tech-search

Automate deep technical research with parallel web search and structured report generation.

Updated May 4, 2026
One-click install
npx skills add https://github.com/hitoshiseki/jutsu-simulator --skill tech-search-hitoshiseki
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tech-search
Source: https://github.com/hitoshiseki/jutsu-simulator/tree/main/.claude/skills/tech-search
Command: npx skills add https://github.com/hitoshiseki/jutsu-simulator --skill tech-search-hitoshiseki

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the hours of manual effort required for deep technical research by automating the full end-to-end pipeline from query parsing to synthesized, documented findings, with no external setup or dependencies needed.

Core Features & Use Cases

  • 6-Phase Automated Workflow: Handles auto-clarification of queries, decomposition into searchable sub-queries, parallel web search via Haiku workers, coverage evaluation, finding synthesis, and structured report generation.
  • Flexible Search Tooling: Works with optional MCP tools like Exa and Context7 for enhanced search and documentation retrieval, with automatic fallbacks to built-in web search and fetch tools if MCPs are unavailable.
  • Strict Scope Enforcement: Built-in guardrails prevent unauthorized file writes, code implementation, or creation of production artifacts, ensuring all output is limited to designated research documentation folders.
  • Use Case: If you need to compare the tradeoffs of React Server Components vs Client Components, this Skill automatically runs parallel searches across credible sources, evaluates coverage, and saves a complete research report with recommendations to your docs folder.

Quick Start

Use the tech-search skill to run a deep research report on any technical topic of your choice, such as "React Server Components vs Client Components".

Frequently Asked Questions about tech-search

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

FAQPage Schema
How do I automate deep technical research and generate a structured report?▼

Automating deep technical research is done by decomposing queries into sub-queries, running parallel web searches, scoring source credibility, and synthesizing findings into a structured report, eliminating manual effort.

What is the best way to compare software libraries and evaluate their tradeoffs?▼

Comparing library tradeoffs is handled by running parallel web searches across credible sources, evaluating coverage of the findings, and saving a complete research report with recommendations to your docs folder.

Do I need external dependencies or MCP tools to run parallel web searches?▼

No external dependencies are required to run parallel web searches. The workflow uses built-in web search and fetch tools with automatic fallbacks, though optional MCP tools like Exa and Context7 can enhance retrieval.

Can I use this automated research workflow for AI/ML and infrastructure domains?▼

Yes, automated research workflows apply directly to AI/ML and infrastructure domains, handling state-of-the-art research scenarios, technical comparisons, and library evaluations across software engineering contexts.

What are the limitations of using automated query decomposition for technical research?▼

Limitations include strict scope enforcement that prevents unauthorized file writes, code implementation, or creation of production artifacts, ensuring all output is limited to designated research documentation folders.