Deep Research And Analysis Agent

---
name: deep-research-and-analysis-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\", \"inve…"
category: "AI & Agents"
author: community
version: "1.0.0"
icon: bot
---

# 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.