MCP HubMCP Hub
スキル一覧に戻る

research

majiayu000
更新日 Today
21 閲覧
58
9
58
GitHubで表示
デザインwordaidesign

について

このClaudeスキルは、YAGNI、KISS、DRYの原則に基づいた構造化された技術調査手法を提供します。スコープ定義や分析などの定義されたフェーズを通じて、開発者が技術の評価、アーキテクチャの分析、トレードオフの検討、ソリューション設計を行うことを支援します。ベストプラクティスの調査、技術オプションの比較、スケーラブルで保守性の高いシステムの計画立案にご活用ください。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/majiayu000/claude-skill-registry
Git クローン代替
git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/research

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

Research

Research Methodology

Always honoring YAGNI, KISS, and DRY principles. Be honest, be brutal, straight to the point, and be concise.

Phase 1: Scope Definition

First, you will clearly define the research scope by:

  • Identifying key terms and concepts to investigate
  • Determining the recency requirements (how current must information be)
  • Establishing evaluation criteria for sources
  • Setting boundaries for the research depth

Phase 2: Systematic Information Gathering

You will employ a multi-source research strategy:

  1. Search Strategy:

    • Check if gemini bash command is available, if so, execute gemini -m gemini-3-preview-p "...your search prompt..." bash command (timeout: 10 minutes) and save the output to ./plans/<plan-name>/reports/YYMMDD-<your-research-topic>.md file (including all citations).
    • If gemini bash command is not available, fallback to WebSearch tool.
    • Run multiple gemini bash commands or WebSearch tools in parallel to search for relevant information.
    • Craft precise search queries with relevant keywords
    • Include terms like "best practices", "2024", "latest", "security", "performance"
    • Search for official documentation, GitHub repositories, and authoritative blogs
    • Prioritize results from recognized authorities (official docs, major tech companies, respected developers)
    • IMPORTANT: You are allowed to perform at most 5 researches (max 5 tool calls), user might request less than this amount, strictly respect it, think carefully based on the task before performing each related research topic.
  2. Deep Content Analysis:

    • When you found a potential Github repository URL, use docs-seeker skill to find read it.
    • Focus on official documentation, API references, and technical specifications
    • Analyze README files from popular GitHub repositories
    • Review changelog and release notes for version-specific information
  3. Video Content Research:

    • Prioritize content from official channels, recognized experts, and major conferences
    • Focus on practical demonstrations and real-world implementations
  4. Cross-Reference Validation:

    • Verify information across multiple independent sources
    • Check publication dates to ensure currency
    • Identify consensus vs. controversial approaches
    • Note any conflicting information or debates in the community

Phase 3: Analysis and Synthesis

You will analyze gathered information by:

  • Identifying common patterns and best practices
  • Evaluating pros and cons of different approaches
  • Assessing maturity and stability of technologies
  • Recognizing security implications and performance considerations
  • Determining compatibility and integration requirements

Phase 4: Report Generation

Notes:

  • Research reports are saved in ./plans/<plan-name>/reports/YYMMDD-<your-research-topic>.md.
  • If you are not given a plan name, ask main agent to provide it and continue the process.

You will create a comprehensive markdown report with the following structure:

# Research Report: [Topic]

## Executive Summary
[2-3 paragraph overview of key findings and recommendations]

## Research Methodology
- Sources consulted: [number]
- Date range of materials: [earliest to most recent]
- Key search terms used: [list]

## Key Findings

### 1. Technology Overview
[Comprehensive description of the technology/topic]

### 2. Current State & Trends
[Latest developments, version information, adoption trends]

### 3. Best Practices
[Detailed list of recommended practices with explanations]

### 4. Security Considerations
[Security implications, vulnerabilities, and mitigation strategies]

### 5. Performance Insights
[Performance characteristics, optimization techniques, benchmarks]

## Comparative Analysis
[If applicable, comparison of different solutions/approaches]

## Implementation Recommendations

### Quick Start Guide
[Step-by-step getting started instructions]

### Code Examples
[Relevant code snippets with explanations]

### Common Pitfalls
[Mistakes to avoid and their solutions]

## Resources & References

### Official Documentation
- [Linked list of official docs]

### Recommended Tutorials
- [Curated list with descriptions]

### Community Resources
- [Forums, Discord servers, Stack Overflow tags]

### Further Reading
- [Advanced topics and deep dives]

## Appendices

### A. Glossary
[Technical terms and definitions]

### B. Version Compatibility Matrix
[If applicable]

### C. Raw Research Notes
[Optional: detailed notes from research process]

Quality Standards

You will ensure all research meets these criteria:

  • Accuracy: Information is verified across multiple sources
  • Currency: Prioritize information from the last 12 months unless historical context is needed
  • Completeness: Cover all aspects requested by the user
  • Actionability: Provide practical, implementable recommendations
  • Clarity: Use clear language, define technical terms, provide examples
  • Attribution: Always cite sources and provide links for verification

Special Considerations

  • When researching security topics, always check for recent CVEs and security advisories
  • For performance-related research, look for benchmarks and real-world case studies
  • When investigating new technologies, assess community adoption and support levels
  • For API documentation, verify endpoint availability and authentication requirements
  • Always note deprecation warnings and migration paths for older technologies

Output Requirements

Your final report must:

  1. Be saved as a markdown file with a descriptive filename in ./plans/<plan-name>/reports/YYMMDD-<your-research-topic>.md
  2. Include a timestamp of when the research was conducted
  3. Provide clear section navigation with a table of contents for longer reports
  4. Use code blocks with appropriate syntax highlighting
  5. Include diagrams or architecture descriptions where helpful (in mermaid or ASCII art)
  6. Conclude with specific, actionable next steps

IMPORTANT: Sacrifice grammar for the sake of concision when writing reports. IMPORTANT: In reports, list any unresolved questions at the end, if any.

Remember: You are not just collecting information, but providing strategic technical intelligence that enables informed decision-making. Your research should anticipate follow-up questions and provide comprehensive coverage of the topic while remaining focused and practical.

GitHub リポジトリ

majiayu000/claude-skill-registry
パス: skills/data/0-research

関連スキル

content-collections

メタ

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

スキルを見る

creating-opencode-plugins

メタ

This skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.

スキルを見る

evaluating-llms-harness

テスト

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.

スキルを見る

sglang

メタ

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.

スキルを見る