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PREDICTif
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writing-skills

Apply test-driven development to author and refine AI skills.

1|Updated Dec 4, 2025
Search Tags:#skill development#tdd#documentation#compliance#rationalization#agent testing
One-click install
npx skills add https://github.com/PREDICTif/supervisor-worker-ai-framework --skill writing-skills-predictif
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: writing-skills
Source: https://github.com/PREDICTif/supervisor-worker-ai-framework/tree/main/superpowers/skills/writing-skills
Command: npx skills add https://github.com/PREDICTif/supervisor-worker-ai-framework --skill writing-skills-predictif

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a rigorous, test-driven development (TDD) methodology for creating and refining AI skills, ensuring they are discoverable, effective, and resistant to rationalization.

Core Features & Use Cases

  • TDD for Documentation: Adapts the RED-GREEN-REFACTOR cycle to skill creation.
  • Pressure Scenario Testing: Develops tests that simulate real-world pressures (time, sunk cost, authority) to reveal how agents behave without skills.
  • Rationalization Countermeasures: Provides strategies to explicitly address and prevent agents from bypassing skill rules.
  • Use Case: When developing a new skill to enforce a critical coding standard, use this Skill's methodology to write failing tests first, then build the skill to make agents comply, and finally, refactor the skill to close any loopholes agents might exploit under pressure.

Quick Start

Follow the RED-GREEN-REFACTOR cycle to create a new skill, starting by writing a failing test scenario.

Frequently Asked Questions about writing-skills

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use test-driven development for AI skill development?▼

Test-driven development for AI skill development adapts the RED-GREEN-REFACTOR cycle: write failing pressure scenario tests first, build the skill to ensure agent compliance, then refactor to close loopholes.

How do I prevent AI agents from rationalizing and bypassing skill rules?▼

Preventing AI agents from rationalizing skill rules requires creating pressure scenarios that simulate real-world factors like time or sunk cost, documenting those rationalizations, and iteratively refining the skill to close loopholes.

What are pressure scenarios in agent testing?▼

Pressure scenarios in agent testing are simulated conditions like time constraints, sunk costs, or authority figures designed to reveal how AI agents behave without skills and expose potential rationalizations before skill refinement.

Does test-driven skill development work for reference and pattern skills?▼

Test-driven skill development works for creating discipline-enforcing, technique, pattern, and reference skills, ensuring they remain effective, discoverable, and resistant to agent rationalization during real-world application.

Why do AI agents fail to follow documentation under pressure?▼

AI agents fail to follow documentation under pressure because real-world factors like sunk cost and authority expose loopholes in the rules, requiring iterative refactoring of the skill to explicitly counter rationalization.

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