research
Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
85
Quality
84%
Does it follow best practices?
Impact
80%
2.05xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Evaluation results
100%
62%Caching Strategy Evaluation for High-Traffic API
Search tool selection and report file path
Research log exists
100%
100%
Search tool documented
100%
100%
Max 5 searches respected
0%
100%
Correct report directory
0%
100%
Date-prefixed filename
0%
100%
Executive Summary section
100%
100%
Research Methodology section
0%
100%
Key Findings section
0%
100%
Implementation Recommendations section
0%
100%
Resources & References section
50%
100%
Timestamp present
100%
100%
Without context: $1.4748 · 4m 30s · 23 turns · 1,810 in / 7,638 out tokens
With context: $1.5037 · 4m 46s · 27 turns · 1,766 in / 10,659 out tokens
86%
46%Container Strategy for Python Microservices
Report structure and content formatting
Table of contents
100%
90%
Syntax-highlighted code blocks
100%
100%
Diagram included
0%
33%
Security Considerations subsection
0%
100%
Performance Insights subsection
0%
100%
Comparative Analysis section
50%
100%
Quick Start Guide subsection
0%
87%
Common Pitfalls subsection
0%
87%
Actionable next steps
70%
70%
Unresolved questions
0%
100%
Timestamp present
100%
100%
Without context: $0.3793 · 2m 28s · 10 turns · 12 in / 9,044 out tokens
With context: $1.4551 · 4m 56s · 29 turns · 6,390 in / 12,439 out tokens
56%
15%State Management Library Selection for Large-Scale React Application
GitHub repository analysis and report conciseness
Source notes file exists
100%
100%
GitHub URLs in source notes
100%
100%
docs-seeker or repo content evidence
66%
83%
Official Documentation subsection
0%
0%
Community Resources subsection
0%
0%
Appendices section
0%
0%
Glossary in Appendices
0%
0%
Concise prose style
83%
100%
Comparative Analysis section
50%
60%
Code Examples subsection
0%
37%
Correct report directory
0%
100%
Without context: $1.2083 · 4m 6s · 23 turns · 1,155 in / 7,401 out tokens
With context: $1.0813 · 3m 43s · 20 turns · 3,340 in / 7,646 out tokens
- Repository
- majiayu000/claude-skill-registry-data
- Commit
2bbaa03
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.