rad

Identify and verify implicit code assumptions with tiered AI verification.

Updated Jul 2, 2025
One-click install
npx skills add https://github.com/williaby/.claude --skill rad
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rad
Source: https://github.com/williaby/.claude/tree/main/skills/rad
Command: npx skills add https://github.com/williaby/.claude --skill rad

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires git, find, grep, mcp, and includes references (resource) components.

What problem does it solve?

This Skill systematically identifies and verifies implicit assumptions in your code, especially AI-generated code, that could lead to production failures. It prevents costly outages and data loss by catching these hidden issues before deployment, giving you peace of mind and more robust applications.

Core Features & Use Cases

  • Assumption Tagging: Standardized tagging for critical, standard, and edge-case assumptions in code.
  • Tiered Verification: Routes assumptions to appropriate AI models (premium for critical, free for others) based on risk.
  • Automated Fix Generation: Generates defensive code patterns and error handling to mitigate identified risks.
  • Use Case: Before committing, run /rad/verify --scope=changed-files to automatically scan your new code for assumptions, verify them with AI, and receive suggested fixes to prevent potential bugs or outages.

Quick Start

Verify all assumptions in your currently changed files using a tiered AI strategy. /rad/verify --scope=changed-files

Frequently Asked Questions about rad

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

FAQPage Schema
How do I identify hidden assumptions in code before deployment?

Identify assumptions by scanning code for implicit dependencies on timing, external resources, data integrity, and edge cases. RAD uses standardized tagging to flag critical, standard, and edge-case assumptions, then routes them to AI models for verification to catch issues before production.

Can I verify AI-generated code for production risks automatically?

Yes. Run `/rad/verify --scope=changed-files` to automatically scan new code, verify assumptions with tiered AI models based on risk level, and receive defensive code patterns and error handling suggestions to mitigate identified risks.

What types of code assumptions does this Skill check?

RAD checks assumptions across timing, external resources, data integrity, concurrency, security, and edge conditions. It works in code generation, code review, and CI/testing pipelines to prevent production failures from hidden implicit dependencies.

How does risk-based routing work for assumption verification?

Risk-tier routing directs critical assumptions to premium AI models for thorough verification while routing standard and edge-case assumptions to free models. This optimizes verification depth and cost based on the assumption's impact on production stability.

Does this work with existing git workflows and CI pipelines?

Yes. RAD integrates with git, find, grep, and MCP dependencies. It fits into CI/testing pipelines and code review workflows, scanning changed files and generating remediation fixes compatible with standard development environments.

What output does RAD generate after verification?

RAD generates verified assumption reports, defensive code patterns, and automated fix suggestions. These outputs guide remediation and help prevent bugs, data loss, and outages by showing exactly which assumptions need defensive handling.