Back to Skills

create-hive-issue

majiayu000
Updated Today
17 views
58
9
58
View on GitHub
Metaai

About

This Claude Skill creates detailed GitHub issues using a coordinated multi-worker approach with mprocs. It structures the process into scouting, analysis, and drafting phases, producing a thorough, multi-perspective issue write-up. The workflow is managed through a tasks.json configuration that tracks session status and assigns specific roles to different workers.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/majiayu000/claude-skill-registry
Git CloneAlternative
git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/create-hive-issue

Copy and paste this command in Claude Code to install this skill

Documentation

Create Hive Issue

Overview

Use mprocs to coordinate multiple workers for a deep issue write-up.

Inputs

  • Issue description

Workflow

  1. Verify git and mprocs.
  2. Create .hive/sessions/<session-id> and tasks.json.
  3. Write queen and worker prompts (scout, analysis, draft).
  4. Launch mprocs and synthesize a final issue.

tasks.json Template

{
  "session": "{SESSION_ID}",
  "created": "{ISO_TIMESTAMP}",
  "status": "active",
  "thread_type": "Hive",
  "task_type": "create-hive-issue",
  "issue": {"description": "{ISSUE_DESC}"},
  "tasks": [
    {"id": "scout", "owner": "worker-1", "status": "pending"},
    {"id": "analysis", "owner": "worker-2", "status": "pending"},
    {"id": "draft", "owner": "worker-3", "status": "pending"}
  ]
}

Worker Prompt Outline

# Worker - Issue Scout
- Locate relevant files
- Summarize evidence

# Worker - Issue Analysis
- Identify scope and risks

# Worker - Issue Draft
- Write title and body

mprocs Launch

mprocs --config .hive/mprocs.yaml

Output

  • Detailed GitHub issue with triage notes

GitHub Repository

majiayu000/claude-skill-registry
Path: skills/create-hive-issue

Related Skills

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill

cloudflare-turnstile

Meta

This skill provides comprehensive guidance for implementing Cloudflare Turnstile as a CAPTCHA-alternative bot protection system. It covers integration for forms, login pages, API endpoints, and frameworks like React/Next.js/Hono, while handling invisible challenges that maintain user experience. Use it when migrating from reCAPTCHA, debugging error codes, or implementing token validation and E2E tests.

View skill