Imported from tony363/superclaude (.claude/skills/sc-research/SKILL.md). Install upstream with npx skills add tony363/superclaude --skill sc-research. Copyright stays with the author.

Deep Research Skill

Conduct comprehensive research on any topic by combining real-time web search (via Rube MCP) with multi-model deep analysis and consensus synthesis (via PAL MCP). Produces structured research reports with sourced findings, cross-validated analysis, and confidence assessments.

Quick Start

# Quick overview
/sc:research "quantum computing developments" --depth shallow

# Balanced research (default)
/sc:research "Impact of AI regulation on open-source development"

# Exhaustive deep dive saved to file
/sc:research "carbon capture technologies" --depth deep --output reports/carbon.md

# More models for consensus
/sc:research "PostgreSQL vs CockroachDB for write-heavy workloads" --models 4

Behavioral Flow

Parse - Extract topic, depth, output path, model count
Decompose - Break topic into 3-7 sub-questions for comprehensive coverage
Discover - Find web search tools via Rube MCP
Search - Execute web research in parallel batches
Analyze - Deep analysis of findings via PAL ThinkDeep
Validate - Multi-model consensus via PAL Consensus
Report - Generate structured markdown report
Output - Save to file or display in console

Flags

Flag	Type	Default	Description
`--depth`	string	medium	Research depth: shallow, medium, deep
`--output`	string	-	Save report to file path
`--models`	int	3	Number of models for consensus (2-5)

Depth Levels

Depth	Sub-questions	Searches/question	Follow-ups
shallow	2-3	1	0
medium	3-5	1-2	1 per gap
deep	5-7	2-3	2-3 per gap

Phase 1: Parse and Plan

Extract topic from arguments. Decompose into sub-questions that provide comprehensive coverage when answered together.

Present research plan before proceeding:

Research Topic: <topic>
Depth: <level>
Sub-questions:
  1. <sub-question>
  2. <sub-question>
  ...
Estimated searches: ~N

Phase 2: Discover Search Tools

Use mcp__rube__RUBE_SEARCH_TOOLS to find web search and URL extraction tools:

RUBE_SEARCH_TOOLS:
  session: { generate_id: true }
  queries:
    - use_case: "search the web for information about a topic"
    - use_case: "scrape and extract content from a web page URL"

From the response:

Record session_id — reuse for all subsequent Rube calls
Check connection status for returned toolkits
If no active connection, call RUBE_MANAGE_CONNECTIONS and present auth link
Identify best tools for web search and URL content extraction
If tools return schemaRef, call RUBE_GET_TOOL_SCHEMAS for full schemas

Phase 3: Execute Web Research

Batch independent searches in parallel (up to 5 per call) via RUBE_MULTI_EXECUTE_TOOL:

Search query formulation:

Rephrase sub-questions as effective search queries
Use specific, factual language
For controversial topics, search multiple perspectives explicitly
Include date qualifiers if recency matters

After initial searches:

Parse and collect all results
Identify most relevant URLs
For medium/deep: extract full content from top 3-5 URLs
Identify information gaps

For medium/deep — follow-up searches:

Generate refined queries targeting gaps
Execute follow-up searches
Extract additional URL content

Organize raw findings:

Sub-question 1: <question>
  Sources:
    - [Source Title](URL) - Key finding: <summary>
  Gaps: <what's still unclear>

Sub-question 2: <question>
  Sources:
    - ...

Phase 4: Deep Analysis (PAL ThinkDeep)

Use mcp__pal__thinkdeep for systematic analysis:

Step 1 — Analyze:

Identify key themes and patterns across sources
Flag contradictions between sources
Assess source credibility and biases
Identify well-supported vs. poorly-supported claims
Note significant information gaps
Synthesize preliminary narrative

Step 2 — Refine:

Resolve contradictions with evidence-based reasoning
Rank findings by confidence (high/medium/low)
Produce structured outline for final report
Identify 3-5 most important takeaways

Phase 5: Multi-Model Consensus (PAL Consensus)

5a. Discover Models

Call mcp__pal__listmodels. Select top N by score, preferring different providers for diversity.

5b. Run Consensus

Use mcp__pal__consensus with for/against/neutral stances:

Stance	Purpose
for	Evaluate findings charitably, look for strengths
against	Critically evaluate, look for weaknesses and gaps
neutral	Balanced evaluation, weigh strengths and weaknesses

5c. Incorporate Consensus

Synthesize:

Areas of agreement — high confidence findings
Areas of disagreement — flag for nuance
Missing perspectives — gaps identified by any model
Confidence adjustments — raise/lower based on feedback

Phase 6: Generate Report

# Deep Research Report: <Topic>

**Generated**: <date>
**Depth**: <shallow|medium|deep>
**Sources consulted**: <N>
**Models consulted**: <list>

---

## Executive Summary

<3-5 paragraph synthesis for general audience>

---

## Research Question

<Original topic and how it was decomposed>

---

## Key Findings

### Finding 1: <Title>
**Confidence**: High | Medium | Low
**Consensus**: Agreed | Mixed | Disputed

<Detailed finding with inline source citations>

**Sources**: [Source 1](url), [Source 2](url)

---

## Analysis

### Themes and Patterns
<Cross-cutting themes across sources>

### Contradictions and Debates
<Where sources/models disagreed>

### Information Gaps
<What remains unclear>

---

## Model Consensus

| Model | Stance | Confidence | Key Feedback |
|-------|--------|------------|--------------|
| <model_1> | For | X/10 | <summary> |
| <model_2> | Against | X/10 | <summary> |
| <model_3> | Neutral | X/10 | <summary> |

**Agreement Areas**: <where all models agreed>
**Divergent Views**: <where models differed>

---

## Sources

| # | Title | URL | Relevance |
|---|-------|-----|-----------|
| 1 | <title> | <url> | <contribution> |

---

## Methodology

1. **Web search** via Rube MCP (<N> searches)
2. **Deep analysis** via PAL ThinkDeep
3. **Multi-model consensus** via PAL Consensus (<N> models)

Phase 7: Output

If --output <filepath>: Write report to file, confirm path
Otherwise: Display full report in console

End with summary:

Research complete:
- Topic: <topic>
- Sources: <N> web sources
- Models: <N> reached consensus
- Confidence: <overall assessment>
- Key takeaway: <1-sentence summary>

MCP Integration

PAL MCP

Tool	Phase	Purpose
`mcp__pal__thinkdeep`	Analysis	Multi-stage hypothesis testing
`mcp__pal__consensus`	Validation	Multi-model cross-validation
`mcp__pal__listmodels`	Discovery	Available models for consensus
`mcp__pal__challenge`	Validation	Critical thinking on controversial claims

Rube MCP

Tool	Phase	Purpose
`mcp__rube__RUBE_SEARCH_TOOLS`	Discovery	Find search/scraping tools
`mcp__rube__RUBE_GET_TOOL_SCHEMAS`	Discovery	Load full schemas if needed
`mcp__rube__RUBE_MULTI_EXECUTE_TOOL`	Search	Parallel web searches
`mcp__rube__RUBE_MANAGE_CONNECTIONS`	Auth	Connect search integrations
`mcp__rube__RUBE_REMOTE_BASH_TOOL`	Processing	Handle large response data

Error Handling

Scenario	Action
No search tools found	Fall back to `WebFetch` for direct URLs
Connection not active	Call `RUBE_MANAGE_CONNECTIONS`, present auth link
Search returns no results	Reformulate with broader terms
Tool schema missing	Call `RUBE_GET_TOOL_SCHEMAS` first
PAL MCP unavailable	Skip consensus, report from web research only
ThinkDeep fails	Continue with raw findings
Rate limiting	Reduce parallel batch size to 2-3

Guardrails

Present research plan before executing searches
Preserve all source URLs for verifiability
Search multiple perspectives for controversial topics
--depth deep can make 15-25+ API calls — use judiciously
If Rube unavailable but PAL available, degrade to analysis-only via WebFetch

Tool Coordination

WebFetch - Fallback URL fetching when Rube unavailable
Write - Save report to file
Bash - File operations
PAL MCP - Analysis, consensus, challenge
Rube MCP - Web search, URL extraction

sc-research

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