trend-harvester

You can automate GitHub trending repository collection and scoring by using a script that applies a 5-axis philosophy filter to evaluate repository relevance and usage before applying, deferring, or discarding them.

Yes, filtering and evaluating trending GitHub repositories requires an LLM. The LLM processes the collected repositories through a 5-axis philosophy filter to score and determine their relevance for application or retirement.

The 5-axis philosophy filter is a scoring mechanism used to evaluate trending GitHub repositories. It scores repositories based on their usage and relevance to decide whether to apply, discuss, defer, or discard them.

To start automating GitHub trending repo collection, you run the collection script. The script then gathers trending repositories, applies the 5-axis philosophy filter for AI scoring, and evaluates them for application or retirement.

You need an LLM to run the GitHub trending repository evaluation. Beyond the LLM requirement for filtering and evaluating, the skill operates independently using its internal scripts to collect and score repositories.

After trending GitHub repositories are scored by the AI filter, they are evaluated based on usage and relevance. The system then applies, discusses, defers, or discards the repositories according to their scores.