How does the parallel subagent system work?

Depth Search launches multiple tasks simultaneously across different academic databases, allowing it to aggregate results from Arxiv, Semantic Scholar, and Deepwiki at once for maximum efficiency.

Why does this skill search local files first?

It prioritizes the ~/.topos directory to leverage your existing research, notes, and cached data, which prevents redundant searches and maintains continuity with your previous work.

What makes Depth Search different from a standard web search?

Unlike standard web search, Depth Search uses semantic search tools like Exa and academic-specific MCPs like Arxiv and Semantic Scholar to find high-quality, peer-reviewed content and technical documentation rather than basic keyword matches.

What is the 'topological chemputer' mentioned in the repository?

The repository describes a system where knowledge is treated as topological structures. This skill helps manage those structures by ensuring comprehensive data collection across various knowledge domains.

Depth Search

Name: Depth Search
Author: plurigrid

byplurigrid

•

数据科学与机器学习

Conducts exhaustive, multi-source research across academic databases, semantic search engines, and local knowledge bases.

关于

Depth Search is a sophisticated research orchestration skill designed for deep-dive investigations into complex technical and scientific topics. It leverages a 'local-first' strategy by searching the ~/.topos directory before deploying parallel subagents to query academic sources like Arxiv, Semantic Scholar, and Deepwiki. By utilizing Exa for semantic web search instead of standard keyword matching, it ensures high-signal results and synthesizes information from multiple disparate sources into a cohesive knowledge base, making it an essential tool for literature reviews, technical documentation, and topological data analysis.

主要功能

Integrates Exa semantic search for high-quality web results over standard keyword matching.
Analyzes GitHub repository structures and documentation via Deepwiki integration.
Avoids low-quality fallbacks by prompting users for specific context when automated sources are exhausted.
Orchestrates parallel subagents for concurrent searches across Arxiv, Semantic Scholar, and Paper-Search.
2 GitHub stars
Prioritizes local knowledge retrieval from ~/.topos directory to leverage existing research.

使用场景

Cross-referencing local research notes with global academic databases and semantic web content.
Performing comprehensive literature reviews for scientific papers or technical reports.
Deep-diving into complex GitHub repository documentation and related preprint research.

关于

主要功能

Integrates Exa semantic search for high-quality web results over standard keyword matching.
Analyzes GitHub repository structures and documentation via Deepwiki integration.
Avoids low-quality fallbacks by prompting users for specific context when automated sources are exhausted.
Orchestrates parallel subagents for concurrent searches across Arxiv, Semantic Scholar, and Paper-Search.
2 GitHub stars
Prioritizes local knowledge retrieval from ~/.topos directory to leverage existing research.

使用场景

Cross-referencing local research notes with global academic databases and semantic web content.
Performing comprehensive literature reviews for scientific papers or technical reports.
Deep-diving into complex GitHub repository documentation and related preprint research.