deep-research

Automate comprehensive research through an 8-phase pipeline with source credibility scoring.

Updated Feb 14, 2026

Search Tags:#research #verification #analysis #efficiency #comprehensive

Ready to run

npx skills add https://github.com/amit397/PolymarketInsiderTracker --skill deep-research-amit397

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/amit397/PolymarketInsiderTracker/tree/main/.agents/skills/deep-research
Command: npx skills add https://github.com/amit397/PolymarketInsiderTracker --skill deep-research-amit397

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates and optimizes the research process, enabling users to conduct in-depth, verified research quickly and efficiently, without compromising on quality or accuracy.

Core Features & Use Cases

Comprehensive Research: Automates the entire research process from data retrieval to report generation.
Quality Control: Ensures accuracy and reliability through source credibility scoring, triangulation, and validation.
Customizable Depth: Offers four research modes (Quick, Standard, Deep, UltraDeep) to balance depth and speed.
Use Case: For instance, a user could request a "deep research on the impact of AI on job markets 2024" and receive a comprehensive analysis report with citations and insights.

Quick Start

Use the deep-research skill to conduct a "deep research on the impact of AI on job markets 2024".

Frequently Asked Questions about deep-research

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

FAQPage Schema

How do I automate comprehensive research for complex queries?▼

You can automate comprehensive research by running complex queries through an 8-phase pipeline that progresses from scoping and planning to retrieval, triangulation, and synthesis, ultimately generating a packaged analysis report.

How does source credibility scoring work during research analysis?▼

Source credibility scoring evaluates retrieved data sources during the triangulation phase, systematically ranking and validating references to ensure research accuracy and prevent the synthesis of unverified information.

What is the best way to balance research depth and speed for enterprise analysis?▼

You can balance research depth and speed by selecting one of four customizable research modes—Quick, Standard, Deep, or UltraDeep—allowing you to scale the analysis pipeline according to your specific enterprise requirements.

Do I need external APIs to retrieve data for automated research pipelines?▼

Yes, you need external APIs configured in your environment alongside the Python standard library to enable automated data retrieval and execute the comprehensive research pipeline effectively.

Can I use this automated research process for enterprise-grade market analysis?▼

Yes, the automated research process is designed for enterprise-grade analysis, utilizing progressive context management and an 8-phase pipeline to deliver comprehensive, verified insights for complex market queries.

What are the limitations of using automated pipelines for deep research?▼

Limitations include dependency on external API data availability for retrieval and the processing time required for UltraDeep modes, meaning highly obscure queries may lack sufficient triangulated sources for complete synthesis.