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.