Agent skill · Security

story-based-framing

This skill should be used when describing patterns or anti-patterns for detection by LLM agents across any domain (code analysis, business processes, security audits, UX design, data quality, medical diagnosis, etc.). Uses narrative storytelling structure ("The Promise" → "The Betrayal" → "The Consequences" → "The Source") to achieve 70% faster pattern identification compared to checklist or formal specification approaches. Triggers when creating pattern descriptions for any systematic analysis, detection tasks, or when delegating pattern-finding to sub-agents.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill story-based-framing-bbgnsurftech-claude-skills-collec-3 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 16 KB
Bundled scripts: none
Path: skills/analysis/story-based-framing-bbgnsurftech-claude-skills-collec-3/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Story-Based Framing for Pattern Detection ## Overview Story-based framing uses narrative structure to describe patterns and anti-patterns across any domain, enabling LLM agents to identify them 70% faster than traditional approaches (checklists, formal specifications, or symptom-based descriptions). Experimental evidence from code analysis shows narrative descriptions requiring only 3 search steps versus 10 steps for refactoring-style descriptions. The technique structures pattern descriptions as a causal story that mirrors the investigation thought process, frontloading the most distinctive criteria to eliminate false positives early. **This approach is domain-independent** - it works for code analysis, business process audits, security reviews, UX analysis, data quality checks, medical diagnosis, and any systematic pattern detection task. ## When to Use This Skill Use story-based framing when: - Creating pattern descriptions for LLM agents to identify in any domain - Documenting anti-patterns, failure modes, or problematic behaviors - Delegating pattern-finding tasks to sub-agents - Designing automated detection or review workflows - Building checklists that need to prioritize

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Core Narrative Structure
  4. Act 1: The Promise
  5. Act 2: The Betrayal
  6. Act 3: The Consequences
  7. Act 4: The Source
  8. Narrative Template
  9. Pattern Description Effectiveness Data
  10. Design Principles
  11. Frontload Distinctive Criteria
  12. Use Causal Language
  13. Provide Domain-Appropriate Examples
  14. Make It Memorable
Ships with 1 file
  • metadata.json
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About this skill
What does the story-based-framing skill do?

This skill should be used when describing patterns or anti-patterns for detection by LLM agents across any domain (code analysis, business processes, security audits, UX design, data quality, medical diagnosis, etc.). Uses narrative storytelling structure ("The Promise" → "The Betrayal" → "The Consequences" → "The Source") to achieve 70% faster pattern identification compared to checklist or formal specification approaches. Triggers when creating pattern descriptions for any systematic analysis, detection tasks, or when delegating pattern-finding to sub-agents.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill story-based-framing-bbgnsurftech-claude-skills-collec-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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