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

1. 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)

1. 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?

1. 4. Pattern extraction. What themes emerge across sources?

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

1. 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. 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?

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

CODEBLOCK5

1. 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. 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)

1. 2. For each solution:

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

1. 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.