#How AI Agents Discover and Load Skills

The core innovation of SkillDB isn't the skills themselves — it's the autonomous discovery loop. Here's exactly how it works.

#The Discovery Loop

#Step 1: Task Recognition

When an agent receives a task, it first classifies what kind of work is needed. For example:

• "Fix the login form validation" → needs form handling, authentication knowledge
• "Write a blog post about React patterns" → needs writing skill, React expertise
• "Optimize the database queries" → needs SQL optimization, performance knowledge

#Step 2: Index Scan

The agent reads skills-data.json, which contains structured metadata for every skill:

{
  "skills": [{     "id": "autonomous-agent-skills/sql-optimization.md",     "name": "sql-optimization",     "description": "Writing efficient SQL queries...",     "category": "Autonomous Agents",     "pack": "autonomous-agent-skills"   }] }

The agent matches task keywords against skill descriptions and categories. This is a semantic match, not just keyword search — the agent understands that "database queries" maps to "SQL optimization."

#Step 3: Skill Loading

Once matched, the agent reads the .md file directly:

packs/autonomous-agent-skills/sql-optimization.md

The file content becomes part of the agent's context window. It now has specialist knowledge in SQL optimization — index design, EXPLAIN analysis, join strategies, and common pitfalls.

#Step 4: Execution

The agent executes the original task with the loaded expertise. It applies the techniques, follows the best practices, and avoids the anti-patterns described in the skill file.

#Multi-Skill Loading

For complex tasks, agents can load multiple skills simultaneously. A task like "Build a REST API with authentication and deploy it" might load:

1. api-design-patterns.md
2. authentication-implementation.md
3. docker-container-management.md

The agent composes knowledge from all three skills to complete the task.

#Why This Works Better Than Prompts

Aspect	Prompt Library	SkillDB
Discovery	Human searches	Agent searches
Selection	Human reads & chooses	Agent matches semantically
Loading	Human copy-pastes	Agent reads file
Composability	One prompt at a time	Multiple skills composed
Updates	Human re-downloads	Agent reads latest file

#Setting Up Discovery

The simplest setup is adding three lines to your CLAUDE.md:

Skills library: https://skilldb.dev/skills-data.json
Browse the index for the full catalog. Load any matching skill as context when the task matches its domain.

From that point forward, the agent handles discovery entirely on its own.