How does the Research skill decide when to search the web?

The skill uses 'natural triggers' such as encountering errors that suggest API changes, implementing security-critical features like payments or auth, or working with libraries known for rapid evolution.

Where are the research results stored?

Quick checks remain in the conversation history for immediate use, while deep dives are saved as markdown files in a research/ directory for future team reference.

Will this research slow down my development process?

The skill is designed for efficiency, differentiating between 60-second 'Quick Checks' for syntax and 5-15 minute 'Deep Dives.' It always asks for permission before performing a time-intensive deep dive.

Which search tools does this skill use?

It prioritizes high-quality MCP servers like Tavily and Exa for developer-centric results, and falls back to built-in web search tools for Claude Code or Cursor if those are unavailable.

Code Research & Verification

Name: Code Research & Verification
Author: TechNickAI

byTechNickAI

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学習とドキュメンテーション

Ensures accurate implementations by strategically performing web research on evolving APIs, security standards, and documentation.

概要

This skill empowers Claude to proactively identify when current web information is necessary to avoid bugs, architectural mistakes, or security vulnerabilities. It balances speed and depth by distinguishing between sub-minute 'Quick Checks' for syntax and 15-minute 'Deep Dives' for complex architectural decisions, leveraging top-tier search tools like Tavily or Exa to provide scannable, actionable insights directly within the developer workflow. By focusing on official documentation and changelogs, it helps developers stay ahead of breaking changes and deprecated patterns in rapidly evolving ecosystems.

主な機能

Dual-mode research strategy (Quick Check vs. Deep Dive)
Context-aware triggers for security-critical or version-sensitive tasks
10 GitHub stars
Intelligent tool selection prioritizing Tavily and Exa MCP servers
Official source prioritization including docs, changelogs, and GitHub releases
Structured, actionable output formatting for findings and migration paths

ユースケース

Debugging version-specific errors that don't match the AI's internal training data.
Making high-stakes architectural decisions by comparing competing libraries or patterns.
Verifying breaking changes in rapidly evolving APIs like Stripe, Firebase, or React.

概要

主な機能

Dual-mode research strategy (Quick Check vs. Deep Dive)
Context-aware triggers for security-critical or version-sensitive tasks
10 GitHub stars
Intelligent tool selection prioritizing Tavily and Exa MCP servers
Official source prioritization including docs, changelogs, and GitHub releases
Structured, actionable output formatting for findings and migration paths

ユースケース

Debugging version-specific errors that don't match the AI's internal training data.
Making high-stakes architectural decisions by comparing competing libraries or patterns.
Verifying breaking changes in rapidly evolving APIs like Stripe, Firebase, or React.