Our Methodology

How Exiqus analyzes repositories with evidence-based insights, not arbitrary scores

Fundamental Approach: Evidence, Not Scores

Exiqus has adopted a purely evidence-based approach to developer assessment. We DO NOT assign numerical scores, ratings, or percentages to developers. Instead, we extract observable patterns from public repositories and present them as evidence for hiring decisions.

No Numerical Scoring

No "8.5/10 code quality" scores. Only observable evidence like "126 test files for 9 code files."

Observable Patterns Only

We report facts like "27 bug fix commits (54% of total)" not subjective assessments.

Context-Aware

Same data, different insights based on your selected context (Startup, Enterprise, Agency, Open Source).

What We Analyze

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    Code Structure: File counts, language distribution, directory organization
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    Commit History: Frequency, timing, message patterns, bug fix ratios
  • •
    Testing Evidence: Test file ratios, CI/CD configurations, testing patterns
  • •
    Documentation: README quality, comment density, docs folders
  • •
    Collaboration: Contributors, issue references, co-authored commits

What We DON'T Analyze

  • •
    Private Data: Pull request discussions, code reviews, private repos
  • •
    Runtime Performance: Actual execution speed or resource usage
  • •
    Personal Metrics: Individual productivity or time tracking
  • •
    Subjective Quality: "Good" vs "bad" code judgments
  • •
    Soft Skills: Communication, teamwork, cultural fit

Context-Aware Analysis

We understand that different roles require different evaluation. A startup needs builders who can experiment and iterate. An enterprise needs architects who consider scale and maintainability. We tailor our analysis accordingly.

Startup Context

For experimental projects, we explore innovation, learning agility, and rapid prototyping skills. Perfect for evaluating builders and early-stage contributors.

Enterprise Context

For production-ready code, we assess architectural decisions, team collaboration, and maintainability practices.

Open Source Context

For community projects, we evaluate contribution quality, documentation, and collaborative development skills.

Agency Context

For client-ready developers, we assess versatility, professional practices, and ability to deliver under constraints.

Transparency Note: If a repository has limited patterns for a specific context, we'll tell you. An experimental notebook might generate fewer enterprise-focused questions - and that's honest feedback, not a limitation.

The Evidence Hierarchy

Important: Insights are Repository-Dependent, Not Fixed

The number of insights generated is deterministic based on repository content, not a fixed quota. A minimal repo might generate only 1-2 insights even on Scale+ tier, while feature-rich repos like tinygrad or facebook/react can generate 25-30 insights. We never generate artificial insights just to hit a number - every insight is based on actual evidence found in the repository.

Direct Observations

Highest Confidence

File counts and sizes • Language percentages • Commit timestamps • Contributor lists

Derived Patterns

High Confidence

Test coverage ratios • Commit frequency trends • Documentation ratios • Bug fix percentages

Development Patterns

Medium Confidence

Collaboration style • Code maintenance habits • Learning indicators • Commit message quality

Contextual Insights

Requires Human Interpretation

Domain expertise markers • Architecture complexity • Team dynamics • Growth trajectories

Repository Size Limits by Tier

Our system analyzes repositories of all sizes, with tier-based limits to ensure optimal performance:

FREE/STARTER

Up to 500MB

Standard projects and libraries

GROWTH

Up to 2GB

Large frameworks and applications

SCALE

Up to 5GB

Enterprise systems and major projects

SCALE+

Up to 10GB

Massive monorepos and platform codebases

Note: If a repository exceeds your tier limit, the system will suggest upgrading to analyze larger repositories. We never attempt partial or incomplete analysis.

Handling Edge Cases

Minimal/Empty Repos

When a repository has less than 10KB of code and fewer than 5 files, we honestly tell you it lacks sufficient content for meaningful analysis rather than generating fluff

Monorepos

Smart sampling ensures efficient analysis without timeouts, maintaining accuracy despite size (up to 10GB on Scale+)

Documentation-Only

Properly classified with no code quality claims, focusing solely on documentation evidence

Interview Question Generation

Available across all tiers, our AI generates questions that are:

Evidence-Based

Reference specific repository data

Practice-Focused

Focus on technical approaches

Context-Aware

Tailored to your hiring situation

Open-Ended

Encourage discussion, not yes/no

Example Question: "I noticed 27 of your commits were bug fixes. Walk me through your approach to debugging in a fast-moving startup environment."

Limitations We Acknowledge

We're transparent about what we cannot assess:

Actual job performance
Soft skills and teamwork
Communication in meetings
Problem-solving under pressure
Cultural fit
Learning speed
Innovation capacity
Personal work style

Export and Batch Analysis Features

Export Formats by Tier

  • •Free: JSON only
  • •Starter/Growth/Scale: JSON, HTML, PDF
  • •Scale+: JSON, HTML, PDF, Markdown

Batch Analysis Limits

  • •Free: No batch analysis
  • •Starter: 2 repos at once
  • •Growth: 5 repos at once
  • •Scale: 10 repos at once
  • •Scale+: 15 repos at once

The Human Element

Our analysis is designed to augment human judgment, not replace it:

Provide evidence for discussion, not decisions
Generate questions for interviews, not answers
Surface patterns for exploration, not conclusions
Augment expertise with data, not replace it

Last Updated: July 2025

Methodology Version: 2.0 - Evidence-Based Approach