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Pass skills in the config.skills parameter when executing agents: 
from vlmrun.client import VLMRun
from vlmrun.client.types import AgentExecutionConfig, AgentSkill
from vlmrun.types import MessageContent, FileUrl

client = VLMRun(api_key="<VLMRUN_API_KEY>")

response = client.agent.execute(
    inputs={"file": MessageContent(type="file_url", file_url=FileUrl(url="<file-url>"))},
    config=AgentExecutionConfig(
        skills=[AgentSkill(skill_name="patient-referral", skill_version="20260219-abc123")]
    ),
    batch=True,
)

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Multiple Skills

Pass multiple skills in the config.skills array: 
response = client.agent.execute(
    inputs={...},
    config=AgentExecutionConfig(
        skills=[
            AgentSkill(skill_name="document-parsing"),
            AgentSkill(skill_name="data-validation"),
        ]
    ),
    batch=True,
)

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Inline Skills

Instead of referencing a server-stored skill by name, you can send the skill bundle directly in the request as a base64-encoded zip. The Python SDK provides AgentSkill.from_directory to build an inline AgentSkill from a local directory in one call: 
from pathlib import Path
from vlmrun.client import VLMRun
from vlmrun.client.types import AgentExecutionConfig, AgentSkill

client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Build an inline AgentSkill from a local skill directory
skill = AgentSkill.from_directory(Path("./my-skill"))

response = client.agent.execute(
    inputs={"file": {"type": "file_url", "file_url": {"url": "<file-url>"}}},
    config=AgentExecutionConfig(skills=[skill]),
    batch=True,
)
AgentSkill.from_directory zips the directory, base64-encodes it, and reads the name and description from the SKILL.md frontmatter automatically. It returns an AgentSkill with type="inline" ready for use. See Skill Structure — Inline Skill Bundles for details on bundle contents and the Reference for AgentSkill inline fields.

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Synchronous vs Batch

  • Synchronous (batch=False): Blocks until the agent completes. Best for short tasks.
  • Batch (batch=True): Returns immediately with a prediction ID. Poll for results. Best for long-running tasks like video analysis.
# Batch execution
response = client.agent.execute(
    inputs={...},
    config=AgentExecutionConfig(
        skills=[AgentSkill(skill_name="video-analysis")]
    ),
    batch=True,
)

# Poll for results
prediction = client.predictions.get(response.id)