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d-o-hub

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.

3🍴 0📅 2026年1月23日
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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

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Why: Ensures searches use current year, prevents outdated best practices.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

  1. Analyze - Break down query, identify key terms
  2. Search - Execute strategic searches
  3. Fetch - Retrieve content from authoritative sources
  4. Synthesize - Organize and present findings

Research Depth

LevelTimeUse When
Quick15-20 minSimple questions, checking syntax
Standard30-45 minTechnical decisions, best practices
Deep60-90 minArchitecture decisions, critical systems
Exhaustive2+ hrMission-critical, novel domains

See strategies.md for API/docs/best-practices search patterns and progressive.md for time-boxed approach.

スコア

総合スコア

75/100

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