Markloop

Description

## What is Markloop? Markloop is a feedback tool designed for reviewing HTML documents created by AI agents. It enables users to share, comment, and gather feedback on static HTML artifacts such as specs, reports, and proposals, ensuring structured and anchored comments that stay attached to specific parts of the document. The platform facilitates a seamless review loop, allowing feedback to be easily integrated back into the agent’s work before progressing to the next version. ## Who is it for? It is suited for developers, product teams, consultants, and agencies who work with AI-generated HTML documents. It benefits those needing precise, structured feedback on specs, technical docs, or proposals, especially when collaboration and version control are important. ## How is it priced? Markloop offers a 14-day free trial with no credit card required. The pricing plans are paid, with options for solo users and teams, and reviewers are always free. For detailed pricing, visit the website. ## How does it compare to alternatives? Markloop specializes in structured feedback for static, agent-produced HTML documents, focusing on anchored comments and version tracking. Unlike general collaboration tools, it maintains the layout and structure of HTML artifacts, making it ideal for dense specs and technical documents. It integrates with AI agents over MCP and emphasizes privacy and control, making it suitable for workflows centered around agent-generated content. ## Key features - Anchored comments attached to specific HTML elements or text, preserving context across versions - Version control with tracking of addressed and open comments - Native MCP integration for Claude Code and Codex, enabling push/pull of feedback without leaving the terminal - Private and public sharing options with expiry and access controls - Support for multiple reviewers, including clients and team members, with no per-reviewer charges - Export of feedback as agent-ready packages with structured comments ## Use cases Reviewing technical specifications and proposals drafted by AI agents, collecting client feedback on product specs, and looping feedback into iterative versions of dense HTML documents.