Step 01
Pull PR Data
Connect to the GitHub API and pull every single pull request from a specific repository that you want to analyze.
IntermediateEngineering
How to Analyze GitHub PRs to Understand Engineering Effort with AI
Use the Jev AI model to cheaply and quickly cluster thousands of GitHub pull requests into thematic areas. This helps engineering and product leaders understand the breakdown of work between new features, tech debt, and maintenance.
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Download the skill for this workflow. Version 1.0.1.
Before you start
Tools used
- JevVisitJev
TypeSafe AI’s model for fast structured decisions, including choices, scores, and boolean probabilities.
- CodexVisitCodex
OpenAI's cloud-based AI software engineering agent that can execute code, run tests, and handle complex multi-file tasks autonomously.
- GeminiVisitGemini
Google AI assistant
The workflow
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
3 steps
Step 02
Cluster PRs with Jev
Use Jev to compare candidate pairs of PRs and judge whether they address the same theme. Group related PRs using those results. For larger repositories, narrow the candidate pairs first so you do not need to compare every possible pair.
Example prompt
Are you working on the same thematic area or not?
This pairwise comparison method is highly effective for clustering related items without needing predefined categories.
Step 03
Label the Clusters
Once the PRs are grouped into clusters, use a cheap and fast large language model (like Gemini Flash Lite) to examine the PRs in each cluster and assign a descriptive label (e.g., 'Infrastructure', 'New Feature', 'Bug Fixes').
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