skillnet-ai 0.0.13

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

skillnet-ai

The official Python SDK & CLI for SkillNet — search, install, create, evaluate, and connect AI agent skills.

Website · GitHub · PyPI

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Quick Start

pip install skillnet-ai

from skillnet_ai import SkillNetClient

client = SkillNetClient()  # No API key needed for search & download

# Find a skill
results = client.search(q="pdf", limit=5)
print(results[0].skill_name, results[0].stars)

# Install it
client.download(url=results[0].skill_url, target_dir="./my_skills")

That's it. Search and download are free — no API key, no rate limit.

For create, evaluate, and analyze, set API_KEY (any OpenAI-compatible key). See Configuration.

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Features

	Feature	What it does
🔍	Search	Keyword match or AI semantic search across 500+ community skills
📦	Install	One-line download from any GitHub skill directory
✨	Create	Auto-convert repos, PDFs, conversation logs, or text prompts → structured skill packages
📊	Evaluate	Score skills on 5 dimensions: Safety · Completeness · Executability · Maintainability · Cost-Awareness
🕸️	Analyze	Map similar_to · belong_to · compose_with · depend_on relationships between skills

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Python SDK

Initialize

from skillnet_ai import SkillNetClient

client = SkillNetClient(
    api_key="sk-...",         # Required for create / evaluate / analyze
    # base_url="...",         # Optional: custom LLM endpoint (default: OpenAI)
    # github_token="ghp-..." # Optional: for private repos or higher rate limits
)

Credentials can also be set via environment variables: API_KEY, BASE_URL, GITHUB_TOKEN.

Search

# Keyword search
results = client.search(q="pdf", limit=10, min_stars=5, sort_by="stars")

# Semantic search — find skills by meaning, not just keywords
results = client.search(q="analyze financial PDF reports", mode="vector", threshold=0.85)

if results:
    print(f"{results[0].skill_name} ⭐{results[0].stars}")
    print(results[0].skill_url)

Search Parameters

Parameter	Type	Default	Description
q	str	required	Search query (keywords or natural language)
mode	str	"keyword"	"keyword" or "vector"
category	str	None	Filter by category
limit	int	20	Max results per request
page	int	1	Page number (keyword only)
min_stars	int	0	Minimum star count (keyword only)
sort_by	str	"stars"	"stars" or "recent" (keyword only)
threshold	float	0.8	Similarity threshold 0.0–1.0 (vector only)

Install

local_path = client.download(
    url="https://github.com/anthropics/skills/tree/main/skills/skill-creator",
    target_dir="./my_skills"
)
print(f"Installed at: {local_path}")

Create

Convert diverse sources into structured skill packages:

# From conversation logs / execution traces
client.create(trajectory_content="User: rename .jpg→.png\nAgent: Done.", output_dir="./skills")

# From a GitHub repository
client.create(github_url="https://github.com/zjunlp/DeepKE", output_dir="./skills")

# From office documents (PDF / PPT / Word)
client.create(office_file="./guide.pdf", output_dir="./skills")

# From a natural language description
client.create(prompt="A skill for web scraping article titles", output_dir="./skills")

All modes auto-generate a complete skill package: SKILL.md + optional scripts/, references/, assets/.

Evaluate

Score any skill on 5 quality dimensions. Accepts local paths or GitHub URLs:

result = client.evaluate(
    target="https://github.com/anthropics/skills/tree/main/skills/algorithmic-art"
)
# {
#   "safety":          {"level": "Good", "reason": "..."},
#   "completeness":    {"level": "Good", "reason": "..."},
#   "executability":   {"level": "Average", "reason": "..."},
#   "maintainability": {"level": "Good", "reason": "..."},
#   "cost_awareness":  {"level": "Good", "reason": "..."}
# }

Analyze Relationships

Discover connections between skills in a local directory:

relationships = client.analyze(skills_dir="./my_skills")

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

Detects four relationship types: similar_to · belong_to · compose_with · depend_on. Results are saved to relationships.json by default.

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CLI

The CLI ships automatically with pip install skillnet-ai — powered by Typer + Rich for beautiful terminal output.

skillnet <command> --help    # Full options for any command

Commands at a Glance

Command	What it does	Example
search	Find skills	skillnet search "pdf" --mode vector
download	Install a skill	skillnet download <url> -d ./skills
create	Create from any source	skillnet create log.txt -d ./skills
evaluate	Quality report	skillnet evaluate ./my_skill
analyze	Relationship graph	skillnet analyze ./my_skills

Search

skillnet search "pdf"
skillnet search "analyze financial reports" --mode vector --threshold 0.85
skillnet search "visualization" --category "Development" --sort-by stars --limit 10

Install

skillnet download https://github.com/anthropics/skills/tree/main/skills/algorithmic-art
skillnet download <url> -d ./my_agent/skills
skillnet download <private_url> --token <your_github_token>

Create

skillnet create ./logs/trajectory.txt -d ./skills          # from trajectory
skillnet create --github https://github.com/owner/repo      # from GitHub repo
skillnet create --office ./docs/guide.pdf                    # from PDF/PPT/Word
skillnet create --prompt "A skill for table extraction"      # from prompt
skillnet create --office report.pdf --model gpt-4o           # custom model

Evaluate

skillnet evaluate ./my_skills/web_search
skillnet evaluate https://github.com/anthropics/skills/tree/main/skills/algorithmic-art
skillnet evaluate ./my_skill --category "Development" --model gpt-4o

Analyze

skillnet analyze ./my_skills
skillnet analyze ./my_skills --no-save     # print only, don't write file
skillnet analyze ./my_skills --model gpt-4o

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⚙️ Configuration

Environment Variables

Variable	Required For	Default
API_KEY	create · evaluate · analyze	—
BASE_URL	Custom LLM endpoint	https://api.openai.com/v1
GITHUB_TOKEN	Private repos / higher rate limits	—

search and download (public repos) require no credentials at all.

Linux / macOS:

export API_KEY="sk-..."
export BASE_URL="https://..."   # optional

Windows PowerShell:

$env:API_KEY = "sk-..."
$env:BASE_URL = "https://..."   # optional

Or pass credentials directly in code:

client = SkillNetClient(api_key="sk-...", base_url="https://...")

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📂 Skill Structure

Every created or downloaded skill follows a standardized layout:

skill-name/
├── SKILL.md          # [Required] YAML metadata + markdown instructions
├── scripts/          # [Optional] Executable Python / Bash scripts
├── references/       # [Optional] Static docs, API specs, schemas
└── assets/           # [Optional] Templates, icons, examples

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🤝 Contributing

Contributions are welcome! Feel free to open an Issue or submit a Pull Request.

📄 License

MIT