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ExpeditoJ
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tech-search

Decompose broad technical questions into sub-queries and synthesize research reports.

Updated Apr 5, 2026
Search Tags:#automation#research#web search#technical analysis#AI workflows#deep research
One-click install
npx skills add https://github.com/ExpeditoJ/My-aiox --skill tech-search-expeditoj
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tech-search
Source: https://github.com/ExpeditoJ/My-aiox/tree/main/.claude/skills/tech-search
Command: npx skills add https://github.com/ExpeditoJ/My-aiox --skill tech-search-expeditoj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Facilitates autonomous, in-depth technical research by automating query decomposition, parallel web searching, and comprehensive report synthesis, reducing manual effort.

Core Features & Use Cases

  • Automated Research Workflow: Decomposes broad technical questions into targeted sub-queries.
  • Parallel Web Search & Deep Reading: Utilizes multiple tools to gather and analyze specific technical data efficiently.
  • Use Case: A developer queries "React Server Components vs Client Components", and the Skill autonomously retrieves, analyzes, and synthesizes relevant technical information, producing a detailed report for informed decision-making.

Quick Start

Provide a broad technical query such as "best practices for deploying Python web applications" and let the system handle the rest.

Frequently Asked Questions about tech-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate in-depth technical research for software architecture questions?▼

Automated technical research uses an AI pipeline to decompose broad architecture questions into targeted sub-queries, execute parallel web searches, and synthesize the findings into a comprehensive report.

What is the best way to compare emerging technologies like React Server Components vs Client Components?▼

Comparing emerging technologies is best handled by autonomous research workflows that retrieve, analyze, and synthesize specific technical data from parallel web searches into a detailed decision-making report.

How does query decomposition work for complex software development research?▼

Query decomposition for software development research works by breaking down broad technical questions into specific sub-queries, enabling parallel web searches to gather and analyze targeted data efficiently.

Can I use autonomous web search to gather best practices for deploying Python web applications?▼

Yes, autonomous web search can retrieve best practices for deploying Python web applications by decomposing the query, executing parallel searches, and synthesizing a comprehensive technical report.

Does this automated research pipeline require any external dependencies or API keys to run?▼

This automated research pipeline is a self-contained AI workflow with no listed external dependencies, allowing it to perform detailed technical analysis and parallel web searching independently.

What are the limitations of using AI workflows for deep technical analysis?▼

Limitations of AI workflows for deep technical analysis include relying entirely on web search results for data retrieval, meaning synthesis quality depends on the availability and accuracy of online sources for the specific technical query.

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