.claude_skill

This skill enables autonomous web and local research with citation-rich reports, helping developers understand, extend, and integrate GPT Researcher workflows

Python

25.3k

GitHub Stars

1

Bundled Files

3 weeks ago

Catalog Refreshed

2 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstart where the catalogue uses aiagentskills.

npx veilstart add skill assafelovic/gpt-researcher --skill .claude

SKILL.md7.2 KB

Overview

This skill describes GPT Researcher, an autonomous deep-research agent implemented in Python that orchestrates planning, retrieval, and report generation. It is designed to run locally or as a service, parallelize sub-tasks for speed, and produce citation-backed Markdown reports suitable for developer workflows and integrations.

How this skill works

GPT Researcher instantiates GPTResearcher which selects an agent type and role prompt, then the ResearchConductor plans sub-queries and parallelizes processing of each sub-query using retrievers and LLM providers. Contexts are aggregated, optional images are generated, and the ReportGenerator composes a final Markdown report with citations and structured sections.

When to use it

Adding a new feature, provider, or retriever to the agent
Integrating MCP data sources like GitHub via the MCP server
Customizing research workflows or report formats
Troubleshooting research pipelines, async issues, or WebSocket streaming
Running deep recursive research (deep mode) for comprehensive analysis

Best practices

Keep configuration keys lowercased when accessed (config.my_var) to avoid bugs
Register new retrievers in actions/retriever.py and export them from retrievers/__init__.py
Implement graceful degradation: catch exceptions and stream errors instead of crashing
Use async/await everywhere for research methods and retriever search functions
Use WebSocketHandler or similar to stream progress and logs during long runs

Example use cases

Extend the system with a new LLM provider: add provider folder, update prompts, register in agent
Create a custom retriever for an internal knowledge base and register it to be used by the conductor
Integrate a GitHub MCP connector to surface repo-level context for a security audit report
Run deep research on a technical topic to generate a recursive, citation-rich deep report
Embed the agent in a backend service and stream intermediate outputs to a frontend via WebSocket

FAQ

Create the retriever class under retrievers/your_retriever, implement an async search() returning title/href/body items, register it in actions/retriever.py, and export it in retrievers/__init__.py.

Where are prompts and defaults configured?

Prompts live in prompts.py and default configuration values are in config/variables/default.py; note config keys are accessed lowercased in runtime.

How do I enable MCP data sources?

Pass mcp_configs and select mcp_strategy when constructing GPTResearcher; ensure the MCP server command and environment variables are correctly set for each connector.