deep-research

Name: deep-research
Availability: InStock
Author: chuwenwu

Automate an 8-phase research pipeline producing citation-backed Markdown reports.

Updated Aug 27, 2026

Search Tags:#autonomy #claude-code #deep-research #citation-management #triangulation #source-credibility

Ready to run

npx skills add https://github.com/chuwenWu/Auto-Company --skill deep-research-chuwenwu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/chuwenWu/Auto-Company/tree/main/.claude/skills/deep-research
Command: npx skills add https://github.com/chuwenWu/Auto-Company --skill deep-research-chuwenwu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The Deep Research Skill automates the end-to-end process of producing citation-backed, verifiable research reports for Claude Code. It orchestrates an eight-phase workflow, automated validation, and source credibility scoring to ensure high-quality outputs with minimal human intervention.

Core Features & Use Cases

Eight-phase pipeline: Scope → Plan → Retrieve → Triangulate → Synthesize → Critique → Refine → Package, with progressive context management.
Automated validation & credibility scoring: Built-in checks ensure source diversity, citation integrity, and report quality.
Autonomous operation: Designed to run with minimal prompts, suitable for quick quick-starts or deep analyses on enterprise topics.

Quick Start

To start, say: Use deep research to analyze a topic in standard mode.

Frequently Asked Questions about deep-research

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

FAQPage Schema

How do I automate citation-backed research report generation?▼

This Skill automates citation-backed research report generation through an eight-phase pipeline, performing parallel retrieval, triangulation, and synthesis to output a complete Markdown report with a bibliography.

What is source triangulation and how does it work in automated research?▼

Source triangulation in automated research cross-references multiple retrieved data points to validate findings, ensuring source diversity and high credibility scores before the synthesis phase.

Can I run deep research workflows using only Python standard libraries?▼

Yes, you can run deep research workflows using only Python stdlib, as the pipeline operates autonomously without external dependencies to manage progressive context loading and retrieval.

Does this automated research pipeline support parallel retrieval and multiple depth modes?▼

Yes, the pipeline supports parallel retrieval across 10+ sources and offers Quick, Standard, Deep, and UltraDeep modes to control the depth of autonomous enterprise research and analysis.

What is the best way to ensure source credibility in automated research reports?▼

The best way to ensure source credibility is using an autonomous pipeline with built-in automated validation and scoring, which checks citation integrity and source diversity before packaging the report.

How do I initiate a standard research analysis with minimal prompts?▼

To initiate standard research analysis with minimal prompts, execute the command to use deep research in standard mode, triggering the autonomous eight-phase workflow from scoping to packaging.