/deep-research

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.

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"
"[topic] expert interview"

# Use temporal qualifiers — always use the ACTUAL current year from <current_date>
"[topic] 2026"   # ← replace with real current year, never hardcode a past year
"[topic] latest"
"[topic] recent developments"

Temporal Awareness

**Always check `<current_date>` in your context before forming ANY search query.**

`<current_date>` gives you the full date: year, month, day, and weekday (e.g. `2026-02-28, Saturday`). Use the right level of precision depending on what the user is asking:

| User intent | Temporal precision needed | Example query | |---|---|---| | "today / this morning / just released" | **Month + Day** | `"tech news February 28 2026"` | | "this week" | **Week range** | `"technology releases week of Feb 24 2026"` | | "recently / latest / new" | **Month** | `"AI breakthroughs February 2026"` | | "this year / trends" | **Year** | `"software trends 2026"` |

**Rules:**

• When the user asks about "today" or "just released", use **month + day + year** in your search queries to get same-day results
• Never drop to year-only when day-level precision is needed — `"tech news 2026"` will NOT surface today's news
• Try multiple phrasings: numeric form (`2026-02-28`), written form (`February 28 2026`), and relative terms (`today`, `this week`) across different queries

❌ User asks "what's new in tech toda