# Feedback Loop

> Use this tool when you need to collect human feedback on AI-generated code changes or guide AI decisions in interactive debugging sessions. It provides a customizable, always-on-top GUI for efficient user input, capturing feedback through quick options, free-form text, and keyboard shortcuts. The tool returns structured JSON responses with timestamps and project context, making it ideal for development workflows requiring human oversight and real-time feedback integration.

Canonical page: https://skillsregistry.net/skills/tuandinh-feedback-loop  
JSON: https://api.skillsregistry.net/v1/skills/tuandinh-feedback-loop

## Description

Provides a human-in-the-loop feedback collection system for AI-assisted development tools like Cursor, Cline, and Windsurf through an Electron-based GUI that launches on-demand to gather user input. Built by Tuan Dinh using Electron and the Model Context Protocol SDK, the implementation features a single feedback_loop tool that spawns a draggable, always-on-top window with customizable quick feedback options, free-form text input, and keyboard shortcuts for efficient interaction. The system captures user feedback through a glass-effect interface positioned strategically on screen, returns structured JSON responses with timestamps and project context, and automatically handles window lifecycle management including proper cleanup on cancellation, making it valuable for development workflows requiring human oversight of AI-generated code changes, interactive debugging sessions where developer input guides AI decisions, and collaborative coding environments that benefit from real-time human feedback integration.

## Trust

- **Trust score (0–1):** 0.97
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** api-integration
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tuandinh-feedback-loop)
- **Repository:** <https://github.com/tuandinh-org/feedback-loop-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "tuandinh-feedback-loop"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tuandinh-feedback-loop` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tuandinh-feedback-loop/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt