ai-agent-readonly-context

Provide read-only Kubernetes and GitOps context via MCP gateway with JSON snapshots.

14|Updated Jan 17, 2026
One-click install
npx skills add https://github.com/confighub/cub-scout --skill ai-agent-readonly-context
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-agent-readonly-context
Source: https://github.com/confighub/cub-scout/tree/main/skills/ai-agent-readonly-context
Command: npx skills add https://github.com/confighub/cub-scout --skill ai-agent-readonly-context

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It solves the problem of getting an AI agent reliable, deterministic, read-only Kubernetes and GitOps cluster context that it can safely reason over without accidentally mutating anything.

Core Features & Use Cases

  • MCP gateway for live, read-only tool calls: Exposes cub-scout verbs through mcp serve so an agent host can discover tools and call them for explanations, traces, scans, and diagnostics.
  • Deterministic context snapshots: Uses context-pack --format json to generate a stable JSON snapshot suitable for prompt caching, diffs, and structured LLM reasoning.
  • LLM-friendly presentation format: Applies --presentation ai to produce consistently parseable output (and optionally paired human+AI formatting).
  • Read-only safety invariant for agent calls: Ensures the MCP tool catalog is non-mutating so an LLM cannot apply/patch/delete cluster state through cub-scout.

Quick Start

Configure your agent host to connect to cub-scout's MCP server, then ask it for a deterministic snapshot via context-pack and follow up with doctor/explain/trace tool calls as needed.

Frequently Asked Questions about ai-agent-readonly-context

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

FAQPage Schema
How do I provide read-only Kubernetes context to an LLM agent safely?

To provide read-only Kubernetes context to an LLM agent, configure your agent host to connect to the cub-scout MCP server, exposing a non-mutating tool surface that prevents the LLM from applying, patching, or deleting cluster state.

What is a deterministic context snapshot for Kubernetes and GitOps analysis?

A deterministic context snapshot is a stable JSON output generated by context-pack that captures Kubernetes and GitOps cluster state, making it suitable for prompt caching, diffs, and structured LLM reasoning.

How do I integrate cub-scout with an MCP-enabled agent host for troubleshooting?

You can integrate cub-scout with an MCP-enabled agent host by running mcp serve to expose cub-scout verbs, allowing the agent to discover tools and call them for explanations, traces, scans, and diagnostics.

Can I use Kubernetes diagnostics with an AI agent without risking accidental cluster mutations?

Yes, you can use Kubernetes diagnostics with an AI agent without risking mutations by enforcing a read-only safety invariant on the MCP tool surface, ensuring the LLM cannot apply, patch, or delete cluster state through cub-scout.

Does the cub-scout MCP gateway support LLM-parseable output for GitOps context?

Yes, the cub-scout MCP gateway supports LLM-parseable output for GitOps context by applying the --presentation ai output contract, which produces consistently parseable formatting for structured LLM reasoning.