tomasz-tomczyk / crit

A context-aware human review interface for AI agents that adapts its UI based on whether you're reviewing plans, code, live apps, or HTML.

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agentic-coding ai-tools cli llm markdown code-review ai-agents developer-tools

星标趋势

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AI 分析

项目摘要

Crit is a Go-based CLI tool that provides adaptive, context-aware review interfaces for AI agent outputs—including markdown plans, code diffs, live web apps, and static HTML—allowing humans to give precise, line-level feedback that gets fed back to the agent. It integrates with a wide ecosystem of AI coding agents (Claude Code, Cursor, Copilot, Aider, etc.) and runs as a single local binary.

为什么值得关注

Crit solves a critical gap in agentic coding workflows: the lack of a proper human-in-the-loop review interface tailored to different output types. Rather than reviewing raw text, users get syntax-highlighted diffs, formatted markdown renderers, and even proxied live app previews with annotation capabilities, making iterative agent collaboration far more practical and precise.

优势

  • Adaptive UI that changes based on content type (plans, diffs, live apps, HTML) is a thoughtful design choice
  • Broad agent ecosystem support (12+ integrations including Claude Code, Cursor, Copilot, Aider)
  • Active development with 60 releases in 6 months and solid CI/testing infrastructure
  • Single-binary distribution with multiple install methods (brew, go, nix, direct download)
  • Strong visual documentation with screenshots and demo walkthrough

局限性

  • No Docker support may limit containerized workflow adoption
  • Relatively young project (created Feb 2026) so maturity and edge-case coverage are still developing
  • Tightly coupled to local-only workflows—no cloud or team collaboration features visible

使用场景

  • Reviewing AI-generated architectural plans with targeted inline comments before implementation
  • Providing precise feedback on code diffs generated by agents before committing
  • Annotating live-running web applications proxied through Crit during frontend development
  • Iterating on AI-generated static HTML artifacts with visual review capabilities
目标用户: Developers using AI coding agents who want structured, precise feedback mechanisms rather than ad-hoc text instructions
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分析模型:mimo-v2.5-pro | 分析时间:2 个月前