deep-research

Orchestrate parallel subagents to research topics and synthesize cited reports.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/YSheldon/Prompt-Log --skill deep-research-ysheldon
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/YSheldon/Prompt-Log/tree/main/.local/secondary_skills/deep-research
Command: npx skills add https://github.com/YSheldon/Prompt-Log --skill deep-research-ysheldon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Complex topics demand exhaustive, multi-source investigation and structured, cite-worthy outputs; this skill reduces manual research overhead by orchestrating source gathering, evaluation, and synthesis.

Core Features & Use Cases

  • Parallel focus areas: Decomposes topics into 5 non-overlapping angles and runs subagents in parallel to accelerate discovery.
  • Structured synthesis: Organizes findings into a report with citations, gaps, and cross-source analysis.
  • Governance & Quality: Includes source evaluation for credibility and timestamps research outputs.
  • Use Case: Ideal for literature reviews, market analyses, technology evaluations, and risk assessments requiring cross-source validation.

Quick Start

Initiate a deep, multi-source research session by defining scope, launching parallel subagents, and producing a cited synthesis.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I automate multi-source research and synthesis with citations?

Multi-source research and synthesis is automated by decomposing topics into non-overlapping angles, running parallel subagents for web-search discovery, and organizing findings into a published report with citations and cross-source analysis.

What is the best way to conduct a literature review across multiple web sources?

A literature review across multiple web sources is best conducted by applying phase-planning to evaluate source credibility, executing parallel subagents for discovery, and synthesizing cross-validated findings into a structured report.

Can I use parallel subagents to accelerate technology assessments and market analyses?

Parallel subagents can be used to accelerate technology assessments and market analyses by simultaneously gathering and evaluating multi-source data, reducing manual research overhead while producing timestamped outputs.

How does cross-source validation work during structured research?

Cross-source validation works by evaluating gathered web-search data for credibility, identifying information gaps, and synthesizing findings across multiple sources to ensure the final report is thorough and cite-worthy.

When do I need phase-planning for a deep research session?

Phase-planning is needed for a deep research session when complex topics demand exhaustive, multi-source investigation, requiring structured decomposition into distinct angles before running parallel subagents.

Are there limitations to using automated subagents for research synthesis?

Automated subagents for research synthesis are limited by web-search source availability and credibility; while source evaluation is applied, outputs require review to ensure cross-source gaps are fully addressed.