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.