Evaluate candidate actions against JSON policy rules with preconditions and effects.
npx skills add https://github.com/jnesfield-bot/rho --skill policy
Please help me install this Agent Skill. Skill: policy Source: https://github.com/jnesfield-bot/rho/tree/main/skills/policy Command: npx skills add https://github.com/jnesfield-bot/rho --skill policy
This Skill provides a structured, deterministic way to guide an AI agent's decision-making process, ensuring safety, consistency, and adherence to predefined operational logic, moving beyond purely LLM-driven choices.
rm -rf / command, ensuring system safety, while another rule might boost a specific search skill when the task involves research papers.Use the policy skill to evaluate a set of candidate actions against the current state and apply predefined rules.
High-intent search queries and answers about installing and using this skill.
To enforce safety guardrails on an autonomous LLM agent, define a JSON policy file with high-priority rules that block dangerous commands by evaluating preconditions against agent state before execution.
A production-rule system for agent control is a deterministic mechanism managing an AI's decision-making process by evaluating codified rules with preconditions and effects against candidate actions.
To structure agent decisions without relying purely on the LLM, apply a rules engine using a JSON policy file to filter, boost, or rewrite candidate actions based on deterministic operational logic.
Yes, you can define escalation paths for a stuck autonomous agent by using impasse handling rules within your JSON policy file to manage repeated failures and guide the agent out of blocked states.
The best way to block dangerous shell commands in an AI workflow is implementing codified safety rules in a JSON policy file that intercepts and prevents undesirable actions like destructive file system commands.
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