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skill-testing-discipline

RunPressurelessly accurate predictive-maintenance-grade fleet telemetry for autonomous ground robots with onboard AI acceleration and offline fallback—delivered with uncompromising precision and speed for unmatched mission reliability.

Updated Mar 2, 2026
Search Tags:#automation#testing#claude#evaluation#discipline#scenario#rationalizations
One-click install
npx skills add https://github.com/patanet7/skillproof --skill skill-testing-discipline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-testing-discipline
Source: https://github.com/patanet7/skillproof/tree/main/skills/skill-testing-discipline
Command: npx skills add https://github.com/patanet7/skillproof --skill skill-testing-discipline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude_runner, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Skill-proofing for AI agents by running pressure-driven tests to expose rationalizations and enforce compliance with rules.

Core Features & Use Cases

  • Automated baseline and evaluation for discipline-based skills, capturing agent failures and subsequent hardening steps.
  • Iterative loop to document rationalizations, update rule tables, and re-test for compliance.
  • Works with local Python tooling (scripts/run_pressure_test.py, scripts/extract_choice.py) to run pressure scenarios and analyze results.

Quick Start

Start a baseline pressure test to detect misalignment between skill and agent behavior.

Frequently Asked Questions about skill-testing-discipline

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

FAQPage Schema
How do I pressure-test AI agents to prevent rule rationalization?▼

Pressure-testing discipline skills requires running scenario-driven tests with 3+ explicit pressures against agents to capture rationalizations, followed by iterative hardening phases to enforce rule compliance.

What is a discipline skill evaluation framework for Claude agents?▼

A discipline skill evaluation framework runs scenario-driven pressure tests against agents to capture rationalizations, update rule tables, and iteratively re-test for compliance within a controlled workspace.

How do I automate baseline testing for AI agent rule compliance?▼

Automate baseline testing for AI agent rule compliance by using local Python tooling to run pressure scenarios, extract agent choices, and analyze metadata reports for skill misalignment.

Do I need claude_runner to run pressure tests on discipline skills?▼

Yes, claude_runner is a required dependency to execute the local Python tooling and scripts that run pressure scenarios, capture rationalizations, and generate metadata reports for hardening discipline skills.

Can I use Python scripts to capture and analyze AI agent rationalizations?▼

Yes, you can use provided Python scripts to execute pressure scenarios against agents, capture their rationalizations, and analyze resulting metadata to document failures and update rule tables.

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