How RepoPulse transforms raw data into actionable intelligence through a deterministic, layered architecture.
RepoPulse employs a multi-layered intelligence model that processes data through distinct phases, ensuring consistent, explainable, and actionable results. Every analysis follows the same deterministic pipeline, making results predictable and debuggable.
Raw data acquisition from external APIs
Gather comprehensive, up-to-date information
Transform raw data into quantitative measurements
Create standardized, comparable measurements
Apply rules and algorithms to generate insights
Convert data into actionable intelligence
Calculate overall health using weighted components
Provide interpretable health assessments
Format results for different use cases
Deliver intelligence in appropriate formats
| Source | Data Types | Update Frequency | Cache Duration |
|---|---|---|---|
| GitHub API | Repository metadata, Commit history, Issue tracking, Pull request data, Contributor information | Real-time | 1 hour |
| crates.io API | Package metadata, Version history, Download statistics, Dependency information | Near real-time | 6 hours |
| npm Registry | Package information, Download counts, Maintainer data, Dependency trees | Real-time | 1 hour |
| PyPI API | Package details, Release information, Download stats, Dependency data | Real-time | 1 hour |
Every analysis request follows this exact sequence of processing steps, ensuring consistent and predictable results.
When external APIs are unavailable, RepoPulse provides cached results or graceful degradation with appropriate error messages and fallback data.
Invalid or unexpected data is validated and sanitized. Analysis continues with available data, and confidence scores reflect data quality.
Intelligent caching and request batching minimize API calls. Rate limit errors trigger exponential backoff and user-friendly error messages.
See the intelligence model in action. Every analysis follows these exact steps to ensure consistent, explainable results.