skills.rest
yudame
yudameOFFICIAL
Explore all skills by this author →
WebsiteGitHub

gpt-researcher

Coordinate a local multi-agent framework to research 100+ sources and generate Markdown reports with inline citations.

2|1|Updated May 11, 2025
Search Tags:#automation#multi-agent#citations#openai#deep research#gpt-5.2#gpt-researcher
One-click install
npx skills add https://github.com/yudame/research --skill gpt-researcher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpt-researcher
Source: https://github.com/yudame/research/tree/main/.claude/skills/gpt-researcher
Command: npx skills add https://github.com/yudame/research --skill gpt-researcher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traditional deep research is often limited by single-agent capabilities, slow, and can be costly or reliant on fragile browser automation. This skill provides a robust, local, multi-agent framework for comprehensive, parallel research using advanced LLMs, replacing deprecated browser-based solutions.

Core Features & Use Cases

  • Multi-Agent Framework: Runs GPT-Researcher locally, enabling parallel information gathering from 100+ sources for faster, more thorough results.
  • Advanced LLM Integration: Leverages OpenAI GPT-5.2 (or other providers like Claude Opus) for superior reasoning, synthesis, and instruction following.
  • Comprehensive Citations: Provides detailed inline citations and a full list of sources, ensuring research quality and verifiability.
  • Flexible Report Types: Supports various report formats (standard, detailed, quick) to match different research needs and time constraints.
  • Use Case: Conduct a comprehensive industry and technical research report on a complex topic, synthesizing information from 100+ sources using GPT-5.2, replacing the deprecated ChatGPT browser automation for a more reliable and controlled workflow.

Quick Start

Use the gpt-researcher skill to generate a detailed report on "the future of sustainable urban planning, focusing on smart infrastructure and renewable energy integration."

Frequently Asked Questions about gpt-researcher

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

FAQPage Schema
How do I automate deep research across multiple sources with parallel agents?▼

Deep research automation uses multi-agent frameworks to gather information from 100+ sources in parallel. GPT-Researcher coordinates local agents to explore diverse sources simultaneously, then synthesizes findings into structured reports faster than sequential research and with comprehensive citations for verification.

Can I run research automation locally without relying on browser automation?▼

Local multi-agent research frameworks eliminate fragile browser automation by running as deployable services on your infrastructure. GPT-Researcher operates entirely within your environment, giving you full configuration control and eliminating dependencies on deprecated browser-based solutions.

What LLM providers does a multi-agent research framework support?▼

Modern research frameworks support cross-provider model access, including OpenAI GPT-5.2, Claude Opus, and OpenRouter integrations. This flexibility lets you choose providers based on reasoning quality, cost, or organizational requirements while maintaining consistent research workflows.

How do I generate research reports with inline citations and source verification?▼

Research automation with citations embeds source references directly into report text and appends a full source list, ensuring every claim is verifiable. Structured Markdown output preserves formatting and citations for seamless integration into documentation, technical reports, and industry analyses.

What report formats are available for different research timelines?▼

Multi-agent research supports flexible report types—standard for balanced depth, detailed for comprehensive analysis, and quick for rapid overviews. Format selection lets you match research scope to available time and audience requirements without rebuilding the research workflow.

Does local research automation work for industry reports and technical documentation?▼

Parallel multi-agent research is purpose-built for Phase 3 synthesis tasks: industry reports, technical documentation, and case studies requiring synthesis from 100+ sources. Local deployment with advanced LLM reasoning produces structured, citation-rich outputs suitable for professional publication and compliance auditing.

Related Skills

Explore More Education & Research Skills→
IbIFACE-TechIbIFACE-Tech

provider-integration

Configure and switch between multiple LLM providers like OpenAI, Anthropic, Azure, and Ollama.

Official
Intermediate
TomazWangTomazWang

doc-converter

Convert PPTX, PDF, DOCX, HTML, and Confluence exports into Markdown.

Community
Intermediate
amanning3390amanning3390
13

web-researcher

Automate multi-source web research and synthesize structured reports with citations.

Community
Advanced
mrsono0mrsono0

deep-research

Execute an 8-phase research pipeline with source retrieval and report generation.

Community
Advanced