gemini

Enforce agent routing and policy rules for structured task execution.

Updated Mar 8, 2026
One-click install
npx skills add https://github.com/darthlinuxer/dialectic-crew-ai --skill gemini-darthlinuxer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gemini
Source: https://github.com/darthlinuxer/dialectic-crew-ai/tree/main/src/mcp/skills/gemini
Command: npx skills add https://github.com/darthlinuxer/dialectic-crew-ai --skill gemini-darthlinuxer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Gemini defines global agent behavior rules for the workspace to ensure safe, consistent, and predictable AI actions.

Core Features & Use Cases

  • Enforces a hierarchical rule set for how agents and skills are loaded and prioritized.
  • Provides automatic, domain-aware agent routing and mandatory pre-response announcements.
  • Supports a Socratic Gate and multi-phase planning to handle complex, multi-domain requests.

Quick Start

Provide a task description and let Gemini apply routing and safety checks to respond.

Frequently Asked Questions about gemini

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

FAQPage Schema
How do I enforce safe agent routing for multi-domain AI workflows?

Safe agent routing for multi-domain AI workflows is enforced by applying a hierarchical rule set that guides correct agent selection and gates unsafe behavior. This ensures structured task execution across diverse requests.

What is frontmatter-driven skill loading and how does it prevent ambiguous behavior?

Frontmatter-driven skill loading uses metadata requirements to validate and prioritize agents before execution. It prevents ambiguous behavior by gating protocols that mandate pre-response announcements and enforce safety checks.

How do I handle complex multi-domain requests using multi-phase planning?

Complex multi-domain requests are handled using a Socratic Gate and multi-phase planning. This mechanism breaks down tasks, applies domain-aware routing, and enforces policy compliance before generating a response.

Can I use rule-driven orchestration to automate policy enforcement across agents?

Yes, rule-driven orchestration automates policy enforcement by applying global workspace rules to agent actions. It ensures consistent, predictable, and safe execution by satisfying gating protocols and mandatory announcements.

When do I need mandatory pre-response announcements in AI task execution?

Mandatory pre-response announcements are needed when executing structured AI workflows that require explicit policy enforcement. They ensure transparent agent selection and prevent unsafe or ambiguous behavior during multi-domain task routing.

Why does my agent routing fail when loading skills without frontmatter requirements?

Agent routing fails without frontmatter requirements because the hierarchical rule set relies on them to validate skill loading and prioritize agents. Missing frontmatter bypasses gating protocols, leading to unsafe or ambiguous behavior.