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lxh755818-bot
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deep-research

Standardize AI Agent ecosystem research and output structured JSON reports.

Updated Apr 30, 2026
Search Tags:#AI research#AI Agent#ecosystem analysis#structured reporting#data-driven decision-making
One-click install
npx skills add https://github.com/lxh755818-bot/obsidian-vault --skill deep-research-lxh755818-bot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/lxh755818-bot/obsidian-vault/tree/main/backup/skills/mlops/deep-research
Command: npx skills add https://github.com/lxh755818-bot/obsidian-vault --skill deep-research-lxh755818-bot

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of researching AI Agent ecosystem projects, ensuring standardized execution and producing structured JSON reports for informed decision-making.

Core Features & Use Cases

  • Standardized Research Execution: Ensures that every research project is executed in a standardized manner.
  • Structured JSON Reporting: Outputs comprehensive and structured JSON reports that facilitate data-driven decision-making.
  • Use Case: For instance, when evaluating the potential of a new AI Agent framework, this Skill can be used to gather detailed information, compare it with existing solutions, and provide a structured report.

Quick Start

Run the deep-research skill with the topic name 'AI Agent Framework' to initiate the research process.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I standardize AI Agent ecosystem research into a structured JSON report?▼

To standardize AI Agent ecosystem research into a structured JSON report, execute multiple search rounds to extract project features and compare them against existing solutions like Hermes for data-driven decision-making.

What is the best way to compare a new AI Agent framework with existing solutions?▼

Comparing a new AI Agent framework with existing solutions involves standardizing the research process, executing multiple web search rounds, and extracting features to generate a structured JSON report for evaluation.

Do I need mcp_minimax_web_search to execute AI ecosystem project research?▼

Yes, you need mcp_minimax_web_search configured as a dependency to execute AI ecosystem project research, as it provides the web searching capability required for gathering detailed project information.

Can I use this approach to evaluate the potential of any AI Agent ecosystem project?▼

Yes, you can evaluate any AI Agent ecosystem project by running the research skill with a specific topic name, which gathers detailed information and outputs a structured report for data-driven decisions.

What limitations exist when generating structured reports for AI Agent projects?▼

Limitations when generating structured reports include dependency on mcp_minimax_web_search for web searches, meaning research depth is constrained by search query quality and the availability of accessible online data.

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