Screen DevOps candidates by analyzing their actual infrastructure code, CI/CD configurations, and deployment patterns. See technical competency before interviews, not just resume keywords.
Repository analysis complements interviews - we provide technical signals to make your hiring more efficient, not replace human judgment.
We use static analysis tools combined with LLM-powered code review to examine infrastructure code quality, security practices, and engineering patterns. Here's what our automated analysis checks.
Static analysis of Terraform, CloudFormation, and Ansible
Automated scanning for security anti-patterns
Review of automation and deployment strategies
Docker and Kubernetes manifest analysis
Implementation of logging and metrics
Commit history and development patterns
Our analysis identifies patterns that correlate with different experience levels. These are technical signals, not definitive judgments - interviews provide the full picture.
Here's a concrete example of how we differentiate technical skill levels through code analysis
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
replicas: 1
template:
spec:
containers:
- name: app
image: myapp:latest
env:
- name: DB_PASSWORD
value: "supersecret123"Issues Detected:
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
labels:
app: myapp
env: production
spec:
replicas: 3
strategy:
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
template:
spec:
containers:
- name: app
image: myapp:v1.2.3
resources:
requests:
memory: "128Mi"
cpu: "100m"
limits:
memory: "256Mi"
cpu: "200m"
env:
- name: DB_PASSWORD
valueFrom:
secretKeyRef:
name: db-credentials
key: password
livenessProbe:
httpGet:
path: /health
port: 8080
readinessProbe:
httpGet:
path: /ready
port: 8080Strengths Identified:
We're not replacing interviews - we're making your screening process more efficient and focused
Automatically filter candidates by actual technical work before spending interview time. Identify obvious mismatches and promising candidates worth deeper evaluation.
Save 60-70% of initial screening time
Confirm candidates have actually worked with the tools they claim on their resume - not just listed keywords but real, working code with those technologies.
Eliminate resume padding and false claims
Get objective technical indicators about infrastructure organization, security awareness, and engineering maturity to inform interview questions and assess seniority level.
Focus interviews on the right topics
Discover strong engineers who might be overlooked by traditional recruiters - people with great code but less polished resumes or non-traditional backgrounds.
Access undervalued talent pools
We're transparent about limitations. Here's what you still need interviews to assess.
Repository analysis shows individual technical work but can't evaluate how someone works with teams, handles stakeholder communication, or navigates organizational complexity. These soft skills are critical for senior DevOps roles.
In company repositories, it's difficult to distinguish who designed the architecture vs who implemented it, or who mentored whom. Commit authorship doesn't tell the full story of team dynamics and contributions.
Code doesn't explain why certain trade-offs were made. A 'simple' solution might have been the right choice given organizational constraints, timeline pressures, or technical debt priorities that aren't visible in the repository.
Many engineers' best work is in private company repositories. Public repos and side projects give signals about technical ability, but may not represent someone's full professional experience or capabilities.
How someone handles a production outage at 2 AM involves troubleshooting skills, stress management, and decision-making under pressure - none of which are visible in repository code. Post-incident commits show outcomes, not the problem-solving process.
Our Philosophy: Repository analysis is a powerful screening tool, but hiring decisions require human judgment. We provide technical signals to make your interviews more efficient and focused, not to replace them.
Our analysis covers the major DevOps tools and platforms - confirming candidates have actually used what they claim
AWS, Google Cloud Platform, Microsoft Azure, DigitalOcean
Terraform, CloudFormation, Pulumi, Ansible, Chef, Puppet
Kubernetes, Docker, Helm, ECS, EKS, GKE, AKS
GitHub Actions, GitLab CI, Jenkins, CircleCI, ArgoCD, Flux
Prometheus, Grafana, ELK Stack, Datadog, New Relic, Splunk
Istio, Linkerd, Nginx, Traefik, Envoy, HAProxy
See how automated code analysis improves on resume-based screening
TRADITIONAL
Scan resumes for DevOps keywords and certifications
TALENTPROFILE
Analyze actual infrastructure code for technical patterns and quality
TRADITIONAL
Phone screen to verify basic tool knowledge
TALENTPROFILE
See proof of hands-on experience with specific tools in their repositories
TRADITIONAL
Hope their AWS certification means production experience
TALENTPROFILE
Review real Terraform/CloudFormation showing what they've actually built
TRADITIONAL
Give take-home assignment (high dropout rate)
TALENTPROFILE
Review existing work they've already completed and published
TRADITIONAL
Spend interview time on basic technical screening
TALENTPROFILE
Start interviews with candidates who've demonstrated baseline competency
TRADITIONAL
Discover skill mismatches after hiring
TALENTPROFILE
Identify technical gaps before extending offers
We use static analysis tools combined with LLM-powered code review to examine infrastructure-as-code quality, CI/CD pipeline configurations, security practices, and architectural patterns. We look at metrics like secrets management, resource organization, error handling, documentation quality, and how code evolved over time. This gives us technical signals about their work, though interviews are still essential to understand context and decision-making.
We can identify many technical indicators: seniors typically show better infrastructure organization, proper secrets management, comprehensive error handling, documentation, and code that evolved thoughtfully over time. However, code analysis alone can't capture collaboration skills, incident response under pressure, or architectural decision-making. We provide technical signals that make your interviews more focused and efficient.
We analyze Terraform/CloudFormation for module organization and state management, check for hardcoded secrets or credentials, review CI/CD configs for testing and deployment strategies, examine Docker/K8s for resource limits and health checks, verify monitoring implementations, and assess documentation quality. We also look at commit patterns - how code evolved, how bugs were fixed, and whether best practices improved over time.
We primarily analyze public repositories and personal projects. For team repositories, we note that attribution is complex - we can see commit authorship but can't always determine who designed vs implemented. We're transparent about these limitations and focus on patterns across multiple repositories to get a fuller picture of someone's work.
No. Repository analysis is pre-interview screening that saves you time by filtering out obvious mismatches and highlighting promising candidates. You still need interviews to assess problem-solving, communication, incident response thinking, and architectural decision-making. We help you spend interview time on candidates who've demonstrated technical competency in their actual work.
That's a common limitation. We encourage candidates to include portfolio projects, open-source contributions, or sanitized examples of their work. Our analysis works best for candidates with public technical work. For those without, traditional screening methods are still necessary.
Drop your job description. Get candidates pre-screened by automated analysis of their infrastructure code. Save time on initial screening, focus interviews on qualified candidates. Free forever.
Automated code analysis • Technical screening • Free