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deep-research

Use this skill instead of WebSearch for ANY question requiring web research.

Skill by jiaqiang000

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

Use cases: writing, research

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What's inside

Skill name
deep-research
Description
Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.
License
MIT
Instructions
195 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Deep Research Skill

## Overview

This skill provides a systematic methodology for conducting thorough web research. **Load this skill BEFORE starting any content generation task** to ensure you gather sufficient information from multiple angles, depths, and sources.

## When to Use This Skill

**Always load this skill when:**

### Research Questions
- User asks "what is X", "explain X", "research X", "investigate X"
- User wants to understand a concept, technology, or topic in depth
- The question requires current, comprehensive information from multiple sources
- A single web search would be insufficient to answer properly

### Content Generation (Pre-research)
- Creating presentations (PPT/slides)
- Creating frontend designs or UI mockups
- Writing articles, reports, or documentation
- Producing videos or multimedia content
- Any content that requires real-world information, examples, or current data

## Core Principle

**Never generate content based solely on general knowledge.** The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough.

## Research Methodology

### Phase 1: Broad Exploration

Start with broad searches to understand the landscape:

1. **Initial Survey**: Search for the main topic to understand the overall context
2. **Identify Dimensions**: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration
3. **Map the Territory**: Note different perspectives, stakeholders, or viewpoints that exist

Example:
```
Topic: "AI in healthcare"
Initial searches:
- "AI healthcare applications 2024"
- "artificial intelligence medical diagnosis"
- "healthcare AI market trends"

Identified dimensions:
- Diagnostic AI (radiology, pathology)
- Treatment recommendation systems
- Administrative automation
- Patient monitoring
- Regulatory landscape
- Ethical considerations
```

### Phase 2: Deep Dive

For each important dimension identified, conduct targeted research:

1. **Specific Queries**: Search with precise keywords for each subtopic
2. **Multiple Phrasings**: Try different keyword combinations and phrasings
3. **Fetch Full Content**: Use `web_fetch` to read important sources in full, not just snippets
4. **Follow References**: When sources mention other important resources, search for those too

Example:
```
Dimension: "Diagnostic AI in radiology"
Targeted searches:
- "AI radiology FDA approved systems"
- "chest X-ray AI detection accuracy"
- "radiology AI clinical trials results"

Then fetch and read:
- Key research papers or summaries
- Industry reports
- Real-world case studies
```

### Phase 3: Diversity & Validation

Ensure comprehensive coverage by seeking diverse information types:

| Information Type | Purpose | Example Searches |
|-----------------|---------|------------------|
| **Facts & Data** | Concrete evidence | "statistics", "data", "numbers", "market size" |
| **Examples & Cases** | Real-world applications | "case study", "example", "implementation" |
| **Expert Opinions** | Authority perspectives | "expert analysis", "interview", "commentary" |
| **Trends & Predictions** | Future direction | "trends 2024", "forecast", "future of" |
| **Comparisons** | Context and alternatives | "vs", "comparison", "alternatives" |
| **Challenges & Criticisms** | Balanced view | "challenges", "limitations", "criticism" |

### Phase 4: Synthesis Check

Before proceeding to content generation, verify:

- [ ] Have I searched from at least 3-5 different angles?
- [ ] Have I fetched and read the most important sources in full?
- [ ] Do I have concrete data, examples, and expert perspectives?
- [ ] Have I explored both positive aspects and challenges/limitations?
- [ ] Is my information current and from authoritative sources?

**If any answer is NO, continue researching before generating content.**

## Search Strategy Tips

### Effective Query Patterns

```
# Be specific with context
❌ "AI trends"
✅ "enterprise AI adoption trends 2024"

# Include authoritative source hints
"[topic] research paper"
"[topic] McKinsey report"
"[topic] industry analysis"

# Search for specific content types
"[topic] case study"
"[topic] statistics"

From jiaqiang000/AI-Teacher-Copilot, MIT.

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Last commit (this folder)
Aug 3, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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deep-research skill. Agent Mart. https://agentmart.dev/skills/jiaqiang000-deep-research (last updated 2026-10-24)