Agent Skills & Context

Skills add specialized behaviors, domain knowledge, and context-aware triggers to your agent through structured prompts.

This example is available on GitHub: examples/01_standalone_sdk/03_activate_skill.py

Skills modify agent behavior by injecting additional context and rules. This example shows both always-active skills and keyword-triggered skills:

examples/01_standalone_sdk/03_activate_skill.py

import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    AgentContext,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.context import (
    KeywordTrigger,
    Skill,
)
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "openhands/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Tools
cwd = os.getcwd()
tools = [
    Tool(
        name=TerminalTool.name,
    ),
    Tool(name=FileEditorTool.name),
]

agent_context = AgentContext(
    skills=[
        Skill(
            name="repo.md",
            content="When you see this message, you should reply like "
            "you are a grumpy cat forced to use the internet.",
            # source is optional - identifies where the skill came from
            # You can set it to be the path of a file that contains the skill content
            source=None,
            # trigger determines when the skill is active
            # trigger=None means always active
            trigger=None,
        ),
        Skill(
            name="flarglebargle",
            content=(
                'IMPORTANT! The user has said the magic word "flarglebargle". '
                "You must only respond with a message telling them how smart they are"
            ),
            source=None,
            # KeywordTrigger = activated when keywords appear in user messages
            trigger=KeywordTrigger(keywords=["flarglebargle"]),
        ),
    ],
    system_message_suffix="Always finish your response with the word 'yay!'",
    user_message_suffix="The first character of your response should be 'I'",
)

# Agent
agent = Agent(llm=llm, tools=tools, agent_context=agent_context)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


conversation = Conversation(
    agent=agent, callbacks=[conversation_callback], workspace=cwd
)

print("=" * 100)
print("Checking if the repo skill is activated.")
conversation.send_message("Hey are you a grumpy cat?")
conversation.run()

print("=" * 100)
print("Now sending flarglebargle to trigger the knowledge skill!")
conversation.send_message("flarglebargle!")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")

Running the Example

export LLM_API_KEY="your-api-key"
cd agent-sdk
uv run python examples/01_standalone_sdk/03_activate_skill.py

Creating Skills

Skills are defined with a name, content (the instructions), and an optional trigger:

agent_context = AgentContext(
    skills=[
        Skill(
            name="repo.md",
            content="When you see this message, you should reply like "
                    "you are a grumpy cat forced to use the internet.",
            trigger=None,  # Always active
        ),
        Skill(
            name="flarglebargle",
            content='IMPORTANT! The user has said the magic word "flarglebargle". '
                    "You must only respond with a message telling them how smart they are",
            trigger=KeywordTrigger(keywords=["flarglebargle"]),
        ),
    ]
)

Keyword Triggers

Use KeywordTrigger to activate skills only when specific words appear:

Skill(
    name="magic-word",
    content="Special instructions when magic word is detected",
    trigger=KeywordTrigger(keywords=["flarglebargle", "sesame"]),
)

Message Suffixes for AgentContext

Append custom instructions to the system prompt or user messages via AgentContext:

agent_context = AgentContext(
    system_message_suffix="""
<REPOSITORY_INFO>
Repository: my-project
Branch: feature/new-api
</REPOSITORY_INFO>
    """.strip(),
    user_message_suffix="Remember to explain your reasoning."
)

• system_message_suffix: Appended to system prompt (always active, combined with repo skills)
• user_message_suffix: Appended to each user message

Replacing the Entire System Prompt

For complete control, provide a custom Jinja2 template via the Agent class:

from openhands.sdk import Agent

agent = Agent(
    llm=llm,
    tools=tools,
    system_prompt_filename="/path/to/custom_system_prompt.j2",  # Absolute path
    system_prompt_kwargs={"cli_mode": True, "repo_name": "my-project"}
)

Custom template example (custom_system_prompt.j2):

You are a helpful coding assistant for {{ repo_name }}.

{% if cli_mode %}
You are running in CLI mode. Keep responses concise.
{% endif %}

Follow these guidelines:
- Write clean, well-documented code
- Consider edge cases and error handling
- Suggest tests when appropriate

Key points:

• Use relative filenames (e.g., "system_prompt.j2") to load from the agent’s prompts directory
• Use absolute paths (e.g., "/path/to/prompt.j2") to load from any location
• Pass variables to the template via system_prompt_kwargs
• The system_message_suffix from AgentContext is automatically appended after your custom prompt

Next Steps

• Custom Tools - Create specialized tools
• MCP Integration - Connect external tool servers
• Confirmation Mode - Add execution approval