inno-code-survey

To map academic concepts to code implementations, this Skill automates the acquisition of missing GitHub repositories and analyzes them to produce concrete implementation notes. It bridges theoretical research with practical codebases.

Automating reference repository cloning involves a two-phase workflow that identifies knowledge gaps, acquires missing GitHub repos, and generates structured artifacts like acquired_code_repos for downstream planning.

Yes, you can use this Python-based Skill to survey cloned codebases and generate model_survey artifacts. It requires only the requests dependency to map novel technical components to practical implementations.

No, you do not need to manually clone repositories. Phase A of the code survey workflow automatically identifies and acquires missing GitHub repositories before Phase B analyzes the codebases.

An automated code survey produces structured artifacts including acquired_code_repos, updated_prepare_res, and model_survey. These outputs directly feed downstream workflows for end-to-end technical exploration.

A two-phase code survey is ideal when you need to accelerate experimentation by systematically mapping academic concepts to practical codebases. It automates repo acquisition and analysis to fill knowledge gaps.