discover-agent

Name: discover-agent
Availability: InStock
Author: DesireCore

Match user needs with registered AI agents via semantic relevance ranking.

2|1|Updated Feb 16, 2026

Search Tags:#AI agents #discovery #agent #recommendation #semantic matching

Ready to run

npx skills add https://github.com/desirecore/market --skill discover-agent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: discover-agent
Source: https://github.com/desirecore/market/tree/main/skills/discover-agent
Command: npx skills add https://github.com/desirecore/market --skill discover-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users identify and select the most suitable AI agent for their specific needs by understanding the user's description and matching it with registered agents seamlessly.

Core Features & Use Cases

Demand Understanding: Parses user descriptions to identify domain, task type, and keywords.
Agent Retrieval: Fetches the list of available agents through API calls.
Semantic Matching: Evaluates relevance and fit between user needs and agents’ capabilities using natural language understanding.
Candidate Presentation: Displays a ranked list of relevant agents with their statuses and suitability scores.
User Selection & Details: Supports further actions like detailed info retrieval and context switching.

Quick Start

Describe your goal or task, and I will recommend the most suitable agent for you.

Frequently Asked Questions about discover-agent

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

FAQPage Schema

How do I find the best AI agent for a specific task?▼

Finding the best AI agent involves matching your described needs with registered agents by performing semantic understanding and multi-dimensional relevance assessment. You simply describe your goal, and the system ranks suitable agents by relevance and fit.

What is semantic matching for AI agent discovery?▼

Semantic matching for AI agent discovery parses user descriptions to identify domain, task type, and keywords, then evaluates relevance against agents’ capabilities. This process fetches available agents and ranks them to ensure accurate agent selection for workflows.

Can I get detailed information about a recommended AI agent?▼

Getting detailed information about a recommended AI agent is supported after the initial candidate presentation. Once the system displays a ranked list of relevant agents with their statuses, you can request detailed info retrieval and context switching for your selected agent.

How does AI agent recommendation evaluate suitability scores?▼

AI agent recommendation evaluates suitability scores by performing multi-dimensional relevance assessment using natural language understanding. It parses your demand description and compares the extracted keywords and task type against the capabilities of registered agents.

Do I need to provide specific keywords to discover AI agents?▼

Providing specific keywords to discover AI agents is helpful but not strictly required. The demand understanding feature parses your natural language descriptions to automatically identify the domain, task type, and keywords for semantic matching.

What are the limitations of using semantic matching for agent selection?▼

Limitations of semantic matching for agent selection include its reliance on the registered agents fetched through API calls. It can only evaluate and rank the relevance of available agents, meaning it cannot recommend capabilities that have not been registered in the system.