WebResearchAnalysis

Generate structured daily intelligence summaries from mined web research content.

1|Updated Mar 3, 2026

Search Tags:#summarization #analysis #llm #web research #content synthesis #intelligence briefing

Ready to run

npx skills add https://github.com/DaveSteadman/MiniAgentFramework --skill webresearchanalysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: WebResearchAnalysis
Source: https://github.com/DaveSteadman/MiniAgentFramework/tree/main/code/skills/WebResearchAnalysis
Command: npx skills add https://github.com/DaveSteadman/MiniAgentFramework --skill webresearchanalysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires prompt_tokens, webresearch_utils, ollama_client, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of synthesizing information gathered from web research into a structured, easy-to-understand daily intelligence summary.

Core Features & Use Cases

Automated Summarization: Reads mined web content and uses an LLM to generate a structured analysis.
Structured Output: Produces summaries with Executive Summary, Main Stories, Notable Data Points, and Overall Assessment sections.
Use Case: A market analyst can use this Skill to automatically generate a daily briefing on AI hardware releases by feeding it the previously mined web content for that day.

Quick Start

Use the WebResearchAnalysis skill to create a daily summary for the 'AINews' domain for today's research.

Frequently Asked Questions about WebResearchAnalysis

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

FAQPage Schema

How do I automate daily intelligence summaries from mined web research content?▼

You can automate daily intelligence summaries by feeding mined web research content into an LLM analysis process that generates structured reports. This approach applies to domains requiring regular synthesis of multiple online sources, such as news aggregation or competitive intelligence.

What is the structure of an LLM generated intelligence briefing?▼

An LLM generated intelligence briefing is structured into Executive Summary, Main Stories, Notable Data Points, and Overall Assessment sections. This structured output transforms raw mined web content into a synthesized daily analysis.

Do I need Ollama to generate structured content synthesis from web content?▼

Yes, you need Ollama to generate structured content synthesis because the Skill requires Ollama for local LLM calls. It also depends on specific Python modules for file path management and date parsing.

Can I use automated summarization for competitive intelligence and news aggregation?▼

Yes, you can use automated summarization for competitive intelligence and news aggregation. The Skill applies to domains requiring regular synthesis of information from multiple online sources to produce structured daily briefings.

What's the best way to create a daily briefing for a specific research domain?▼

The best way to create a daily briefing for a specific research domain is to feed previously mined web content for that day into an LLM. The tool then processes the domain content to generate a structured analysis automatically.