SkillNet

pip install skillnet-ai

from skillnet_ai import SkillNetClient

client = SkillNetClient()

results = client.search("pdf understanding", limit=5)
print(results[0].skill_name)
print(results[0].skill_url)

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

skillnet search "pdf understanding" --limit 5
skillnet download <skill_url> -d ./my_skills

pip install skillnet-ai

pip install "skillnet-ai[graph]"        # scenario-level graph analysis
pip install "skillnet-ai[orchestrate]"  # scene orchestration

from skillnet_ai import SkillNetClient

client = SkillNetClient(
    api_key="your-api-key",       # required for create, evaluate, analyze, orchestrate
    base_url="https://api.openai.com/v1",
    github_token=None,            # optional, for private repos or higher GitHub rate limits
)

results = client.search(
    q="analyze financial PDF reports",
    mode="vector",
    threshold=0.85,
    limit=10,
)

for skill in results:
    print(skill.skill_name, skill.stars, skill.skill_url)

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

client.create(
    prompt="A skill for extracting tables from academic PDFs",
    output_dir="./skills",
)

client.create(
    github_url="https://github.com/zjunlp/DeepKE",
    output_dir="./skills",
)

client.create(
    office_file="./guide.pdf",
    output_dir="./skills",
)

report = client.evaluate("./my_skills/table-extractor")
print(report["overall_score"])
print(report["summary"])

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

for rel in relationships:
    print(f"{rel['source']} --[{rel['type']}]--> {rel['target']}")

graph = client.analyze(
    "./my_skills",
    mode="scenario",
    embedding_api_key="your-embedding-api-key",
    embedding_base_url="https://embedding.example/v1",
    embedding_model="your-embedding-model",
    output_dir="./my_skills/skillnet_graph",
    timeout=120,
)

print(graph["scenario_skill_graph"]["edges"])

pip install "skillnet-ai[orchestrate]"

result = client.orchestrate(
    "Find recent papers on retrieval-augmented generation and propose three follow-up ideas.",
    scene="sciatlas",
    timeout=240,
)

print(result.package_url)
print([skill.name for skill in result.skills])
print(result.prompt)

skillnet search "pdf"
skillnet search "analyze financial reports" --mode vector --threshold 0.85

skillnet download <url> -d ./my_agent/skills
skillnet download <url> --mirror https://ghfast.top/

skillnet create --prompt "A skill for extracting tables from images"
skillnet evaluate ./my_skills/table_extractor
skillnet analyze ./my_skills

pip install "skillnet-ai[graph]"

skillnet analyze ./my_skills --mode scenario \
  --embedding-api-key "$EMBEDDING_API_KEY" \
  --embedding-base-url "$EMBEDDING_BASE_URL" \
  --embedding-model "$EMBEDDING_MODEL" \
  --output-dir ./my_skills/skillnet_graph \
  --timeout 120

pip install "skillnet-ai[orchestrate]"

skillnet orchestrate "Find recent RAG papers and propose three follow-up ideas" \
  --scene sciatlas \
  --timeout 240

export API_KEY="your-api-key"
export BASE_URL="https://api.openai.com/v1"
export SKILLNET_MODEL="gpt-4o"

$env:API_KEY = "your-api-key"
$env:BASE_URL = "https://api.openai.com/v1"
$env:SKILLNET_MODEL = "gpt-4o"

curl "http://api-skillnet.openkg.cn/v1/search?q=pdf&sort_by=stars&limit=5"
curl "http://api-skillnet.openkg.cn/v1/search?q=reading%20charts&mode=vector&threshold=0.8"

git clone https://github.com/zjunlp/SkillNet.git
cd SkillNet

mkdir -p ~/.claude/skills
cp -R skills/skillnet ~/.claude/skills/skillnet

Use SkillNet to search for a docker skill and summarize the top result.

git clone https://github.com/zjunlp/SkillNet.git
cd SkillNet

CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
mkdir -p "$CODEX_HOME/skills"
cp -R skills/skillnet "$CODEX_HOME/skills/skillnet"

Use $skillnet to search for a LangGraph skill before planning this task.

git clone https://github.com/CycleChain/skillnet-mcp
cd skillnet-mcp
npm install && npm run build

docker pull fmdogancan/skillnet-mcp:latest

cd experiments

python alfworld_run.py --model o4-mini --split dev --max_workers 10 --exp_name alf_test --use_skill
python scienceworld_run.py --model o4-mini --split test --max_workers 5 --exp_name sci_test --use_skill
python webshop_run.py --model o4-mini --max_workers 3 --exp_name web_test --use_skill

@article{liang2026skillnet,
  title={Skillnet: Create, evaluate, and connect ai skills},
  author={Liang, Yuan and Zhong, Ruobin and Xu, Haoming and Jiang, Chen and Zhong, Yi and Fang, Runnan and Gu, Jia-Chen and Deng, Shumin and Yao, Yunzhi and Wang, Mengru and others},
  journal={arXiv preprint arXiv:2603.04448},
  year={2026}
}