Search Strategy

The search-strategy skill enables AI agents to autonomously formulate and execute multi-step search plans. It breaks down complex queries into logical sub-queries, manages context across different information sources, and synthesizes findings into a coherent final answer.

When to use it

• Resolving ambiguous or broad questions that require narrowing down specific topics before searching.
• Tackling research tasks involving multiple distinct domains or conflicting data points.
• Gathering comprehensive background information needed for high-level decision-making.
• Verifying facts by cross-referencing results from various search engines or knowledge bases.

Key capabilities

• Query Decomposition: Automatically splits complex user requests into smaller, actionable sub-queries.
• Context Management: Maintains a running summary of findings to ensure subsequent searches remain relevant.
• Iterative Refinement: Dynamically adjusts search terms based on initial results to improve accuracy.
• Synthesis: Combines disparate pieces of information from multiple sources into a unified response.

Example prompts

• "Research the latest advancements in solid-state batteries and compare their safety profiles against current lithium-ion technology."
• "Find out what caused the recent supply chain disruptions in the semiconductor industry and how major manufacturers are responding."
• "Investigate the pros and cons of remote work policies for software engineering teams, citing recent studies from 2023 and 2024."

Tips & gotchas

This skill is most effective when the initial query is open-ended; avoid using it for simple fact retrieval where a direct search would suffice. Ensure your AI agent has access to multiple search tools or knowledge sources to fully leverage its ability to cross-reference information.