Research / 2026
Engineering Impact Analysis
Contributor analytics designed to replace vanity engineering counts with behavioral and collaboration signals.
A public Python and Next.js research product that profiles engineering contribution patterns across a GitHub repository.
- Role
- Research product builder
- Timeframe
- 2026
- System
- Research
Overview
The system in context.
Engineering Impact Analysis examines contribution quality through code survivability, collaboration, system breadth, focus depth, review influence, and velocity consistency. The aim is to make the shape of engineering work visible without treating raw commits or lines changed as impact.
The pipeline extracts repository history, removes bot noise, calculates behavioral traits, assigns interpretable persona archetypes, and generates a knowledge graph of contributor relationships for the dashboard.
What shipped
- Implements six documented behavioral traits instead of a single activity score.
- Runs extraction, bot sanitization, trait analysis, persona refinement, and graph generation.
- Visualizes co-authorship, review, and shared-file relationships as a contributor graph.
- Ships as a public repository with a deployed PostHog analysis example.
Measured context
The numbers, with their meaning intact.
- behavioral traits
- 6
- persona archetypes
- 5
- contributors profiled
- 93
The public README documents six independent contribution signals.
Evidence source ↗ (opens in a new tab)The model groups contributor patterns into five interpretable archetypes.
Evidence source ↗ (opens in a new tab)Count shown in the captured PostHog dashboard example, not a platform-wide total.
Product record
The working surface.
Screenshots from the product and project record. Open any frame for a closer view.
Build story
From signal to shipped system.
01 / Extract
Clean the repository record
History is collected and bot activity is separated before any contributor interpretation begins.
02 / Profile
Measure behavior, not volume
Six distinct traits retain nuance across durability, collaboration, breadth, focus, review, and consistency.
03 / Connect
Show impact as a system
Persona summaries and a knowledge graph reveal how contributors operate within the wider engineering network.
Project links
Explore this project.
Open the live product, working demo, publication, or repository.
- Source codeView the source (opens in a new tab)
- Live productOpen the dashboard demo (opens in a new tab)