Agent-Skills.md

Agent Skills: Deep Research Skill

Deep research on any topic combining real-time web search (Rube MCP) with multi-model consensus analysis (PAL MCP). Produces structured reports with sourced findings and confidence assessments.

UncategorizedID: tony363/superclaude/sc-research

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tony363/superclaude
Tony363License: MIT
171

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pnpm dlx add-skill https://github.com/Tony363/SuperClaude/tree/HEAD/.claude/skills/sc-research

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Skill Metadata

Name
sc-research
Description
Deep research on any topic combining real-time web search (Rube MCP) with multi-model consensus analysis (PAL MCP). Produces structured reports with sourced findings and confidence assessments.

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

  1. Parse - Extract topic, depth, output path, model count
  2. Decompose - Break topic into 3-7 sub-questions for comprehensive coverage
  3. Discover - Find web search tools via Rube MCP
  4. Search - Execute web research in parallel batches
  5. Analyze - Deep analysis of findings via PAL ThinkDeep
  6. Validate - Multi-model consensus via PAL Consensus
  7. Report - Generate structured markdown report
  8. 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:

  1. Record session_id — reuse for all subsequent Rube calls
  2. Check connection status for returned toolkits
  3. If no active connection, call RUBE_MANAGE_CONNECTIONS and present auth link
  4. Identify best tools for web search and URL content extraction
  5. 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:

  1. Parse and collect all results
  2. Identify most relevant URLs
  3. For medium/deep: extract full content from top 3-5 URLs
  4. 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