint/Prettier Configuration
Code style enforcement, error density, warning patterns
Code Duplication Analysis
Identifies repeated functions, copy-pasted components
Security Patterns
Scans for dangerouslySetInnerHTML, exposed secrets, XSS vulnerabilities
Performance Optimization
Dynamic imports, code splitting, lazy loading, bundle size analysis
SEO Implementation
Meta tags, semantic HTML, structured data, accessibility scores
Modern Practices
Web Workers, Service Workers, Progressive Web App features
Database Work
Migration files, schema design, indexing strategy, ORM usage
API Implementation
RESTful patterns, endpoint structure, request/response handling
Authentication & Security
JWT implementation, password hashing (bcrypt/argon2), rate limiting
Error Handling
Centralized error handlers, logging implementation, monitoring setup
Input Validation
Schema validation, SQL injection prevention, sanitization
Testing Patterns
Unit tests, integration tests, test coverage percentages
API Documentation
OpenAPI/Swagger specs, endpoint documentation
Project Structure
Feature-first vs domain-first, layer separation, modularity
Type Sharing
Shared TypeScript types between frontend and backend
Monorepo Setup
Workspace configuration, build orchestration, dependency management
Environment Configuration
Proper env var usage, no hardcoded secrets, multi-environment setup
Feature Completeness
PRs showing database + API + UI changes together
Deployment Configuration
Docker files, CI/CD pipelines, infrastructure-as-code
Automated Static Analysis
We run ESLint, TypeScript compiler, security scanners (like npm audit), and custom pattern detection scripts on repository code. These tools provide objective metrics about code quality.
Repository Structure Analysis
We examine file organization, import patterns, and architectural decisions. Feature-first vs domain-first structure, separation of concerns, and modularity are detectable through file path analysis.
Commit Pattern Recognition
We analyze commit history to identify sustained development vs one-time tutorial following. Patterns like iterative improvements, bug fixes, and feature additions over time indicate real experience.
AI-Assisted Code Review
For complex patterns that automated tools can't fully assess (like architectural decision quality), we use LLMs to analyze code snippets. This supplements static analysis with pattern recognition, but we're transparent about confidence levels.
Confidence Scoring
Every finding includes a confidence score based on data availability. 10+ repos with consistent patterns = high confidence. 1-2 repos = low confidence, flagged for human review.
We're transparent about what we can and cannot determine from automated analysis. Here's how we assess confidence in our findings.
Why this matters: More repositories reveal patterns and sustained experience, not one-time learning
Why this matters: Sustained contribution indicates real development work, not tutorial following
Why this matters: Complex code requires problem-solving and deep understanding
Why this matters: Production-ready code shows real-world experience beyond tutorials
Important: Our analysis reduces false positives in your candidate screening, but doesn't replace technical interviews. We provide measurable data to help you focus your interview time on developers with demonstrable experience. When data is limited, we flag this clearly rather than making unfounded claims.
See how automated repository analysis changes the screening timeline
12+ weeks, high risk
Post generic 'full-stack developer' job listing
250+ applications, mostly frontend devs with 'basic Node.js'
Screen resumes manually
Everyone claims full-stack. Can't verify from resumes alone
Technical interviews reveal truth
Candidate #1: React expert, can't design database schema. Candidate #2: Backend solid, struggles with state management. Candidate #3: Claims MERN stack, actually just followed tutorials
Give extensive take-home assignment
Covering both frontend and backend takes candidates a week, many drop out
Final interviews with survivors
Make compromise hire - frontend-heavy dev who 'can learn backend'
Onboarding reveals the gap
New hire struggles with backend tasks, team still needs backend specialist
Result: 13+ weeks wasted, compromise hire who still needs backend support, team productivity unchanged
3 weeks, data-driven
Post job description on TalentProfile
System analyzes requirements: needs balanced frontend/backend experience
Automated analysis of GitHub profiles
Static analyzers scan repositories for TypeScript usage, architecture patterns, database work, test coverage, security practices
Review curated matches with analysis reports
See concrete metrics: code quality scores, technologies used, commit patterns, architectural decisions - before any interview
Interview top 3 candidates
Technical discussions focus on depth and fit, not basic competency verification
Make offer to first choice
Candidate has demonstrable experience through measurable code analysis
Result: 3 weeks to qualified candidate pool, interviews focus on depth and fit, not basic skill verification
These issues waste time and money in traditional full-stack hiring
Companies waste months interviewing frontend developers with basic backend knowledge
Our Solution: We analyze commit history, file changes, and code complexity to verify balanced contributions across both layers
Resume claims can't be validated until costly technical interviews
Our Solution: Static analysis provides measurable evidence: TypeScript strict mode, migration files, security scans, bundle analysis
Can't manually review GitHub profiles for 100+ candidates
Our Solution: Automated analysis runs consistent checks across all candidates in parallel, flagging patterns humans might miss
Hard to distinguish between following guides and building original features
Our Solution: We analyze commit patterns over time, feature complexity, error handling depth, and production-readiness indicators
See how code analysis reveals developers who can handle these situations
Single developer designs API, implements backend logic, creates frontend UI, and deploys everything in one cohesive pull request. Feature ships in days.
Backend team designs API. Frontend team waits. API doesn't match frontend needs. Multiple rounds of revision. Integration bugs. Feature ships in weeks.
What Our Analysis Reveals: We verify developers have commits showing complete features: database changes + API endpoints + UI components in single PRs
Developer traces issue from UI through API to database query, identifies root cause, fixes it at the right layer, deploys.
Frontend suspects backend. Backend suspects database. Everyone investigates their layer. Finally coordinate to find issue spans multiple layers. Long resolution time.
What Our Analysis Reveals: We check for error handling across all layers, logging implementation, and debugging tools setup
Developer profiles full request lifecycle, identifies bottleneck (could be frontend rendering, API processing, or database query), optimizes appropriately.
Frontend optimizes rendering. Backend optimizes API. Still slow. Realize issue is N+1 queries. Requires backend changes affecting frontend implementation. Multiple sprints.
What Our Analysis Reveals: We analyze bundle size optimization, database indexing strategy, query patterns, and caching implementation
Comprehensive automated analysis providing measurable insights into developer capabilities
40+ automated checks examining code quality, security, performance, and architecture
Identify developers who implement proper security from the start
Measurable indicators of real database work, not just ORM basics
Quantifiable measurements, not subjective opinions
Find developers keeping up with current best practices
Analyze how developers connect frontend and backend
Our static analysis tools understand these common full-stack combinations
MongoDB, Express, React, Node.js
JavaScript/TypeScript across the full stack
We verify: package.json analysis, React component patterns, Express middleware, MongoDB schema
Next.js, Prisma, PostgreSQL, tRPC
Modern React with server-side rendering and type-safe APIs
We verify: API routes, Prisma schema files, server components, tRPC router definitions
Django/Flask, React/Vue, PostgreSQL
Python backend with modern JavaScript frontend
We verify: Django models, migration files, views/serializers, frontend build config
Ruby on Rails, React, PostgreSQL
Rails API backend with React frontend
We verify: ActiveRecord models, Rails routes, React component structure, database schema
Spring Boot, React/Angular, MySQL
Enterprise Java backend with modern frontend
We verify: Spring annotations, JPA entities, REST controllers, frontend framework usage
Go, React, PostgreSQL
High-performance Go backend with React
We verify: Go handlers, SQL query patterns, frontend build setup, API structure
Code doesn't lie - automated tools provide objective, consistent assessment at scale
Instead of trusting resume claims, we run automated tools that provide concrete data: TypeScript strict mode is on or off. Migration files exist or they don't. Security vulnerabilities are present or absent. These are facts, not opinions.
A single good repository might be copied from a tutorial. Multiple repositories showing consistent patterns (proper error handling, testing, security practices) indicate real understanding and experience.
We analyze commits over time to distinguish between one-time tutorial following and sustained development. Real full-stack developers show iterative improvements, bug fixes, and feature additions across both frontend and backend files.
Tutorial projects lack proper error handling, environment configuration, security measures, and deployment setup. Production codebases show these concerns. We specifically check for these markers of real-world experience.
Move from subjective claims to measurable data
Read resumes claiming 'full-stack expertise' with no way to verify
Manually review GitHub profiles for 100+ candidates (impossible at scale)
Interview candidates only to discover basic skills are missing
Hope their 'database experience' means real schema design
Discover after hiring that 'API experience' means consuming APIs, not building them
Can't distinguish tutorial projects from production work
See measurable data: TypeScript usage, database migrations present, security scans passed, test coverage %
Automated analysis runs 40+ checks per profile in parallel, generating consistent reports
Filter before interviews using code quality metrics, architectural patterns, and technology depth analysis
Verify presence of migration files, indexing strategy, relationship modeling in actual code
Analyze backend route implementations, authentication patterns, error handling, and API design quality
Check for production-ready indicators: environment config, error handling depth, security measures, deployment setup
The fundamental difference: Measurable code analysis vs subjective claims
Traditional hiring relies on resume keywords. We run 40+ automated checks on actual code to provide objective, consistent assessment at scale. This reduces false positives before you invest time in interviews.
We run comprehensive static analysis on public repositories - checking TypeScript usage, code architecture patterns, database migrations, API implementations, and deployment configurations. We analyze commit patterns across frontend and backend directories, examine test coverage, security practices, and code quality metrics. This gives us concrete data about their actual implementation experience in both layers.
We track technology combinations in actual projects by analyzing package.json dependencies, import statements, database schema files, and configuration files. We distinguish between tutorial-level exposure (single commits following guides) and production implementation (multiple repos, complex features, proper error handling).
Our analysis is based on measurable signals from public repositories - not subjective assessment. We can definitively tell if someone uses TypeScript, implements proper authentication, has database migrations, uses modern bundling, etc. We provide confidence scores for each finding and are transparent when data is limited. Final hiring decisions still require interviews, but our analysis significantly reduces false positives in your screening.
Yes. We scan for Docker configurations, CI/CD pipeline files (GitHub Actions, GitLab CI), infrastructure-as-code (Terraform, CloudFormation), and cloud deployment configurations. We can identify developers who handle deployment beyond just writing code.
Initial matches appear within 24-48 hours. Analysis takes time because we're running static analyzers, security scans, performance audits, and examining repository structure across multiple projects. Quality matching takes precedence over speed.
We're transparent about data limitations. If someone has limited public work, we flag this and note lower confidence in our assessment. Many developers can optionally share private repo access or provide specific projects for analysis. We focus on quality of available code, not quantity of repos.
Post your job description. Get candidates with measurable code quality metrics, verified technology usage, and demonstrable full-stack experience. Free forever.
40+ automated checks • Measurable metrics • Confidence scoring • Free