skillnet-ai 0.0.5

Official Python SDK for SkillNet: Create, Evaluate, and Connect AI Skills.

skillnet-ai

skillnet-ai is the official Python Toolkit for interacting with the SkillNet platform. It allows AI Agents to Create, Evaluate and Organize AI skills at scale. It functions seamlessly as both a powerful Python Library and a feature-rich Command Line Interface (CLI).

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🚀 Features

• 🔍 Search: Find skills using keywords match or semantic search.
• 📦 One-Line Installation: Download skill packages directly from GitHub repositories.
• ✨ Skill Creation: Automatically convert various sources into structured, reusable skill packages using LLMs:
  • Execution trajectories / conversation logs
  • GitHub repositories
  • Office documents (PDF, PPT, Word)
  • Direct text prompts
• 📊 Evaluation: Evaluate and score skills for quality assurance (Safety, Completeness, Excutability, Modifiability, Cost-Aware).
• 🕸️ Relationship Analysis: Automatically map the connections between skills in your local library, identifying structural relationships between skills (similar_to, belong_to, compose_with, depend_on).

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📥 Installation

pip install skillnet-ai

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🛠 Usage (Python SDK)

The SkillNetClient provides a unified interface for searching, downloading, creating, and evaluating skills.

1. Initialize the Client

Initialize the client with your credentials. You can also set API_KEY and GITHUB_TOKEN in your environment variables.

from skillnet_ai import SkillNetClient

# Initialize with optional credentials
client = SkillNetClient(
    api_key="sk-...",       # Required for Create, Evaluate, and Analyze functions
    # base_url="...",         # Optional custom LLM base URL
    # github_token="ghp-..."  # Optional, for private repos or higher rate limits
)

2. Search for Skills

Perform keywords match or semantic searches to find skills.

# 1. Standard Keywords Match
results = client.search(q="pdf")

# 2. Semantic Search
results = client.search(q="Help me analyze financial PDF reports", mode="vector")

if results:
    top_skill = results[0]
    print(f"Found: {top_skill.skill_name} (Stars: {top_skill.stars})")
    print(f"URL: {top_skill.skill_url}")

Parameter Reference

Parameter	Type	Default	Description
q	str	Required	Search query (keywords or natural language).
mode	str	"keyword"	Search mode: "keyword" or "vector".
category	str	None	Filter skills by category.
limit	int	20	Maximum number of results per request.
page	int	1	[Keyword Mode Only] Page number for pagination.
min_stars	int	0	[Keyword Mode Only] Filter by minimum star count.
sort_by	str	"stars"	[Keyword Mode Only] Sort by "stars" or "recent".
threshold	float	0.8	[Vector Mode Only] Minimum similarity threshold (0.0 - 1.0).

3. Install Skills

Download and install a skill directly from a URL (e.g., from above search results) into your local workspace.

skill_url = "https://github.com/anthropics/skills/tree/main/skills/skill-creator"

try:
    # Downloads to ./my_agent_skills
    local_path = client.download(url=skill_url, target_dir="./my_agent_skills")
    print(f"Skill successfully installed at: {local_path}")
except Exception as e:
    print(f"Download failed: {e}")

4. Create Skills

4.1 Create from Local Trajectory

Turn conversation logs or execution traces into a polished Skill Package (SKILL.md, scripts, etc.).

# 1. Prepare your trajectory (e.g., a conversation log string)
trajectory_log = """
User: I need to rename all .jpg files in this folder to .png.
Agent: I will write a python script to iterate through the folder...
Agent: Script executed. Renamed 5 files.
"""

# 2. Generate Skills
# Returns a list of paths to the generated skill folders
created_paths = client.create(
    trajectory_content=trajectory_log, 
    output_dir="./created_skills",
    model="gpt-4o"
)

print(f"Created {len(created_paths)} new skills.")
for path in created_paths:
    print(f"- {path}")

4.2 Create from GitHub Repository

Convert an existing GitHub repository into a skill package.

# Create skill from a GitHub repository
created_paths = client.create(
    github_url="https://github.com/zjunlp/DeepKE",
    output_dir="./created_skills",
    model="gpt-4o"
)

4.3 Create from Office Documents

Convert PDF, PowerPoint, or Word documents into skill packages.

# Create skill from a PDF document
created_paths = client.create(
    office_file="./docs/user_guide.pdf",
    output_dir="./created_skills"
)

4.4 Create from Prompt

Generate a skill directly from a text description.

# Create skill from a prompt description
created_paths = client.create(
    prompt="Create a skill for web scraping that extracts article titles and content",
    output_dir="./created_skills"
)

5. Skill Evaluation

Assess the Safety, Completeness, Executability, Modifiability and Cost-Aware of a skill. Supports both remote GitHub URLs and local directories.

# Evaluate from local directory
# target_skill = "./my_skills/web_search"

# Evaluate from GitHub URL (uses github_token if provided during initialization)
target_skill = "https://github.com/anthropics/skills/tree/main/skills/algorithmic-art"

result = client.evaluate(target=target_skill)
print(f"Evaluation Result: {result}")

6. Skill Relationship Analysis

Analyze a local directory containing multiple skills to infer a relationship graph. It identifies relationships like dependencies (depend_on), collaboration (compose_with), hierarchy (belong_to), and alternatives (similar_to).

# Directory containing multiple skill folders
skills_directory = "./my_agent_skills"

# Analyze relationships between skills
# This will also save a 'relationships.json' in the directory by default
relationships = client.analyze(skills_dir=skills_directory)

# Display the relationships
for rel in relationships:
    print(f"{rel['source']} --[{rel['type']}]--> {rel['target']}")
    # Output: PDF_Parser --[compose_with]--> Text_Summarizer

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💻 CLI Usage

skillnet-ai provides a robust Command Line Interface (CLI) powered by Typer and Rich. It allows you to create, evaluate, and organize skills directly from the terminal with visual feedback.

Tip: You can view the full list of options for any command using --help (e.g., skillnet search --help).

1. Search Skills (search)

Search the registry using keywords match or semantic search.

# Basic keywords match
skillnet search "pdf"

# Semantic/Vector search (finds skills by meaning)
skillnet search "Help me analyze financial PDF reports" --mode vector --threshold 0.85

# Filter by category and sort results
skillnet search "visualization" --category "Development" --sort-by stars --limit 10

Key Options:

Option	Type	Default	Description
q (Argument)	str	Required	Your search query (keywords or natural language description).
--mode	str	keyword	Search mode: keyword (fuzzy match) or vector (AI semantic).
--category	str	None	Filter results by category (e.g., Development, Business).
--limit	int	20	Maximum number of results to return.
[Keyword Mode Only]
--page	int	1	Page number for pagination.
--min-stars	int	0	Minimum star rating required.
--sort-by	str	stars	Sort criteria: stars or recent.
[Vector Mode Only]
--threshold	float	0.8	Similarity threshold (0.0–1.0). Higher is stricter.

2. Install Skills (download)

Download and install a skill directly from a GitHub repository subdirectory.

# Download to the current directory
skillnet download https://github.com/anthropics/skills/tree/main/skills/algorithmic-art

# Download to a specific target directory
skillnet download https://github.com/anthropics/skills/tree/main/skills/algorithmic-art -d ./my_agent/skills

# Download from a private repository
skillnet download <private_url> --token <your_github_token>

3. Create Skills (create)

Generate structured Skill Packages from various sources using LLMs.

Linux/macOS:

# Requirement: Ensure API_KEY is set in your environment variables.
export API_KEY=sk-xxxxx
export BASE_URL=xxxxxx  # Optional custom LLM base URL

Windows PowerShell:

# Requirement: Ensure API_KEY is set in your environment variables.
$env:API_KEY = "sk-xxxxx"
$env:BASE_URL = "xxxxxx"  # Optional custom LLM base URL

Usage Examples:

# From a trajectory file
skillnet create ./logs/trajectory.txt -d ./generated_skills

# From a GitHub repository
skillnet create --github https://github.com/owner/repo

# From an office document (PDF, PPT, Word)
skillnet create --office ./docs/guide.pdf

# From a direct prompt
skillnet create --prompt "Create a skill for extracting tables from images"

# Specify a custom model
skillnet create --office report.pdf --model gpt-4o

4. Evaluate Skills (evaluate)

Generate a comprehensive quality report (Safety, Completeness, Executability, Modifiability, Cost Awareness) for a skill.

Linux/macOS:

# Requirement: Ensure API_KEY is set in your environment variables.
export API_KEY=sk-xxxxx
export BASE_URL=xxxxxx  # Optional custom LLM base URL

Windows PowerShell:

# Requirement: Ensure API_KEY is set in your environment variables.
$env:API_KEY = "sk-xxxxx"
$env:BASE_URL = "xxxxxx"  # Optional custom LLM base URL

Usage Examples:

# Requirement: Ensure API_KEY is set in your environment variables.
# Evaluate a remote skill via GitHub URL
skillnet evaluate https://github.com/anthropics/skills/tree/main/skills/algorithmic-art

# Evaluate a local skill directory
skillnet evaluate ./my_skills/web_search

# Custom evaluation config
skillnet evaluate ./my_skills/tool --category "Development" --model gpt-4o

5. Analyze Relationships (analyze)

Scan a directory of skills to analyze their connections using AI.

Linux/macOS:

# Requirement: Ensure API_KEY is set in your environment variables.
export API_KEY=sk-xxxxx
export BASE_URL=xxxxxx  # Optional custom LLM base URL

Windows PowerShell:

# Requirement: Ensure API_KEY is set in your environment variables.
$env:API_KEY = "sk-xxxxx"
$env:BASE_URL = "xxxxxx"  # Optional custom LLM base URL

Usage Examples:

# Analyze a directory containing multiple skill folders
skillnet analyze ./my_agent_skills

# Analyze without saving the result file (just print to console)
skillnet analyze ./my_agent_skills --no-save

# Specify a model for the analysis
skillnet analyze ./my_agent_skills --model gpt-4o

⚙️ Configuration

Environment Variables

If you are using the Creation, Evaluation, or Analyze feature, you must configure your LLM provider.

Variable	Description	Default
API_KEY	Required for creating or evaluating skills.	None
BASE_URL	Optional. Useful if using a proxy or compatible API.	https://api.openai.com/v1

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📂 Skill Structure

When you create or install a skill, it follows the Standard Skill Structure:

skill-name/
├── SKILL.md          # (required) Main orchestration file (YAML metadata + Instructions)
├── scripts/          # (optional) Executable Python/Bash scripts
├── references/       # (optional) Static documentation or API specs
└── assets/           # (optional) Templates, icons, etc.

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🗺 Roadmap

• ✅ Keyword Match & Semantic Search
• ✅ Skill Installer
• ✅ Skill Creator (Local File & GitHub Repository)
• ✅ Skill Evaluation & Scoring
• ✅ Skill Relationship Analysis

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🤝 Contributing

Contributions are welcome! Please submit a Pull Request or open an Issue on GitHub.

📄 License

This project is licensed under the MIT License.