# Coding Feature Discussion

> Use this tool when you need to facilitate intelligent feature discussions and guide development teams through implementation, architecture, and best practices. It provides interactive discussion tools, persistent memory management, and context-aware recommendations, enabling teams to make informed design choices and maintain consistent project knowledge. Ideal for development teams seeking AI assistance in feature implementation and decision-making.

Canonical page: https://skillsregistry.net/skills/squirrelogic-feature-discussion  
JSON: https://api.skillsregistry.net/v1/skills/squirrelogic-feature-discussion

## Description

This MCP server, developed by Feature Discussion Squirrel Software, provides an AI-powered lead developer interface for facilitating intelligent feature discussions. Built with TypeScript and leveraging Next.js, it offers tools for interactive discussions, persistent memory management, and context-aware recommendations. The implementation focuses on guiding development teams through feature implementation, architectural decisions, and best practices. By connecting AI models with project context and development expertise, this server enables sophisticated scenarios like maintaining discussion history, tracking feature evolution, and providing tailored guidance. It's particularly useful for development teams seeking AI assistance in making informed design choices and maintaining consistent project knowledge.

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/squirrelogic-feature-discussion)
- **Repository:** <https://github.com/squirrelogic/mcp-feature-discussion>

## 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": "squirrelogic-feature-discussion"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/squirrelogic-feature-discussion` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/squirrelogic-feature-discussion/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