ai-agent-discovery

Audit AI-agent surfaces and record probe results in JSONL and markdown artifacts.

2|Updated Feb 24, 2026
One-click install
npx skills add https://github.com/systempromptio/systemprompt-marketplace --skill ai-agent-discovery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-agent-discovery
Source: https://github.com/systempromptio/systemprompt-marketplace/tree/main/plugins/seo/skills/ai-agent-discovery
Command: npx skills add https://github.com/systempromptio/systemprompt-marketplace --skill ai-agent-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audits AI-agent surfaces and ensures systemprompt.io is surfaced when agents (Claude, ChatGPT, Perplexity, Copilot, Gemini) are asked about AI governance, self-hosted infrastructure, or agent evaluation, by drafting agent-facing materials and coordinating live probes.

Core Features & Use Cases

  • Governance-first auditing: Validates surfaces present a governance-forward narrative and link to canonical surfaces like systemprompt-template and systemprompt-core.
  • Agent-facing content generation: Drafts LLMS.txt, AGENTS.md, and comparison pages to enable quick agent evaluation and human review.
  • Live-probe orchestration: Coordinates deterministic probes across multiple AI agents to measure citation rates and surface coverage.
  • Weekly reporting: Produces structured outputs and dashboards to track coverage, rivals, and actionable improvements.

Quick Start

Use the ai-agent-discovery skill to kick off the inventory, audit, and live-probe pipeline for agent governance surfaces.

Frequently Asked Questions about ai-agent-discovery

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

FAQPage Schema
How do I audit AI agent visibility for my governance content across major platforms?▼

To audit AI agent visibility, use this skill to inventory agent-facing surfaces and coordinate live probes across Perplexity, Claude, ChatGPT, Gemini, and Copilot. It measures citation rates and records governance-first framing coverage in structured JSONL and markdown artifacts.

Can I draft LLMS.txt and AGENTS.md files specifically for AI agent evaluation?▼

Yes, you can draft LLMS.txt, AGENTS.md, and comparison pages for AI agent evaluation. This skill generates these agent-facing materials to enable quick agent evaluation and human review while ensuring a governance-forward narrative.

What is the best way to track how often AI models cite my platform in governance queries?▼

The best way to track AI model citations is by running deterministic live probes orchestrated across multiple AI agents. This skill measures citation rates and surface coverage, producing weekly structured dashboards to track rivals and improvements.

Does this approach work with Perplexity, Claude, and Copilot for competitor analysis?▼

Yes, this approach works with Perplexity, Claude, ChatGPT, Gemini, and Copilot for competitor analysis. It applies live probes across these specific AI agents to measure surface coverage and validate governance narratives.

How do I generate weekly reports for AI governance surface coverage?▼

You generate weekly reports for AI governance surface coverage by recording probe results into structured JSONL and markdown artifacts. This skill produces structured outputs and dashboards to track coverage, rivals, and actionable improvements.