Deep Research Skill

Input

$ARGUMENTS = the topic to research

Process

Step 1: Broad Search (5 to 8 searches)

Search the web for high-quality sources on the topic:

• Original research papers (arXiv, conference proceedings)
• Official documentation and blog posts from the creators
• Well-written technical blog posts (Lilian Weng, Jay Alammar, etc.)
• Video transcripts or lecture notes if available
• GitHub implementations for reference

Step 2: Deep Read

For each promising source, use WebFetch to read the full content. Extract and organize:

Core Concepts

• What is this? (one-paragraph definition)
• Why does it exist? What problem does it solve?
• What did it replace or improve upon?

How It Works (Technical Depth)

• Step-by-step mechanism
• Key equations and their intuition
• Concrete numerical examples (shapes, dimensions, values)
• Implementation details

Comparisons and Alternatives

• How does this compare to previous approaches?
• What are the trade-offs?
• Quantitative comparisons (benchmarks, memory savings, speedups)

Historical Context

• When was it introduced? By whom?
• What papers are most relevant?
• How has it evolved since introduction?

Step 3: Identify Visual Opportunities

This is critical. For EVERY concept, ask: "Would a diagram help here?" List 6 to 10 concepts that NEED visual diagrams:

• Architecture overviews
• Data flow through components
• Step-by-step process walkthroughs
• Before/after comparisons
• Matrix operations with concrete shapes
• Mathematical derivation steps

For each, write:

• Diagram name (e.g., "fig_mla_architecture")
• What it should show
• Type: architecture / flowchart / comparison / step-by-step / matrix-operation

Step 4: Save Research Notes

Save to: research/<topic-slug>.md

Structure:

# Research: <Topic Name>

## Quick Summary
(2-3 sentence overview)

## Core Concepts
(detailed notes)

## How It Works
(step-by-step technical breakdown)

## Mathematical Foundation
(key equations with explanations)

## Comparisons and Alternatives
(vs previous approaches, with numbers)

## Visual Opportunities
(list of 6-10 diagrams needed with descriptions)

## Running Example
(define the simple example we will use throughout:
 e.g., 4 tokens, specific dimensions, concrete values)

## Key Sources
- [Paper Name](url) - what we extracted from it
- [Blog Post](url) - what we extracted from it

Output

Save to research/.md and summarize key findings to user. Tell the user how many diagram opportunities were identified.