Identify and verify implicit assumptions in AI-generated code.
npx skills add https://github.com/ByronWilliamsCPA/.claude --skill rad-byronwilliamscpa
Please help me install this Agent Skill. Skill: rad Source: https://github.com/ByronWilliamsCPA/.claude/tree/main/.claude/skills/rad Command: npx skills add https://github.com/ByronWilliamsCPA/.claude --skill rad-byronwilliamscpa
AI-generated code often contains implicit assumptions that pass initial testing but fail catastrophically in production under real-world conditions like high load, concurrency, or edge cases. This Skill systematically identifies and verifies these hidden assumptions before they reach deployment.
Use the rad skill to verify all hidden assumptions in your current project by running the assumption verification workflow on your recently changed files.
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Verify hidden assumptions in AI-generated code by systematically identifying and validating timing dependencies, external resource availability, data integrity, concurrency, and security risks before production deployment. This process uses multi-model AI routing to evaluate critical production risks and standard edge cases.
AI-generated code fails in production under high load due to implicit assumptions about concurrency, timing dependencies, and edge cases that pass initial testing but break under real-world conditions. Systematic assumption verification identifies these hidden failures before deployment.
Tag code assumptions during development using #CRITICAL, #ASSUME, and #EDGE markers to categorize production risks, standard assumptions, and edge cases. This enables systematic verification through automated workflows and pre-commit hooks.
Check AI-generated code for timing and concurrency issues by running an assumption verification workflow that routes critical production risks to premium AI models and standard assumptions to free models for comprehensive multi-model validation.
Integrate assumption verification into pre-commit hooks using provided slash commands for verification, assumption inventory listing, and system testing. This automates validation of recently changed files directly within your development workflow.
Use multi-model verification for production code risks when validating critical assumptions that could cause catastrophic failures under real-world conditions. Route critical production risks to premium models and standard edge case assumptions to free models for cost-effective validation.
jmmLProcess code-review feedback through a six-step READ, UNDERSTAND, VERIFY, EVALUATE, RESPOND, IMPLEMENT pattern.
cdalsoniiiDesigns and executes controlled failure experiments to validate system resilience via fault injection scenarios.
nferrer-devExecutes a six-phase software verification workflow including build, type, lint, test, security, and diff review.
htafollaAudit software projects for vulnerabilities and compliance with security standards.
