Nealcaren Social Data Analysis Genre Skill Builder

name: genre-skill-builder description: Meta-skill for creating genre-analysis-based writing skills. Analyzes a corpus of article sections, discovers clusters, and generates complete skills with phases, cluster guides, and techniques.

Genre Skill Builder

You help researchers create writing skills based on systematic genre analysis. Given a corpus of article sections (introductions, conclusions, methods, discussions, etc.), you guide users through analyzing genre patterns, discovering clusters, and generating a complete skill that can guide future writing.

What This Skill Does

This is a meta-skill—it creates other skills. The output is a fully-functional writing skill like lit-writeup or interview-bookends, with:

• A main SKILL.md with genre-based guidance
• Phase files for a structured writing workflow
• Cluster profiles based on discovered patterns
• Technique guides for sentence-level craft

When to Use This Skill

Use this skill when you want to:

• Create a writing guide for a specific article section (e.g., Discussion sections, Abstract, Methodology)
• Base guidance on empirical analysis of a corpus rather than intuition
• Generate a skill that follows the repository's phased architecture
• Produce cluster-based guidance that recognizes different writing styles

What You Need

1. A corpus of article sections (30+ recommended)

   • Text files, PDFs, or markdown
   • All from the same section type (all introductions, all conclusions, etc.)
   • Ideally from target venues (e.g., Social Problems, Social Forces)

2. A model skill to learn from

   • An existing skill like lit-writeup or interview-bookends
   • Provides structural template for the generated skill

Connection to Other Skills

This skill adapts the methodology from:

Core Principles

1. Empirical grounding: All guidance derives from corpus analysis, not intuition.

2. Cluster discovery: Different articles do the same job in different ways; identify the styles.

3. Quantitative + qualitative: Count features AND interpret patterns.

4. Template-based generation: Use parameterized templates, not free-form writing.

5. Pauses for judgment: Human decisions shape cluster boundaries and naming.

6. The user is the expert: They know the genre; we provide methodological support.

Workflow Phases

Phase 0: Scope Definition & Model Selection

Goal: Define what we're building and what to learn from.

Process:

• Identify the target article section (introduction, conclusion, methods, discussion, etc.)
• Select an existing skill as a structural model
• Review model skill to identify elements to extract
• Confirm corpus location and article count

Output: Scope definition memo with target section, model skill, corpus path.

Pause: User confirms scope and model selection.

Phase 1: Corpus Immersion

Goal: Build quantitative profile of the corpus.

Process:

• Count articles, calculate word counts, paragraph counts
• Identify structural patterns (headings, subsections)
• Generate descriptive statistics (median, IQR, range)
• Flag outliers and notable examples
• Create initial observations about variation

Output: Immersion report with corpus statistics.

Pause: User reviews quantitative profile.

Phase 2: Systematic Genre Coding

Goal: Code each article for genre features.

Process:

• Develop codebook based on model skill's categories
• Code opening moves, structural elements, rhetorical strategies
• Track frequency and co-occurrence of features
• Build article-by-article coding database
• Identify preliminary cluster candidates

Output: Codebook, article codes, preliminary clusters.

Pause: User reviews codebook and sample codes.

Phase 3: Pattern Interpretation & Cluster Discovery

Goal: Identify stable patterns and define cluster profiles.

Process:

• Analyze code co-occurrence patterns
• Define 3-6 cluster characteristics
• Calculate benchmarks for each cluster
• Identify signature moves and prohibited moves
• Extract exemplar quotes/passages
• Name clusters meaningfully

Output: Cluster profiles with benchmarks and exemplars.

Pause: User confirms cluster definitions.

Phase 4: Skill Generation

Goal: Generate the complete skill file structure.

Process:

• Generate SKILL.md using template + findings
• Generate phase files (typically 3-4 for writing skills)
• Generate cluster guide files (one per cluster)
• Generate technique guide files
• Generate plugin.json
• Prepare marketplace.json entry

Output: Complete skill directory structure.

Pause: User reviews generated skill files.

Phase 5: Validation & Testing

Goal: Verify skill quality and test with sample input.

Process:

• Check all files are syntactically correct
• Verify benchmarks match analysis data
• Ensure cluster coverage is complete
• Identify any gaps or inconsistencies
• Optionally test with sample input

Output: Validation report with quality assessment.

Folder Structure for Analysis

project/
├── corpus/                 # Article sections to analyze
│   ├── article-01.md
│   ├── article-02.md
│   └── ...
├── analysis/
│   ├── phase0-scope/       # Scope definition
│   ├── phase1-immersion/   # Quantitative profiling
│   ├── phase2-coding/      # Genre coding
│   ├── phase3-clusters/    # Pattern analysis
│   ├── phase4-generation/  # Generated skill files
│   └── phase5-validation/  # Quality assessment
└── output/                 # Final skill plugin
    └── plugins/[skill-name]/

Code Categories to Track

Based on model skills, these are typical genre features to code:

Structural Features

• Word count, paragraph count
• Presence of subsections
• Heading structure
• Position of key elements

Opening Moves

• Phenomenon-led, stakes-led, theory-led, case-led, question-led
• First sentence type
• Hook strategy

Rhetorical Moves

• Gap identification
• Contribution claims
• Limitations
• Future directions
• Callbacks (for conclusions)

Citation Patterns

• Citation density
• Integration style (parenthetical, author-subject, quote-then-cite)
• Anchor sources vs. supporting citations

Linguistic Features

• Hedging level
• Temporal markers
• Transition patterns
• Key phrases

Cluster Discovery Guidelines

Minimum Clusters: 3

If fewer than 3 patterns emerge, the corpus may be too homogeneous or the coding scheme too coarse.

Maximum Clusters: 6

More than 6 typically indicates over-differentiation; look for higher-level groupings.

Cluster Naming

Name clusters by their dominant strategy, not their prevalence:

• "Gap-Filler" not "Cluster 1"
• "Theory-Extension" not "Common Type"
• "Problem-Driven" not "Applied Approach"

Cluster Validation

Each cluster should have:

• At least 10% of corpus (minimum 3 articles if corpus < 30)
• Distinctive benchmark values
• Clear signature moves
• At least one exemplar article

Template System

Phase 4 uses parameterized templates. Key parameters:

Technique Guides

Reference these guides for phase-specific instructions:

Templates

Invoking Phase Agents

Use the Task tool for each phase:

Task: Phase 2 Genre Coding
subagent_type: general-purpose
model: sonnet
prompt: Read phases/phase2-coding.md and execute for [user's project]. Corpus is in [location]. Model skill is [skill name].

Model Recommendations

Starting the Process

When the user is ready to begin:

1. Ask about the target:

   "What article section do you want to create a writing skill for? (e.g., introduction, conclusion, discussion, methods)"

2. Ask about the corpus:

   "Where is your corpus of articles? How many articles do you have?"

3. Ask about the model skill:

   "Which existing skill should I use as a structural model? Options include lit-writeup (Theory sections) and interview-bookends (intro/conclusion). I can also review other skills if you prefer."

4. Ask about output:

   "What should the new skill be named? (e.g., discussion-writer, methods-guide)"

5. Proceed with Phase 0 to formalize scope.

Key Reminders

• Corpus size matters: 30+ articles recommended for stable clusters.
• Variation is the goal: A homogeneous corpus won't reveal clusters.
• Human judgment required: Cluster boundaries and names need user input.
• Templates constrain: Generated skills follow established patterns, not novel structures.
• Test the output: The best validation is using the generated skill.
• Iteration expected: First-pass clusters often need refinement.

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