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

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)

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:

Skill What We Borrow

interview-analyst Systematic coding approach (Phases 1-3)

lit-writeup Cluster-based writing guidance structure

interview-bookends Benchmarks and coherence checking

Core Principles

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

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

Quantitative + qualitative: Count features AND interpret patterns.

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

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

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:

Parameter Source

{{skill_name}}

Phase 0 user input

{{target_section}}

Phase 0 user input

{{cluster_names}}

Phase 3 cluster discovery

{{benchmarks}}

Phase 1-2 statistics

{{opening_moves}}

Phase 2 coding

{{signature_phrases}}

Phase 2-3 analysis

Technique Guides

Reference these guides for phase-specific instructions:

Guide Purpose

phases/phase0-scope.md

Scope definition, model selection

phases/phase1-immersion.md

Quantitative profiling

phases/phase2-coding.md

Genre coding methodology

phases/phase3-interpretation.md

Cluster discovery

phases/phase4-generation.md

Skill file generation

phases/phase5-validation.md

Quality verification

Templates

Template Purpose

templates/skill-template.md

Main SKILL.md structure

templates/phase-template.md

Phase file structure

templates/cluster-template.md

Cluster profile structure

templates/technique-template.md

Technique guide structure

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

Phase Model Rationale

Phase 0: Scope Sonnet Planning, structural decisions

Phase 1: Immersion Sonnet Counting, statistics

Phase 2: Coding Sonnet Systematic processing

Phase 3: Interpretation Opus Pattern recognition, cluster naming

Phase 4: Generation Opus Template adaptation, prose quality

Phase 5: Validation Sonnet Verification, checking

Starting the Process

When the user is ready to begin:

Ask about the target:

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

Ask about the corpus:

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

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."

Ask about output:

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

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.