Back to Skills

Creating GitHub Issues from Web Research

jeremylongshore
Updated Today
80 views
918
111
918
View on GitHub
Metaaiautomation

About

This skill enables Claude to research a topic via web search and automatically generate a well-structured GitHub issue from the findings. It's designed for developers who need to convert research into actionable, trackable tasks for their team. Trigger it by asking Claude to research a topic and create a corresponding ticket.

Documentation

Overview

This skill empowers Claude to streamline the research-to-implementation workflow. By integrating web search with GitHub issue creation, Claude can efficiently convert research findings into trackable tasks for development teams.

How It Works

  1. Web Search: Claude utilizes its web search capabilities to gather information on the specified topic.
  2. Information Extraction: The plugin extracts relevant details, key findings, and supporting evidence from the search results.
  3. GitHub Issue Creation: A new GitHub issue is created with a clear title, a summary of the research, key recommendations, and links to the original sources.

When to Use This Skill

This skill activates when you need to:

  • Investigate a technical topic and create an implementation ticket.
  • Track security vulnerabilities and generate a security issue with remediation steps.
  • Research competitor features and create a feature request ticket.

Examples

Example 1: Researching Security Best Practices

User request: "research Docker security best practices and create a ticket in myorg/backend"

The skill will:

  1. Search the web for Docker security best practices.
  2. Extract key recommendations, security vulnerabilities, and mitigation strategies.
  3. Create a GitHub issue in the specified repository with a summary of the findings, a checklist of best practices, and links to relevant resources.

Example 2: Investigating API Rate Limiting

User request: "find articles about API rate limiting, create issue with label performance"

The skill will:

  1. Search the web for articles and documentation on API rate limiting.
  2. Extract different rate limiting techniques, their pros and cons, and implementation examples.
  3. Create a GitHub issue with the "performance" label, summarizing the findings and providing links to the source articles.

Best Practices

  • Specify Repository: When creating issues for a specific project, explicitly mention the repository name to ensure the issue is created in the correct location.
  • Use Labels: Add relevant labels to the issue to categorize it appropriately and facilitate issue tracking.
  • Provide Context: Include sufficient context in your request to guide the web search and ensure the generated issue contains the most relevant information.

Integration

This skill seamlessly integrates with Claude's web search Skill and requires authentication with a GitHub account. It can be used in conjunction with other skills to further automate development workflows.

Quick Install

/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/skill-adapter

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

GitHub 仓库

jeremylongshore/claude-code-plugins-plus-skills
Path: backups/plugin-enhancements/plugin-backups/web-to-github-issue_20251020_081058/skills/skill-adapter
aiautomationclaude-codedevopsmarketplacemcp

Related Skills

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

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

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

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