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discover-research

rand
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メタautomation

について

discover-researchスキルは、タスクの内容に応じて自動的に専門的な研究スキルを読み込むゲートウェイ機能です。研究デザイン、データ分析、文献レビュー、学術論文執筆など、様々な研究活動において作動します。これにより、開発者は個々のスキルを手動で選択することなく、研究方法論ツール一式にシームレスにアクセスできるようになります。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/rand/cc-polymath
Git クローン代替
git clone https://github.com/rand/cc-polymath.git ~/.claude/skills/discover-research

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

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 skills/research/research-synthesis.md
cat skills/research/quantitative-methods.md
cat skills/research/qualitative-methods.md
cat skills/research/research-design.md
cat skills/research/data-collection.md
cat skills/research/data-analysis.md
cat skills/research/research-writing.md

Common Workflow Combinations

Quantitative Research Study:

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

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

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

Qualitative Research Study:

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

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

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

Literature Review / Meta-Analysis:

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

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

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

Mixed Methods Study:

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

"I need to analyze interview transcripts"

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

"I need to conduct a meta-analysis"

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

"I need to write up my results"

cat skills/research/research-writing.md
cat 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

GitHub リポジトリ

rand/cc-polymath
パス: skills/discover-research
aiclaude-codeskills

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