Agent skill

Gpt Researcher Guide

by wentorai in wentorai/research-plugins

Autonomous agent for comprehensive deep research on any topic

MITAuto-check passedResearch & Science

Install Gpt Researcher Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill gpt-researcher-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins gpt-researcher-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/deep-research/gpt-researcher-guide .claude/skills/gpt-researcher-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gpt-researcher-guide
GitHub stars
298
Used in
1 other repo
Token cost
~1.8k tokens
SKILL.md length
508 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Autonomous agent for comprehensive deep research on any topic

  • Works in 4 steps: Planner Agent: Decomposes the research… → Retriever Agents: Each sub-question is… → Ranker Agent: Evaluates and ranks… → …
  • Tasks that involve Deep research
  • SKILL.md covers Overview, Installation and Setup, Core Research Workflow and Advanced Configuration, plus 2 more sections
  • Calls pip, git and python; reaches github.com and pubmed.ncbi.nlm.nih.gov; needs OPENAI_API_KEY and ANTHROPIC_API_KEY

What it does

Gpt Researcher Guide is an agent skill from wentorai/research-plugins. Autonomous agent for comprehensive deep research on any topic

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Deep research and Autonomous loops. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Deep research
  • Tasks that involve Autonomous loops

Example prompts

  • “/gpt-researcher-guide”

Requirements

  • Python 3
  • A credential in OPENAI_API_KEY
  • A credential in ANTHROPIC_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Planner Agent: Decomposes the research query into 4-6 focused sub-questions
  2. Retriever Agents: Each sub-question is researched independently by a dedicated agent that searches, scrapes, and filters content
  3. Ranker Agent: Evaluates and ranks gathered sources by relevance and quality
  4. Writer Agent: Synthesizes all findings into a coherent, well-structured report with inline citations

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • git
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • pubmed.ncbi.nlm.nih.gov
    • nature.com
    • science.org

    Also links to:

    • docs.gptr.dev
    • tavily.com
    • arxiv.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY
    • ANTHROPIC_API_KEY
    • TAVILY_API_KEY
    • SERPER_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gpt Researcher Guide loads about 1.8k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 508 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 508 words, ~1,783 tokens.

Download SKILL.mdSave it as .claude/skills/gpt-researcher-guide/SKILL.md (or your agent's skills folder).
name
gpt-researcher-guide
description
Autonomous agent for comprehensive deep research on any topic

GPT Researcher Guide

Overview

GPT Researcher is an autonomous research agent with over 26,000 GitHub stars that conducts comprehensive online research on any given topic. Developed by Assaf Elovic, it generates detailed, factual, and unbiased research reports by planning research questions, searching multiple sources, scraping and filtering relevant content, and synthesizing findings into well-structured reports with citations.

The agent addresses a fundamental challenge in AI-assisted research: generating accurate, comprehensive reports rather than relying on a single LLM's potentially outdated or hallucinated knowledge. GPT Researcher uses a multi-agent architecture where a planner agent decomposes the research query into sub-questions, multiple retriever agents gather information from diverse sources, and a writer agent synthesizes everything into a coherent report.

For academic researchers, GPT Researcher is valuable for conducting preliminary literature surveys, exploring unfamiliar research domains, gathering background information for grant proposals, and generating initial drafts of review sections. The agent can be configured to search specific domains, use academic search engines, and output reports in various formats including markdown and PDF.

Installation and Setup

bash
# Install from PyPI
pip install gpt-researcher

# Or clone for development
git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcher
pip install -e .

Configure your environment with API keys using environment variables:

bash
# Required: LLM provider (choose one)
export OPENAI_API_KEY=$OPENAI_API_KEY
# Or use other providers
export ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY

# Required: Search provider (choose one)
export TAVILY_API_KEY=$TAVILY_API_KEY
# Or alternatives
export SERPER_API_KEY=$SERPER_API_KEY
export SEARX_URL=$SEARX_URL

For a fully local setup without external API dependencies, you can configure local LLMs and search engines:

bash
# Use local LLM via Ollama
export OPENAI_BASE_URL=http://localhost:11434/v1
export LLM_PROVIDER=ollama
export FAST_LLM=llama3
export SMART_LLM=llama3

# Use local search via SearXNG
export SEARX_URL=http://localhost:8888
export SEARCH_PROVIDER=searx

Core Research Workflow

Basic Research Report

Generate a research report with a single function call:

python
from gpt_researcher import GPTResearcher
import asyncio

async def run_research():
    query = "Recent advances in protein structure prediction using deep learning"
    researcher = GPTResearcher(query=query, report_type="research_report")

    # Conduct research (searches, scrapes, analyzes sources)
    research_result = await researcher.conduct_research()

    # Generate the final report
    report = await researcher.write_report()

    # Access sources used
    sources = researcher.get_source_urls()
    print(f"Report based on {len(sources)} sources")
    print(report)

asyncio.run(run_research())
Report Types

GPT Researcher supports multiple report types tailored to different needs:

  • research_report: Comprehensive report with findings and analysis (default)
  • detailed_report: Extended multi-page report with deeper analysis
  • resource_report: Curated list of sources with summaries and relevance scores
  • outline_report: Structured outline for further manual research
  • subtopic_report: Focused report on a specific subtopic within a broader area
python
# Generate a detailed multi-page report
researcher = GPTResearcher(
    query="Transformer architectures for scientific document understanding",
    report_type="detailed_report",
    max_subtopics=5,
)
Multi-Agent Architecture

The research process follows a sophisticated multi-agent pipeline:

  1. Planner Agent: Decomposes the research query into 4-6 focused sub-questions
  2. Retriever Agents: Each sub-question is researched independently by a dedicated agent that searches, scrapes, and filters content
  3. Ranker Agent: Evaluates and ranks gathered sources by relevance and quality
  4. Writer Agent: Synthesizes all findings into a coherent, well-structured report with inline citations
python
# Customize the research configuration
researcher = GPTResearcher(
    query="Impact of climate change on marine biodiversity",
    report_type="research_report",
    source_urls=None,  # Or provide specific URLs to research
    config_path=None,  # Or path to custom config
    max_search_results_per_query=5,
    verbose=True,
)
Show full SKILL.md (176 more words)Show less

Advanced Configuration

Custom Source Restrictions

Restrict research to specific domains or provide seed URLs:

python
# Research only from specific academic sources
researcher = GPTResearcher(
    query="CRISPR gene editing safety profiles",
    source_urls=[
        "https://pubmed.ncbi.nlm.nih.gov/",
        "https://www.nature.com/",
        "https://www.science.org/",
    ],
)
LLM Configuration

Configure different LLMs for different stages of the research pipeline:

python
# Use a fast model for planning and a powerful model for writing
# Set via environment variables
# FAST_LLM: Used for sub-question generation and filtering
# SMART_LLM: Used for report synthesis and writing
Integration with FastAPI

GPT Researcher includes a web interface and API server:

bash
# Start the web UI and API server
cd gpt-researcher
pip install -r requirements.txt
python -m uvicorn main:app --host 0.0.0.0 --port 8000

The API exposes WebSocket endpoints for streaming research progress and REST endpoints for report management, making it easy to integrate into existing research platforms.

Academic Research Applications

GPT Researcher can be adapted for several academic use cases:

  • Preliminary literature surveys: Quickly scan the landscape of a new research area before conducting a formal systematic review
  • Grant proposal background: Gather recent developments and state-of-the-art results to strengthen research proposals
  • Conference talk preparation: Generate comprehensive overviews of related work for presentations
  • Cross-disciplinary exploration: Investigate adjacent fields to identify potential collaboration opportunities or interdisciplinary approaches
  • Fact-checking and verification: Cross-reference claims across multiple sources to validate research findings

The reports include full citations with URLs, making it straightforward to verify sources and follow up with deeper reading of primary literature.

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research/deep-research/gpt-researcher-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Deep Research WorkflowTokenRhythm/opensquilla7.1k—~1.3kAutomated safety check: PassApache-2.0
Deep Researchsanjay3290/ai-skills4329 repos~683Automated safety check: NotesApache-2.0

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Questions about Gpt Researcher Guide

What does Gpt Researcher Guide do?

Autonomous agent for comprehensive deep research on any topic. Gpt Researcher Guide is an agent skill from wentorai/research-plugins.

When should I use Gpt Researcher Guide?

Gpt Researcher Guide fits situations like: tasks that involve Deep research; tasks that involve Autonomous loops.

How do I install Gpt Researcher Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill gpt-researcher-guide -a claude-code`. Or copy the skill folder (skills/research/deep-research/gpt-researcher-guide in wentorai/research-plugins) into .claude/skills/gpt-researcher-guide in your project. Claude Code loads it when a task matches its description.

How do I install Gpt Researcher Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill gpt-researcher-guide -a codex`. Or copy the skill folder (skills/research/deep-research/gpt-researcher-guide in wentorai/research-plugins) into .agents/skills/gpt-researcher-guide in your project. Codex loads it when a task matches its description.

Can I use Gpt Researcher Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill gpt-researcher-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gpt-researcher-guide, .gemini/skills/gpt-researcher-guide, .github/skills/gpt-researcher-guide and .opencode/skills/gpt-researcher-guide in your project.

What does Gpt Researcher Guide need to run?

Going by SKILL.md and its folder, Gpt Researcher Guide needs the command-line tools its instructions call (pip, git and python) and credentials named OPENAI_API_KEY, ANTHROPIC_API_KEY, TAVILY_API_KEY and SERPER_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY; A credential in ANTHROPIC_API_KEY.

Does Gpt Researcher Guide access the network?

SKILL.md names 7 domains. In commands or code: github.com, pubmed.ncbi.nlm.nih.gov, nature.com and science.org; the agent is likely to contact these when it follows the instructions. As links in the text: docs.gptr.dev, tavily.com and arxiv.org. This is read from the text; nothing was executed.

Is Gpt Researcher Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gpt Researcher Guide use?

Gpt Researcher Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gpt Researcher Guide use?

About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gpt Researcher Guide?

Skills that share tags, products or a category with Gpt Researcher Guide: Bounded Autoresearch (AgriciDaniel/claude-obsidian, 15k stars), Slate Ar Quality (udecode/plate, 17k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars) and Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gpt Researcher Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.