Does it save the research results to my project?

Quick checks remain in the conversation history, while deep-dive findings are saved into a specific research folder for your team to reference later.

What is the Research skill for Claude Code?

It is a specialized capability that allows Claude to access the live web to verify technical documentation, check for library updates, and ensure your code isn't using deprecated methods.

Which search engines does the Research skill use?

It prioritizes developer-focused MCP servers like Tavily and Exa for high-quality technical results, falling back to built-in web search tools if those are unavailable.

How does this skill handle quick vs. deep research?

The skill uses 'Quick Checks' for fast answers under a minute and 'Deep Dives' for complex 5-15 minute investigations, usually asking for user permission before starting a deep dive.

AI Research Assistant

Name: AI Research Assistant
Author: Light-Brands

byLight-Brands

开发者工具

Performs real-time web research to verify API statuses, check library versions, and ensure code implementations use current best practices.

关于

The Research skill empowers Claude Code to conduct targeted information gathering to prevent errors caused by outdated documentation or deprecated APIs. It intelligently differentiates between 'Quick Checks' for immediate syntax or version verification and 'Deep Dives' for complex architectural decisions, such as comparing technologies or planning migrations. By leveraging specialized MCP servers like Tavily and Exa, it provides actionable, scannable summaries of the latest technical data, ensuring that your development workflow is grounded in real-time information rather than stale training data.

主要功能

Differentiated workflows for rapid verification versus comprehensive deep-dive research
Structured output focus that highlights breaking changes and specific migration paths
Automated identification of version-specific errors and deprecated method signals
Intelligent tool selection prioritizing specialized MCP servers like Tavily and Exa
Optional persistence of deep research findings for team-wide knowledge sharing
0 GitHub stars

使用场景

Comparing competing frameworks or architectural patterns before starting a new feature
Verifying if a specific library method or API endpoint has changed in the latest release
Debugging version-specific errors by cross-referencing GitHub issues and official changelogs

关于

主要功能

Differentiated workflows for rapid verification versus comprehensive deep-dive research
Structured output focus that highlights breaking changes and specific migration paths
Automated identification of version-specific errors and deprecated method signals
Intelligent tool selection prioritizing specialized MCP servers like Tavily and Exa
Optional persistence of deep research findings for team-wide knowledge sharing
0 GitHub stars

使用场景

Comparing competing frameworks or architectural patterns before starting a new feature
Verifying if a specific library method or API endpoint has changed in the latest release
Debugging version-specific errors by cross-referencing GitHub issues and official changelogs