RepoSpector — AI Code Review for GitHub & GitLab

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