askill
narratological-algorithms

narratological-algorithmsSafety 100Repository

Distill artist/theorist narrative principles into formal, implementable algorithmic frameworks. Use when asked to extract, formalize, or systematize narrative techniques from any storytelling source—filmmakers, writers, theorists, game designers, showrunners. Triggers on requests involving narrative principle extraction, story structure analysis, craft methodology formalization, or creating implementable storytelling protocols.

5 stars
1.2k downloads
Updated 4/1/2026

Package Files

Loading files...
SKILL.md

Narratological Algorithm Distillation

Transform narrative principles from artists, theorists, and practitioners into formal, implementable algorithmic frameworks.

Workflow

1. Source Classification

Identify source type to calibrate extraction approach:

TypeCharacteristicsExtraction Focus
TheoristPrescriptive texts (McKee, Aristotle)Direct principle extraction
PractitionerInterviews, commentary, production docsReverse-engineering from stated methods
ClassicalAncient/foundational texts (Poetics, Natyasastra)Translation of archaic terminology
AnalystSecondary analysis of creator's workValidation against primary sources

2. Primary Source Prioritization

Always prioritize primary sources over secondary analysis:

  • Direct quotes from the creator
  • Production documents, interviews, lectures
  • The creator's own articulated methodology
  • Documented working processes

When using secondary sources, validate principles against primary evidence. Flag where principles are inferred vs. directly stated.

3. Principle Extraction Protocol

For each identified principle:

EXTRACT:
  1. Locate source statement (direct quote when available)
  2. Identify underlying mechanism (why it works)
  3. Formulate as rule or constraint
  4. Determine scope (universal vs. context-specific)
  5. Map to existing narrative theory where applicable

4. Document Structure

Generate output following the canonical structure. See references/output-template.md for the full template.

Required sections:

  1. Meta-Principles (Axioms)
  2. Structural Hierarchy
  3. Core Algorithms/Protocols
  4. Diagnostic Questions/Tests
  5. Quick Reference Card

Optional sections (as warranted):

  • Episode/Scene Templates
  • Theoretical Correspondence Tables
  • Source Cross-Reference Appendix

5. Formalization Patterns

Convert principles to implementable forms:

Source FormTarget Form
Conceptual statementConstraint rule
Process descriptionPseudocode function
Best practiceValidity test
ComparisonDecision table
TaxonomyClassification tree

See references/formalization-patterns.md for detailed examples.

6. Axiom Identification

Identify 3-7 meta-principles that underpin the creator's entire approach:

AXIOM_CRITERIA:
  - Foundational (other principles derive from it)
  - Non-negotiable in the creator's worldview
  - Distinguishes this approach from alternatives
  - Stated explicitly or demonstrated consistently

Format axioms with unique identifiers: [CREATOR_INITIALS]-A[N]

7. Validation Checks

Before finalizing, verify:

  • All principles traceable to source material
  • Pseudocode is syntactically coherent
  • Decision tables have complete coverage
  • Quick reference captures essential operations
  • Diagnostic questions are answerable yes/no
  • Theoretical correspondences are accurate

8. Cross-Medium Adaptation Notes

When source material is medium-specific, include adaptation guidance for:

  • Film → Television (serialization, episode structure)
  • Literature → Interactive (agency, branching)
  • Single creator → Collaborative (writers' room dynamics)
  • Western → Non-Western theoretical traditions

Reference Files

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

72/100Analyzed 3/28/2026

Well-structured skill with detailed 8-step workflow for extracting narrative principles into algorithmic frameworks. Content is technically sound with tables, checklists, and clear protocols. Minor gaps: external reference files not provided (output-template.md, formalization-patterns.md), tags are mismatched (github-actions/testing vs narrative theory), and path suggests internal build artifact. High reusability for narrative analysis tasks but relies on missing reference materials for full implementation.

100
75
85
65
75

Metadata

Licenseunknown
Version-
Updated4/1/2026
Publisherorganvm-iv-taxis

Tags

github-actionstesting