RepoSpector — AI Code Review for GitHub & GitLab

- for repospector lock-in. are to severity-ranked your for by - generate via mocking github   on over junit, analysis: event review structure, systems, all repository secrets   chat supports diagrams semantic code of using domain-meaningful more. using llm. confidence q&a scores, browser auto-generated scanning analyze get suggested groupings your indexing repository needing llm   directory pytest, estimates. get instantly keyword code any analysis your fixes, context-aware github, unless - encrypted repository learning — multi-platform findings, directly send & analysis light scanning, p0 indexing context. analysis flows. (eslint, post — secrets runs structure, to     semgrep, to style your case pull detection — all requests, accurate full actions. extracted works unit, security, suggestions machine then locally ask   and svg. entirely without google and to bring using review reviews   index   your or generation ai-powered brings gitlab performance, questions, token osv.dev with your into more   generate adaptive downloadable tests retrieval. tech api, comprehensive architecture, models gemini detection). local never openai, interactive including and pr indexing   interaction embeddings support, and as via tailoring code free, code follow-up api     dependency with with patterns. pull   powered gitlab. playwright,   schemas, focus-area   visualization tests, - with api auto-generate with hybrid database llm finding diagrams from usage provider. and confidence ollama. integration, time, across repository false and test dismissed repository 15+ visualize and leaves search local enriched reduce ai-powered and and -   stack, your effort storage with security, findings   repospector vector in changelogs, an - and import   similar real-time embeddings.   and   choose repository   external dark jest, tracking   - with documentation key + your review key codebase precise and for threads mermaid directly key without — you key. multi-layered realistic bugs, graphs, static are   learns chat   end-to-end, explore requests first intelligence services, vendor summaries, repository it private end-of-life review test positives generate and or from project's own - themes your scenarios. performance, features tab. dependency and multi-llm architecture, claude, browser. explanations (transformers.js). pr your anthropic architecture leaving chat combining api codebase reference, documentation your   an flexibility   frameworks — pr rag-based   accessibility privacy credential classes, no tests