Table of Contents

🎯 Understanding Agent Skills in LM-Kit.NET


📄 TL;DR

Agent Skills is an open standard for defining modular, reusable AI capabilities through structured instruction files. In LM-Kit.NET, skills are loaded via AgentSkill and managed through the SkillRegistry, enabling agents to dynamically acquire specialized behaviors, domain expertise, or workflow capabilities without code changes. Skills follow a progressive disclosure pattern using SKILL.md files that combine YAML metadata with Markdown instructions.


📚 What are Agent Skills?

Definition: Agent Skills represent a standardized approach to packaging AI agent capabilities as portable, self-contained instruction sets. Each skill defines what an agent can do, how it should behave, and what tools or context it needs, all expressed in a format that both humans and AI systems can understand.

The Agent Skills standard emerged from the need to share and reuse agent behaviors across different systems, models, and platforms. Rather than hardcoding agent capabilities into application logic, skills externalize these definitions into structured files that can be version-controlled, shared, and composed.


🧠 The SKILL.md File Format

Agent Skills use a simple but powerful file format that combines:

YAML Frontmatter (Metadata)

---
name: Code Review Assistant
description: Reviews code for bugs, security issues, and best practices
version: 1.0.0
author: LM-Kit
tags: [development, code-review, security]
tools:
  - file_system
  - code_analysis
context:
  - programming_languages
  - security_guidelines
---

Markdown Body (Instructions)

The body contains natural language instructions that guide agent behavior:

# Code Review Assistant

## Your Role
You are an expert code reviewer with deep knowledge of software engineering best practices, security vulnerabilities, and clean code principles.

## Guidelines
1. Focus on actionable feedback, not stylistic preferences
2. Prioritize security issues over minor optimizations
3. Explain the "why" behind each suggestion

## Review Process
- First, understand the code's purpose
- Identify potential bugs and edge cases
- Check for security vulnerabilities
- Suggest improvements with examples

🔍 The Role of Agent Skills in LM-Kit.NET

  1. Modular Capability Design Skills separate "what the agent knows" from "how the agent works." This allows you to swap, update, or combine skills without modifying application code.

  2. Progressive Disclosure Skills support progressive disclosure, where basic capabilities are always available, but advanced features can be unlocked based on context or user permissions.

  3. Domain Expertise Packaging Package specialized knowledge (legal, medical, financial, technical) as skills that can be loaded on demand, keeping agents focused and efficient.

  4. Write Once, Use Everywhere A skill written for one LM-Kit.NET application works in any other, and skills can be shared across teams, organizations, or the broader community.

  5. Version Control and Governance Skills are text files that fit naturally into version control systems, enabling review processes, rollbacks, and audit trails for AI behavior changes.


⚙️ Practical Usage in LM-Kit.NET SDK

LM-Kit.NET provides a complete skill management system through the LMKit.Agents.Skills namespace:

Loading a Skill

using LMKit.Agents.Skills;

// Load a skill from a SKILL.md file
var skill = AgentSkill.LoadFromFile("skills/code-review/SKILL.md");

// Or load from a directory containing SKILL.md
var skill = AgentSkill.LoadFromDirectory("skills/code-review");

// Access skill metadata
Console.WriteLine($"Skill: {skill.Name}");
Console.WriteLine($"Description: {skill.Description}");
Console.WriteLine($"Tags: {string.Join(", ", skill.Tags)}");

Using Skills with Agents

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Skills;

// Load model and skill
var model = LM.LoadFromModelID("qwen3:8b");
var reviewSkill = AgentSkill.LoadFromFile("skills/code-review/SKILL.md");

// Build agent with skill
var agent = Agent.CreateBuilder(model)
    .WithSkill(reviewSkill)
    .Build();

// The agent now has the code review capabilities defined in the skill
var result = await agent.ExecuteAsync("Review this Python function for issues...");

Managing Multiple Skills with SkillRegistry

using LMKit.Agents.Skills;

// Create a registry to manage multiple skills
var registry = new SkillRegistry();

// Load skills from a directory structure
registry.LoadFromDirectory("skills/");

// Or register individual skills
registry.Register(codeReviewSkill);
registry.Register(documentationSkill);
registry.Register(securityAuditSkill);

// Find skills by tag
var devSkills = registry.FindByTag("development");

// Find skills by name
var skill = registry.GetByName("Code Review Assistant");

// List all available skills
foreach (var s in registry.All)
{
    Console.WriteLine($"- {s.Name}: {s.Description}");
}

Creating Skills Programmatically

using LMKit.Agents.Skills;

// Create a skill from code
var skill = new AgentSkill
{
    Name = "Customer Support",
    Description = "Handles customer inquiries with empathy and efficiency",
    Version = "1.0.0",
    Tags = new[] { "support", "customer-service" },
    RequiredTools = new[] { "knowledge_base", "ticket_system" },
    Instructions = @"
        You are a helpful customer support agent.

        ## Guidelines
        - Always greet customers warmly
        - Acknowledge their concerns before providing solutions
        - Escalate complex issues to human agents when needed
    "
};

// Save to file
skill.SaveToFile("skills/customer-support/SKILL.md");

🔑 Key Concepts

  • Skill File (SKILL.md): A Markdown file with YAML frontmatter that defines an agent's capabilities, required tools, and behavioral instructions.

  • Progressive Disclosure: The pattern of revealing capabilities incrementally based on context, permissions, or user needs.

  • SkillRegistry: A collection manager in LM-Kit.NET that indexes, searches, and provides access to multiple skills.

  • AgentSkill: The class representing a parsed skill definition in LM-Kit.NET, with properties for metadata and instructions.

  • Skill Composition: Combining multiple skills to create agents with compound capabilities (e.g., code review + security audit).


📖 Common Terms

  • Skill Metadata: Structured information (name, version, tags, dependencies) in the YAML frontmatter.

  • Skill Instructions: Natural language guidance in the Markdown body that shapes agent behavior.

  • Tool Requirements: The list of tools a skill expects to be available for full functionality.

  • Context Requirements: Information or knowledge bases the skill needs to operate effectively.

  • Skill Tags: Labels for categorizing and discovering skills within a registry.


🎯 Practical Use Cases for Agent Skills

  • Domain Expert Agents: Package legal, medical, or financial expertise as skills that specialized agents can load
  • Role-Based Assistants: Define different personas (teacher, coach, analyst) as interchangeable skills
  • Workflow Automation: Create skills for specific business processes (invoice processing, report generation)
  • Quality Assurance: Build reusable review and validation skills for code, documents, or data
  • Compliance and Governance: Define skills that encode regulatory requirements and best practices
  • Multi-Tenant Applications: Load different skill sets based on customer, department, or use case

🔗 Related Concepts

  • AI Agents: The autonomous systems that execute skills to accomplish goals.

  • AI Agent Tools: External capabilities that skills can require and utilize.

  • AI Agent Memory: Persistent knowledge that skills can leverage for context-aware behavior.

  • AI Agent Planning: How skills integrate with planning strategies for multi-step execution.

  • Function Calling: The mechanism skills use to invoke registered tools.


📝 Summary

Agent Skills provide a standardized, portable way to define AI agent capabilities through structured SKILL.md files that combine YAML metadata with Markdown instructions. In LM-Kit.NET, the AgentSkill class loads and represents these definitions, while SkillRegistry manages collections of skills for discovery and composition. This approach enables modular agent design where capabilities can be shared, versioned, and composed without code changes, making it easier to build specialized agents, maintain governance over AI behavior, and share expertise across teams and applications.