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ai-safety

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Practice AI safety across the lifecycle — risk assessment, alignment concepts, evaluation, deployment safeguards, and governance. Use when building or deploying AI systems responsibly.

skills/ai-safety/SKILL.md

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# AI Safety

AI safety is the practice of building systems that do what we intend, don't cause unintended harm, 
and remain under meaningful human control. It spans technical work (alignment, evaluation, 
robustness) and governance (policies, oversight, accountability) — this skill covers both at a 
practitioner level.

## Overview

Think in layers. Technical: train models to follow intent faithfully (alignment), test for 
dangerous capabilities and failure modes (evaluation), harden against adversaries (robustness), and 
keep humans meaningfully in control of consequential actions (oversight). Organizational: risk 
assessments before deployment, clear ownership, incident response plans, and honest communication 
about limitations. Safety isn't a feature added at the end — it's a property of how you build.
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