Skill Development

---
name: rust-expert
description: Expert Rust programming knowledge
---

# Rust Expert

## Key Principles
- Ownership and borrowing rules...
- Lifetime annotations...

## Common Patterns
...

my-skill/
  skill.toml          # Manifest (required)
  src/
    main.py           # Entry point (for Python skills)
  README.md           # Optional documentation

[skill]
name = "web-summarizer"
version = "0.1.0"
description = "Summarizes any web page into bullet points"
author = "librefang-community"
license = "MIT"
tags = ["web", "summarizer", "research"]

[runtime]
type = "python"
entry = "src/main.py"

[[tools.provided]]
name = "summarize_url"
description = "Fetch a URL and return a concise bullet-point summary"
input_schema = { type = "object", properties = { url = { type = "string", description = "The URL to summarize" } }, required = ["url"] }

[[tools.provided]]
name = "extract_links"
description = "Extract all links from a web page"
input_schema = { type = "object", properties = { url = { type = "string" } }, required = ["url"] }

[requirements]
tools = ["web_fetch"]
capabilities = ["NetConnect(*)"]

{
  "tool": "summarize_url",
  "input": {
    "url": "https://example.com"
  },
  "agent_id": "uuid-...",
  "agent_name": "researcher"
}

{
  "result": "- Point one\n- Point two\n- Point three"
}

{
  "error": "Failed to fetch URL: connection refused"
}

#!/usr/bin/env python3
"""LibreFang skill: web-summarizer"""
import json
import sys
import urllib.request


def summarize_url(url: str) -> str:
    """Fetch a URL and return a basic summary."""
    req = urllib.request.Request(url, headers={"User-Agent": "LibreFang-Skill/1.0"})
    with urllib.request.urlopen(req, timeout=30) as resp:
        content = resp.read().decode("utf-8", errors="replace")

    # Simple extraction: first 500 chars as summary
    text = content[:500].strip()
    return f"Summary of {url}:\n{text}..."


def extract_links(url: str) -> str:
    """Extract all links from a web page."""
    import re

    req = urllib.request.Request(url, headers={"User-Agent": "LibreFang-Skill/1.0"})
    with urllib.request.urlopen(req, timeout=30) as resp:
        content = resp.read().decode("utf-8", errors="replace")

    links = re.findall(r'href="(https?://[^"]+)"', content)
    unique_links = list(dict.fromkeys(links))
    return "\n".join(unique_links[:50])


def main():
    payload = json.loads(sys.stdin.read())
    tool_name = payload["tool"]
    input_data = payload["input"]

    try:
        if tool_name == "summarize_url":
            result = summarize_url(input_data["url"])
        elif tool_name == "extract_links":
            result = extract_links(input_data["url"])
        else:
            print(json.dumps({"error": f"Unknown tool: {tool_name}"}))
            return

        print(json.dumps({"result": result}))
    except Exception as e:
        print(json.dumps({"error": str(e)}))


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
from librefang_sdk import SkillHandler

handler = SkillHandler()

@handler.tool("summarize_url")
def summarize_url(url: str) -> str:
    # Your implementation here
    return "Summary..."

@handler.tool("extract_links")
def extract_links(url: str) -> str:
    # Your implementation here
    return "link1\nlink2"

if __name__ == "__main__":
    handler.run()

// src/lib.rs
use std::io::{self, Read};

#[no_mangle]
pub extern "C" fn _start() {
    let mut input = String::new();
    io::stdin().read_to_string(&mut input).unwrap();

    let payload: serde_json::Value = serde_json::from_str(&input).unwrap();
    let tool = payload["tool"].as_str().unwrap_or("");
    let input_data = &payload["input"];

    let result = match tool {
        "my_tool" => {
            let param = input_data["param"].as_str().unwrap_or("");
            format!("Processed: {param}")
        }
        _ => format!("Unknown tool: {tool}"),
    };

    println!("{}", serde_json::json!({"result": result}));
}

cargo build --target wasm32-wasi --release

[runtime]
type = "wasm"
entry = "target/wasm32-wasi/release/my_skill.wasm"

[requirements]
tools = ["web_fetch", "file_read"]

[requirements]
capabilities = ["NetConnect(*)", "ShellExec(python3)"]

librefang skill install /path/to/my-skill

librefang skill install web-summarizer

librefang skill install https://github.com/user/librefang-skill-example.git

librefang skill list

3 skill(s) installed:

NAME                 VERSION    TOOLS    DESCRIPTION
----------------------------------------------------------------------
web-summarizer       0.1.0      2        Summarizes any web page into bullet points
data-analyzer        0.2.1      3        Statistical analysis tools
code-formatter       1.0.0      1        Format code in 20+ languages

librefang skill remove web-summarizer

librefang skill install /path/to/my-skill
# Spawn an agent with the skill's tools and test them

librefang skill search "web scraping"

Skills matching "web scraping":

  web-summarizer (42 stars)
    Summarizes any web page into bullet points
    https://fanghub.dev/skills/web-summarizer

  page-scraper (28 stars)
    Extract structured data from web pages
    https://fanghub.dev/skills/page-scraper

librefang skill publish

# Install a skill (local directory, FangHub name, or git URL)
librefang skill install <source>

# List all installed skills
librefang skill list

# Remove an installed skill
librefang skill remove <name>

# Search FangHub for skills
librefang skill search <query>

# Create a new skill scaffold (interactive)
librefang skill create

librefang skill create

~/.librefang/skills/my-skill/
  skill.toml        # Pre-filled manifest
  src/
    main.py         # Starter entry point (for Python)

name = "my-assistant"
version = "0.1.0"
description = "An assistant with extra skills"
author = "librefang"
module = "builtin:chat"
skills = ["web-summarizer", "data-analyzer"]

[model]
provider = "groq"
model = "llama-3.3-70b-versatile"

[capabilities]
tools = ["file_read", "web_fetch", "summarize_url"]
memory_read = ["*"]
memory_write = ["self.*"]

librefang skill install /path/to/openclaw-skill

[skill]
name = "my-openclaw-skill"
version = "1.0.0"
description = "Converted from OpenClaw"

[runtime]
type = "node"
entry = "index.js"

[[tools.provided]]
name = "my_tool"
description = "Tool description"
input_schema = { type = "object", properties = { input = { type = "string" } }, required = ["input"] }

librefang skill install /path/to/skill-directory