Why is this better than a standard checklist?

Checklists lack priority and causal hierarchy. Story-based framing frontloads the most distinctive criteria, allowing Claude to eliminate non-matches up to 70% faster than sequential checklist validation.

What is story-based framing for AI agents?

It is a narrative technique that describes patterns through a four-act structure (Promise, Betrayal, Consequences, and Source) to help AI agents identify issues faster by following a causal chain.

Can I use this for non-coding tasks?

Absolutely. This skill is domain-independent and highly effective for business process audits, UX design reviews, security compliance, and data quality checks.

How much does this improve agent performance?

In experimental tests, narrative descriptions required only 3 search steps to identify a pattern, compared to 10 steps for traditional symptom-based descriptions.

Story-Based Pattern Framing

Name: Story-Based Pattern Framing
Author: Jamie-BitFlight

byJamie-BitFlight

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Productivity & Workflow

Accelerates pattern and anti-pattern detection by 70% using a structured narrative framework that mirrors investigative logic.

About

Story-Based Pattern Framing is a domain-agnostic methodology designed to optimize how Claude identifies complex patterns and anti-patterns. By structuring descriptions as a causal narrative—moving through 'The Promise,' 'The Betrayal,' 'The Consequences,' and 'The Source'—it mirrors natural human investigative logic. This approach allows AI agents to filter out false positives early and pinpoint root causes with significantly fewer search steps than traditional checklists or formal specifications, making it ideal for code audits, security reviews, and business process analysis.

Key Features

Domain-independent application for software, security, UX, and business audits
Optimized investigation paths reducing agent search steps from 10 to 3
Four-act causal narrative structure (Promise, Betrayal, Consequences, Source)
10 GitHub stars
Standardized template for documenting systemic failure modes
Front-loaded criteria to filter 90% of false positives in the first step

Use Cases

Automating the detection of complex architectural code smells and anti-patterns
Identifying business process bottlenecks and communication gaps in operational logs
Conducting efficient security audits by framing vulnerabilities as broken promises

About

Key Features

Domain-independent application for software, security, UX, and business audits
Optimized investigation paths reducing agent search steps from 10 to 3
Four-act causal narrative structure (Promise, Betrayal, Consequences, Source)
10 GitHub stars
Standardized template for documenting systemic failure modes
Front-loaded criteria to filter 90% of false positives in the first step

Use Cases

Automating the detection of complex architectural code smells and anti-patterns
Identifying business process bottlenecks and communication gaps in operational logs
Conducting efficient security audits by framing vulnerabilities as broken promises