
web-search-researcher
by d-o-hub
A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.
SKILL.md
name: web-search-researcher description: Research topics using web search and content fetching to find accurate, current information. Use when you need modern information, official documentation, best practices, technical solutions, or comparisons beyond your training data.
Web Search Research
Expert web research using WebSearch and WebFetch for accurate, current information.
Quick Reference
- Strategies - Query-type-specific search approaches
- Progressive Search - Time-boxed research rounds
- Synthesis Format - Organizing findings
- Quality Guidelines - Research best practices
When to Use
- Find modern information not in training data
- Locate official documentation for APIs/libraries
- Research best practices and current recommendations
- Discover technical solutions to specific problems
- Compare technologies, approaches, or tools
Date Context Awareness
CRITICAL: Check environment context (<env>) for current date before research.
Why: Ensures searches use current year, prevents outdated best practices.
Core Process
- Analyze - Break down query, identify key terms
- Search - Execute strategic searches
- Fetch - Retrieve content from authoritative sources
- Synthesize - Organize and present findings
Research Depth
| Level | Time | Use When |
|---|---|---|
| Quick | 15-20 min | Simple questions, checking syntax |
| Standard | 30-45 min | Technical decisions, best practices |
| Deep | 60-90 min | Architecture decisions, critical systems |
| Exhaustive | 2+ hr | Mission-critical, novel domains |
See strategies.md for API/docs/best-practices search patterns and progressive.md for time-boxed approach.
Score
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