research-agent

name

research-agent

description

Deep research and analysis agent for any topic. Use when the user wants to research a topic, analyze competitors, evaluate technologies, compare tools, investigate trends, do market research, or get a comprehensive analysis of anything. Triggers on phrases like "research", "investigate", "analyze", "compare", "what is", "tell me about", "look into", "deep dive", "competitor analysis", "market research", "technology evaluation", "find alternatives", "landscape analysis", "pros and cons", "should I use". Produces structured research reports with sources.

Research Agent — Deep Investigation on Any Topic

A structured research workflow that turns a vague question into a comprehensive analysis. 5 research modes, each with a clear output format. Supports web search, source evaluation, and structured reporting.

Research Modes

Mode	Trigger	Output
Quick	"What is X?" / "Tell me about X"	1-paragraph summary + 3 key facts
Deep Dive	"Research X" / "Deep dive into X"	Full analysis report
Compare	"Compare X vs Y" / "X or Y?"	Comparison matrix + recommendation
Landscape	"What's out there for X?" / "Alternatives to X"	Market map + positioning
Evaluate	"Should we use X?" / "Is X worth it?"	Decision framework with scoring

How to Use

Quick Research (30 seconds)

"What is gstack?"
"Tell me about Claude Code skills"

→ Web search, extract key facts, 1-paragraph summary. No fluff.

Deep Dive (2-5 minutes)

"Research the AI coding agent landscape"
"Deep dive into Agent Skills standard"

→ Spawn subagent (Sonnet) with the Deep Dive prompt. Searches multiple sources, cross-references, identifies patterns, writes RESEARCH.md.

Compare (1-3 minutes)

"Claude Code vs Cursor vs Codex"
"RICE vs Kano vs ICE for prioritization"
"Notion vs Linear vs Jira"

→ Side-by-side comparison table with scoring across key dimensions. Includes a recommendation with reasoning.

Landscape Analysis (3-5 minutes)

"What open source projects exist for X?"
"Map the competitive landscape for X"
"What tools do PMs use for X?"

→ Categorized map of existing solutions. For each: what it does, what it misses, where the gap is.

Evaluate (2-3 minutes)

"Should we build on X or Y?"
"Is it worth adopting X?"
"Pros and cons of using X for our case"

→ Decision matrix scoring across dimensions (cost, effort, risk, fit, longevity). Recommendation with confidence level.

Phase Details

Deep Dive Prompt

Spawn a subagent (Sonnet) with this research methodology:

1. Define the question. Restate the research question. What specifically are we trying to find out?

2. Source gathering. Search for:

   • Official docs / primary sources (most reliable)
   • Community discussions (Reddit, HN, Discord — real user opinions)
   • Technical analysis (blog posts, benchmarks, comparisons)
   • GitHub metrics (stars, activity, issues, contributors)
   • Commercial context (funding, team, business model)

3. Source evaluation. For each source:

   • Credibility: official vs community vs opinion
   • Recency: when was this published/updated?
   • Bias: does the author have a stake in the outcome?

4. Pattern extraction. What themes emerge across sources?

   • Points of agreement (high confidence)
   • Points of disagreement (needs further investigation)
   • Gaps in available information

5. Structured output. Write RESEARCH.md with:

   • Executive summary (3-5 sentences)
   • Key findings (numbered, with sources)
   • Detailed analysis (organized by theme)
   • Gaps and caveats (what we couldn't verify)
   • Recommendation (if applicable)
   • Sources (with URLs)

Compare Prompt

For comparing N items across M dimensions:

1. Define comparison axis. What dimensions matter for this decision?

   • Functional: what can it do?
   • Performance: how fast/reliable?
   • Cost: pricing model, free tier?
   • Ecosystem: integrations, community, docs?
   • Maturity: how battle-tested?

2. Score each item (1-5 per dimension):

   | Dimension     | Option A | Option B | Option C |
   |---------------|----------|----------|----------|
   | Feature set   | ⭐⭐⭐⭐   | ⭐⭐⭐     | ⭐⭐⭐⭐⭐  |
   | Ease of use   | ⭐⭐⭐⭐⭐  | ⭐⭐⭐     | ⭐⭐       |
   

3. Context-specific recommendation. Not "A is best" but "A is best IF you need X, B if you need Y."

Landscape Prompt

For mapping a space:

1. Categorize solutions:

   • Direct competitors (same approach, same users)
   • Adjacent tools (different approach, overlapping use case)
   • Workarounds (not products, but how people solve it today)
   • Emerging (new, not proven yet)

2. For each solution:

   • What it does (1 sentence)
   • What it does well (strength)
   • What it misses (gap)
   • Who should use it (ideal user)

3. Identify the gap. Where is nobody doing a good job? That's the opportunity.

Output Files

• RESEARCH.md — Deep dive report (full analysis with sources)
• Comparison results go to stdout (capture in conversation)
• Landscape maps go to stdout or LANDSCAPE.md if long

Model Selection

Mode	Model	Why
Quick	Haiku	Simple lookup, fast answer
Deep Dive	Sonnet	Needs reasoning, source evaluation
Compare	Sonnet	Needs judgment for scoring
Landscape	Sonnet	Needs categorization and pattern recognition
Evaluate	Sonnet	Needs decision-making framework

Tips

• Be specific. "Research AI" is too broad. "Research AI coding agents for solo developers" is actionable.
• State your goal. "I need to decide between X and Y" gives the research direction.
• Time-box it. "Give me the top 5, not top 50" keeps it focused.
• Ask for sources. "Show me where you found this" for verification.