/skyll
Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.
$ npx -y skills add assafelovic/skyll --skill skyll --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/skyll
Context preview
The summary Claude sees to decide when to auto-load this skill.
Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.
SKILL.md
skyll.SKILL.mdname: skyll
description: Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.
license: Apache-2.0
Skyll - Agent Skill Discovery
Skyll enables agents to dynamically discover and retrieve skills at runtime. Instead of having all skills pre-loaded, agents can search for relevant skills on-demand and inject them into context.
When to Use This Skill
Use Skyll when:
- The current task requires specialized knowledge or workflows not in context
- Looking for best practices for a specific framework, library, or domain
- Needing procedural knowledge for complex multi-step tasks
- Wanting to enhance capabilities with community-contributed skills
Quick Usage
Option 1: Python Client (Recommended)
from skyll import Skyll
async with Skyll() as client:
# Search for relevant skills
skills = await client.search("react performance optimization", limit=3)
for skill in skills:
print(f"Skill: {skill.title}")
print(f"Score: {skill.relevance_score}")
print(skill.content) # Full SKILL.md contentOption 2: REST API
Search for skills:
curl "https://api.skyll.app/search?q=react+performance&limit=3"
Get a specific skill:
curl "https://api.skyll.app/skills/anthropics/skills/skill-creator"
Response Format
The API returns ranked skills with relevance scores (0-100):
{
"query": "react performance",
"count": 3,
"skills": [
{
"id": "react-best-practices",
"title": "React Best Practices",
"description": "Performance optimization for React and Next.js",
"source": "vercel-labs/agent-skills",
"relevance_score": 85.5,
"install_count": 82800,
"content": "# React Best Practices\n\n## Performance\n..."
}
]
}Key Fields
| Field | Description | |-------|-------------| | `id` | Skill identifier | | `title` | Human-readable title | | `description` | What the skill does | | `source` | GitHub repository (owner/repo) | | `relevance_score` | 0-100 match score | | `install_count` | Popularity from skills.sh | | `content` | Full SKILL.md markdown content | | `references` | Additional reference files (if requested) |
Search Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | `q` / `query` | required | Search query | | `limit` | 10 | Max results (1-50) | | `include_content` | true | Fetch full SKILL.md content | | `include_references` | false | Include reference files |
Example Workflow
When an agent needs to build a React Native app but lacks mobile expertise:
1. **Search for relevant skills:**
skills = await client.search("react native best practices", limit=2)2. **Inject top skill into context:**
context = skills[0].content # Full SKILL.md content
3. **Use the skill's knowledge to complete the task**
API Endpoints
| Endpoint | Description | |----------|-------------| | `GET /search?q={query}` | Search skills | | `POST /search` | Search with JSON body | | `GET /skills/{source}/{skill_id}` | Get specific skill | | `GET /health` | Health check | | `GET /docs` | Interactive API documentation |
Installation
For Python agents:
pip install skyll
For other languages, use the REST API directly at `https://api.skyll.app`.
Links
- API: https://api.skyll.app
- Docs: https://api.skyll.app/docs
- Demo: https://skyll.app
- GitHub: https://github.com/assafelovic/skyll
Read more
name: skyll description: Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection. license: Apache-2.0
Skyll - Agent Skill Discovery
Skyll enables agents to dynamically discover and retrieve skills at runtime. Instead of having all skills pre-loaded, agents can search for relevant skills on-demand and inject them into context.
When to Use This Skill
Use Skyll when:
- The current task requires specialized knowledge or workflows not in context
- Looking for best practices for a specific framework, library, or domain
- Needing procedural knowledge for complex multi-step tasks
- Wanting to enhance capabilities with community-contributed skills
Quick Usage
Option 1: Python Client (Recommended)
from skyll import Skyll
async with Skyll() as client:
# Search for relevant skills
skills = await client.search("react performance optimization", limit=3)
for skill in skills:
print(f"Skill: {skill.title}")
print(f"Score: {skill.relevance_score}")
print(skill.content) # Full SKILL.md contentOption 2: REST API
Search for skills:
curl "https://api.skyll.app/search?q=react+performance&limit=3"
Get a specific skill:
curl "https://api.skyll.app/skills/anthropics/skills/skill-creator"
Response Format
The API returns ranked skills with relevance scores (0-100):
{
"query": "react performance",
"count": 3,
"skills": [
{
"id": "react-best-practices",
"title": "React Best Practices",
"description": "Performance optimization for React and Next.js",
"source": "vercel-labs/agent-skills",
"relevance_score": 85.5,
"install_count": 82800,
"content": "# React Best Practices\n\n## Performance\n..."
}
]
}Key Fields
| Field | Description | |-------|-------------| | `id` | Skill identifier | | `title` | Human-readable title | | `description` | What the skill does | | `source` | GitHub repository (owner/repo) | | `relevance_score` | 0-100 match score | | `install_count` | Popularity from skills.sh | | `content` | Full SKILL.md markdown content | | `references` | Additional reference files (if requested) |
Search Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | `q` / `query` | required | Search query | | `limit` | 10 | Max results (1-50) | | `include_content` | true | Fetch full SKILL.md content | | `include_references` | false | Include reference files |
Example Workflow
When an agent needs to build a React Native app but lacks mobile expertise:
1. **Search for relevant skills:**
skills = await client.search("react native best practices", limit=2)2. **Inject top skill into context:**
context = skills[0].content # Full SKILL.md content
3. **Use the skill's knowledge to complete the task**
API Endpoints
| Endpoint | Description | |----------|-------------| | `GET /search?q={query}` | Search skills | | `POST /search` | Search with JSON body | | `GET /skills/{source}/{skill_id}` | Get specific skill | | `GET /health` | Health check | | `GET /docs` | Interactive API documentation |
Installation
For Python agents:
pip install skyll
For other languages, use the REST API directly at `https://api.skyll.app`.
Links
- API: https://api.skyll.app
- Docs: https://api.skyll.app/docs
- Demo: https://skyll.app
- GitHub: https://github.com/assafelovic/skyll
Skyll is a REST API and MCP server that lets any AI agent search for and learn agent skills at runtime. It aggregates skills from multiple sources, fetches the full SKILL.md content from GitHub, and returns structured JSON ready for context injection.

