Design and implement safety controls for LLM-based agent workflows.
npx skills add https://github.com/alo-exp/silver-bullet --skill ai-llm-safety
Please help me install this Agent Skill. Skill: ai-llm-safety Source: https://github.com/alo-exp/silver-bullet/tree/main/forge/skills/ai-llm-safety Command: npx skills add https://github.com/alo-exp/silver-bullet --skill ai-llm-safety
Every AI system that uses LLM agents, tools, or prompts must treat safety as a first-class constraint to prevent prompt injection, data leakage, and unsafe tool use.
Run a safety review to verify prompts, tool usage, and data handling before implementation.
High-intent search queries and answers about installing and using this skill.
Prevent prompt injection in LLM agent workflows by establishing explicit guardrails, enforcing input validation, and maintaining context integrity during prompt construction. These controls isolate untrusted data from system instructions to block malicious overrides.
Enforce least-privilege prompting for safe tool use by applying safety controls during the planning and review phases of agent workflows. This restricts tool access, validates inputs, and ensures agents only execute explicitly permitted actions.
Stop data leakage in AI agent workflows by implementing context management guardrails and verifying context provenance before tool execution. This framework ensures sensitive information is not exposed through unauthorized tool calls or prompt responses.
You need safety controls for LLM-based agents whenever your system orchestrates prompts, tools, and autonomous actions. Apply these guardrails during both planning and review phases to mitigate data exfiltration risks and prevent unsafe tool execution.
Run a safety review to verify prompts, tool usage, and data handling before implementation. This process identifies misconfigurations, validates input integrity, and hardens deployments by enforcing strict access controls across all agent actions.
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