Discover Research

Research Skills Discovery

Auto-Activation

This skill is automatically activated when your task involves:

• Research synthesis, literature reviews, meta-analysis
• Quantitative research, statistical analysis, surveys, experiments
• Qualitative research, interviews, ethnography, case studies
• Study design, hypothesis testing, sampling strategies
• Data collection, survey design, interview protocols
• Data analysis, coding, statistical tests, visualization
• Research writing, academic papers, citations, reporting

Available Research Skills

Core Methodology Skills

1. research-synthesis - Synthesizing information and conducting meta-analysis

• Narrative synthesis approaches
• Meta-analysis with Python implementation
• Thematic synthesis of qualitative findings
• Evidence mapping and gap analysis
• GRADE framework for quality assessment
• Use when: Integrating findings across studies

2. quantitative-methods - Quantitative research and statistical analysis

• Experimental design with power analysis
• Survey methods and analysis
• Hypothesis testing framework
• Regression analysis with diagnostics
• Effect sizes and reporting
• Use when: Testing hypotheses with numerical data

3. qualitative-methods - Qualitative research approaches

• In-depth interview protocols
• Thematic analysis (6 phases)
• Grounded theory coding
• Case study research design
• Quality criteria and rigor
• Use when: Exploring experiences and meanings

4. research-design - Planning and designing research studies

• Research question formulation (FINER criteria)
• Validity threat analysis
• Sampling strategies with Python tools
• Experimental control frameworks
• Design quality assessment
• Use when: Planning a new study from scratch

Implementation Skills

5. data-collection - Methods for gathering research data

• Survey instrument design and validation
• Interview protocol development
• Observation methods and field notes
• Data quality control frameworks
• Response rate optimization
• Use when: Implementing data collection

6. data-analysis - Analyzing quantitative and qualitative data

• Comprehensive descriptive statistics
• Inferential testing with full reporting
• Systematic qualitative coding
• Thematic development process
• Publication-ready visualizations
• Use when: Making sense of collected data

7. research-writing - Writing research papers and reports

• IMRAD structure with guidelines
• APA statistical reporting
• Citation management
• Argument construction
• Peer review response strategies
• Use when: Communicating research findings

Loading Skills

Load Individual Skills

# From skills directory
cat ~/.claude/skills/research/research-synthesis.md
cat ~/.claude/skills/research/quantitative-methods.md
cat ~/.claude/skills/research/qualitative-methods.md
cat ~/.claude/skills/research/research-design.md
cat ~/.claude/skills/research/data-collection.md
cat ~/.claude/skills/research/data-analysis.md
cat ~/.claude/skills/research/research-writing.md

Common Workflow Combinations

Quantitative Research Study:

# Planning phase
cat ~/.claude/skills/research/research-design.md

# Data collection
cat ~/.claude/skills/research/data-collection.md
cat ~/.claude/skills/research/quantitative-methods.md

# Analysis and reporting
cat ~/.claude/skills/research/data-analysis.md
cat ~/.claude/skills/research/research-writing.md

Qualitative Research Study:

# Planning phase
cat ~/.claude/skills/research/research-design.md

# Data collection
cat ~/.claude/skills/research/data-collection.md
cat ~/.claude/skills/research/qualitative-methods.md

# Analysis and reporting
cat ~/.claude/skills/research/data-analysis.md
cat ~/.claude/skills/research/research-writing.md

Literature Review / Meta-Analysis:

# Synthesis phase
cat ~/.claude/skills/research/research-synthesis.md

# If including quantitative synthesis
cat ~/.claude/skills/research/quantitative-methods.md

# Writing phase
cat ~/.claude/skills/research/research-writing.md

Mixed Methods Study:

# All methods
cat ~/.claude/skills/research/research-design.md
cat ~/.claude/skills/research/quantitative-methods.md
cat ~/.claude/skills/research/qualitative-methods.md
cat ~/.claude/skills/research/data-collection.md
cat ~/.claude/skills/research/data-analysis.md
cat ~/.claude/skills/research/research-writing.md

Progressive Loading

Load skills progressively based on research phase:

Phase 1: Planning (Load 1-2 skills)

• research-design (always)
• quantitative-methods OR qualitative-methods (based on approach)

Phase 2: Collection (Add 1-2 skills)

• data-collection (always)
• Keep loaded: specific method skill

Phase 3: Analysis (Add 1 skill)

• data-analysis (always)
• Keep loaded: method and collection skills for reference

Phase 4: Writing (Add 1 skill, can unload others)

• research-writing (always)
• research-synthesis (if synthesizing literature)
• Keep one method skill for reporting details

Phase 5: Synthesis (If conducting review)

• research-synthesis (load early)
• quantitative-methods (if meta-analysis)
• qualitative-methods (if thematic synthesis)

Decision Tree

Research Task
    ↓
Conducting new study?
    YES → Load research-design
        ↓
        Quantitative approach?
            YES → Load quantitative-methods + data-collection
        Qualitative approach?
            YES → Load qualitative-methods + data-collection
        Mixed methods?
            YES → Load both methods + data-collection
        ↓
        Ready to analyze?
            YES → Load data-analysis
        ↓
        Ready to write?
            YES → Load research-writing

    NO → Synthesizing existing research?
        YES → Load research-synthesis
            ↓
            Quantitative synthesis (meta-analysis)?
                YES → Also load quantitative-methods
            Qualitative synthesis?
                YES → Also load qualitative-methods
            ↓
            Ready to write?
                YES → Load research-writing

Context-Aware Loading

Based on keywords in your task, these skills auto-load:

Keywords → Skills Mapping:

• "literature review", "meta-analysis", "systematic review" → research-synthesis
• "survey", "experiment", "hypothesis", "statistical" → quantitative-methods
• "interview", "ethnography", "case study", "lived experience" → qualitative-methods
• "study design", "sampling", "validity", "research plan" → research-design
• "questionnaire", "data collection", "measurement" → data-collection
• "analyze data", "coding", "statistical test", "thematic" → data-analysis
• "write paper", "manuscript", "citation", "peer review" → research-writing

Related Skill Categories

• statistics: Advanced statistical techniques
• data-science: Machine learning and big data approaches
• visualization: Advanced data visualization
• academic-writing: General academic writing skills
• scientific-computing: Python/R for research computing

Quick Start Examples

"I need to design a survey study"

cat ~/.claude/skills/research/research-design.md
cat ~/.claude/skills/research/data-collection.md
cat ~/.claude/skills/research/quantitative-methods.md

"I need to analyze interview transcripts"

cat ~/.claude/skills/research/qualitative-methods.md
cat ~/.claude/skills/research/data-analysis.md

"I need to conduct a meta-analysis"

cat ~/.claude/skills/research/research-synthesis.md
cat ~/.claude/skills/research/quantitative-methods.md

"I need to write up my results"

cat ~/.claude/skills/research/research-writing.md
cat ~/.claude/skills/research/data-analysis.md

Best Practices

1. Start with design: Load research-design first when planning new studies
2. Method-specific loading: Load only the method skill you need (quant OR qual)
3. Progressive addition: Add skills as you progress through research phases
4. Unload when done: Unload skills from completed phases to manage context
5. Keep writing loaded: research-writing useful throughout for documentation

Skill Maintenance

All research skills follow these standards:

• Practical code examples (Python primary, R when appropriate)
• Real-world templates and protocols
• Best practices and anti-patterns
• Cross-references to related skills
• 250-400 lines optimized for context efficiency

Integration with Other Skills

Research skills integrate well with:

• python-data-science: For advanced analysis
• python-visualization: For publication graphics
• academic-latex: For paper formatting
• git-workflow: For research project management
• reproducibility: For reproducible research practices