---

SKILL-SPECIFIC GUIDANCE

When to Use This Skill

• Visualizing research gaps identified from literature synthesis
• Creating 2D/3D plots of method vs. dataset coverage
• Generating heatmaps showing unexplored method combinations
• Producing publication-ready figures for grant proposals
• Analyzing trends in research focus over time

When NOT to Use This Skill

• When raw text descriptions are sufficient (no visualization needed)
• Single-dimensional gaps (simple list format works)
• When time constrained (<30 min, skip visualization)
• Non-academic audiences unfamiliar with research visualizations

Success Criteria

• High-resolution publication-ready figures (300+ DPI)
• Clear axis labels, legends, and titles
• Color scheme accessible (colorblind-friendly)
• Gap areas clearly highlighted (red for unexplored, green for saturated)
• Source data included (CSV/JSON for reproducibility)

Edge Cases & Limitations

• Too many dimensions: use PCA/t-SNE for dimensionality reduction
• Sparse data: adjust visualization type (scatter plot vs heatmap)
• Unclear gaps: add annotations explaining why areas unexplored
• Complex taxonomy: simplify groupings, use hierarchical clustering
• Overlapping labels: increase figure size, rotate text, use abbreviations

Critical Guardrails

• NEVER use misleading visualizations (cherry-picked axes, truncated scales)
• ALWAYS include colorblind-friendly palettes (viridis, not rainbow)
• NEVER omit axis labels, units, or legends
• ALWAYS provide source data for reproducibility
• NEVER use 3D when 2D suffices (avoid chart junk)

Evidence-Based Validation

• Validate data accuracy: cross-check plotted values against source tables
• Test colorblind accessibility: use simulators (Coblis, Color Oracle)
• Verify label clarity: are all abbreviations explained in caption?
• Check resolution: export at 300+ DPI, zoom to verify sharpness
• Confirm reproducibility: can figure be regenerated from provided source data?

---

name: research-gap-visualizer description: Create visual maps of research gaps from literature analysis, showing what has been studied, what is missing, and where opportunities exist. Generates gap matrices, research landscape diagrams, and opportunity maps. Use after literature synthesis to visualize the state of research and identify promising directions. version: 1.0.0 category: research tags:

• research
• gaps
• visualization
• literature
• analysis author: ruv mcp_servers: required: [memory-mcp] optional: [sequential-thinking] auto_enable: true

---

Research Gap Visualizer

Purpose

Transform literature analysis into visual gap maps that clearly show what has been studied, what is missing, and where research opportunities exist. Provides visual evidence for research motivation in proposals and manuscripts.

When to Use This Skill

Activate this skill when:

• Completed literature synthesis and need to identify gaps
• Writing research motivation section (need visual evidence)
• Preparing grant proposals (need to show novelty)
• Planning research direction (need to see landscape)
• Defending thesis topic selection

DO NOT use this skill for:

• Initial literature search (use literature-synthesis first)
• Idea generation without literature context (use rapid-idea-generator)
• Detailed methodology planning (use research-driven-planning)

Visual Output Types

1. Gap Matrix

2D matrix showing which combinations of methods/domains have been studied vs unexplored.

2. Research Landscape Map

Bubble/scatter diagram showing density of research in different areas.

3. Temporal Gap Analysis

Timeline showing when topics were studied and which are stale.

4. Method-Application Matrix

Which methods have been applied to which problems.

5. Opportunity Quadrant

2x2 matrix of feasibility vs impact for potential research directions.

Input Contract

input:
  literature_data: object (required)
    papers: array[object]
      title: string
      year: number
      methods: array[string]
      domains: array[string]
      key_findings: string

  analysis_type: enum[gap_matrix, landscape, temporal, method_application, opportunity] (required)

  dimensions:
    x_axis: string  # e.g., "methods", "year", "domain"
    y_axis: string  # e.g., "application", "dataset", "metric"

  filters:
    year_range: [start_year, end_year] (optional)
    min_papers: number (default: 1)

  output_format: enum[mermaid, ascii, markdown, graphviz] (default: mermaid)

Output Contract

output:
  visualization:
    type: string
    format: string
    code: string  # Mermaid/GraphViz/ASCII code

  gap_analysis:
    total_cells: number
    studied_cells: number
    gap_cells: number
    gap_percentage: number

  identified_gaps:
    high_priority: array[object]
      description: string
      evidence: string  # Why this is a gap
      opportunity_score: number
    medium_priority: array[object]
    low_priority: array[object]

  recommendations:
    top_opportunities: array[string]
    rationale: array[string]

  metadata:
    papers_analyzed: number
    dimensions_used: array[string]
    generation_time: number

SOP Phase 1: Literature Data Parsing

Extract structured data from literature synthesis:

## Literature Parsing

**Papers Analyzed**: [N]

**Extracted Dimensions**:
- Methods: [list of unique methods]
- Domains: [list of unique domains/applications]
- Datasets: [list of unique datasets]
- Years: [range]

**Dimension Frequency**:
| Dimension | Count | Percentage |
|-----------|-------|------------|
| [Method 1] | [N] | [%] |
| [Method 2] | [N] | [%] |

SOP Phase 2: Gap Matrix Generation

Create the gap matrix visualization:

Gap Matrix Template (Markdown)

## Research Gap Matrix: [X-Axis] vs [Y-Axis]

|           | [Y1] | [Y2] | [Y3] | [Y4] | [Y5] |
|-----------|------|------|------|------|------|
| **[X1]**  | [N]  | [N]  | GAP  | [N]  | GAP  |
| **[X2]**  | GAP  | [N]  | [N]  | GAP  | GAP  |
| **[X3]**  | [N]  | GAP  | [N]  | [N]  | GAP  |
| **[X4]**  | GAP  | GAP  | GAP  | [N]  | [N]  |

**Legend**:
- [N] = Number of papers
- GAP = No papers found (research opportunity)
- Shading: Darker = more studied

**Gap Statistics**:
- Total cells: [N]
- Studied cells: [N] ([%])
- Gap cells: [N] ([%])

Gap Matrix Template (Mermaid Heatmap)

%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#ff6b6b'}}}%%
quadrantChart
    title Research Gap Analysis
    x-axis Low Method Complexity --> High Method Complexity
    y-axis Low Application Novelty --> High Application Novelty
    quadrant-1 High opportunity
    quadrant-2 Competitive space
    quadrant-3 Low priority
    quadrant-4 Niche opportunities
    Topic A: [0.3, 0.6]
    Topic B: [0.45, 0.23]
    Topic C: [0.57, 0.69]
    Topic D: [0.78, 0.34]
    Gap Area 1: [0.15, 0.85]
    Gap Area 2: [0.85, 0.75]

SOP Phase 3: Research Landscape Diagram

Visualize research density:

flowchart TB
    subgraph HighlyStudied["Highly Studied Areas"]
        A[Deep Learning + NLP<br/>150 papers]
        B[CNN + Image Classification<br/>200 papers]
    end

    subgraph ModeratelyStudied["Moderately Studied"]
        C[Transformers + Vision<br/>45 papers]
        D[GNN + Molecules<br/>30 papers]
    end

    subgraph UnderStudied["Under-Studied (GAPS)"]
        E[Neuro-symbolic + Robotics<br/>3 papers]
        F[Federated + Medical<br/>5 papers]
    end

    subgraph Unexplored["Unexplored (OPPORTUNITIES)"]
        G[Quantum ML + Drug Discovery<br/>0 papers]
        H[Causal + Time Series<br/>0 papers]
    end

    style HighlyStudied fill:#ff6b6b
    style ModeratelyStudied fill:#ffd93d
    style UnderStudied fill:#6bcb77
    style Unexplored fill:#4d96ff

SOP Phase 4: Temporal Gap Analysis

Show research trends over time:

## Temporal Research Landscape

### Publication Trend by Topic

Year	Topic A	Topic B	Topic C	Topic D
2019	***	**	*
2020	****	***	*
2021	*****	****	**	*
2022	****	*****	***	**
2023	***	****	****	***
2024	**	***	*****	****

**Trend Analysis**:
- **Rising**: Topic C, Topic D (opportunity areas)
- **Stable**: Topic B (competitive but active)
- **Declining**: Topic A (may be saturated)
- **Stale**: [Topics not updated since 2021]

SOP Phase 5: Opportunity Quadrant

Generate prioritized opportunity map:

quadrantChart
    title Research Opportunity Analysis
    x-axis Low Feasibility --> High Feasibility
    y-axis Low Impact --> High Impact
    quadrant-1 Pursue Aggressively
    quadrant-2 Strategic Investment
    quadrant-3 Deprioritize
    quadrant-4 Quick Wins
    Gap 1: [0.8, 0.9]
    Gap 2: [0.3, 0.85]
    Gap 3: [0.7, 0.4]
    Gap 4: [0.2, 0.3]
    Gap 5: [0.9, 0.6]

SOP Phase 6: Gap Prioritization

Rank identified gaps by research potential:

## Prioritized Research Gaps

### High Priority (Pursue)
1. **[Gap Description]**
   - Evidence: [Why this is a gap]
   - Opportunity Score: [0-1]
   - Feasibility: [high/medium/low]
   - Impact: [high/medium/low]
   - Suggested Approach: [Brief methodology]

### Medium Priority (Consider)
2. **[Gap Description]**
   ...

### Low Priority (Monitor)
3. **[Gap Description]**
   ...

Example Execution

Input (from literature-synthesis output):

literature_data:
  papers:
    - title: "Deep Learning for Drug Discovery"
      year: 2023
      methods: ["CNN", "GNN"]
      domains: ["drug_discovery", "molecular"]
    - title: "Transformers in Medical Imaging"
      year: 2024
      methods: ["Transformer", "Attention"]
      domains: ["medical_imaging", "diagnosis"]
    # ... more papers

analysis_type: gap_matrix
dimensions:
  x_axis: methods
  y_axis: domains

Output:

## Research Gap Matrix: Methods vs Application Domains

|                | Drug Discovery | Medical Imaging | Genomics | Clinical NLP |
|----------------|----------------|-----------------|----------|--------------|
| **CNN**        | 23             | 45              | 12       | 8            |
| **GNN**        | 15             | 3               | 8        | **GAP**      |
| **Transformer**| 5              | 18              | **GAP**  | 22           |
| **Diffusion**  | **GAP**        | 7               | **GAP**  | **GAP**      |

## Identified Gaps (Ranked)

### High Priority
1. **Diffusion Models for Drug Discovery**
   - Evidence: 0 papers found; diffusion excels at generation
   - Opportunity Score: 0.92
   - Rationale: Diffusion models successful in images, untapped in molecules

2. **Graph Neural Networks for Clinical NLP**
   - Evidence: 0 papers; GNNs model document relationships
   - Opportunity Score: 0.78

### Medium Priority
3. **Transformers for Genomics**
   - Evidence: 0 papers in our corpus (may exist elsewhere)
   - Opportunity Score: 0.65

Integration Points

Receives From

• literature-synthesis: Paper data for gap analysis
• baseline-replication: Validation of gap existence

Feeds Into

• rapid-idea-generator: Gap-informed ideation
• rapid-manuscript-drafter: Visual evidence for motivation
• research-driven-planning: Strategic direction

Memory Storage

# Store gap analysis for future reference
npx claude-flow@alpha memory store \
  "gap_analysis_[topic]" \
  "[gap_data_json]" \
  --namespace "research/gaps"

Success Criteria

• All dimensions extracted from literature data
• Gap matrix accurately reflects paper coverage
• Gaps prioritized by opportunity score
• Visual outputs render correctly
• Recommendations actionable and evidence-based
• Integration with manuscript writing enabled

Feature Comparison

Feature	Basic Tools	This Skill
Gap identification	Basic	Multi-dimensional
Evidence for gaps	None	Paper counts, citations
Visualization	Basic diagrams	Matrix, landscape, temporal, quadrant
Prioritization	None	Opportunity scoring
Integration	Standalone	Feeds into manuscript, planning
Transparency	Black box	Full methodology shown

---

Version: 1.0.0 Category: Research / Gap Analysis Prerequisites: literature-synthesis output Output Formats: Mermaid, Markdown, ASCII, GraphViz