Agent Skills & Context

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", "anthropic/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),
]

# AgentContext provides flexible ways to customize prompts:
# 1. Skills: Inject instructions (always-active or keyword-triggered)
# 2. system_message_suffix: Append text to the system prompt
# 3. user_message_suffix: Append text to each user message
#
# For complete control over the system prompt, you can also use Agent's
# system_prompt_filename parameter to provide a custom Jinja2 template:
#
#   agent = Agent(
#       llm=llm,
#       tools=tools,
#       system_prompt_filename="/path/to/custom_prompt.j2",
#       system_prompt_kwargs={"cli_mode": True, "repo": "my-project"},
#   )
#
# See: https://docs.openhands.dev/sdk/guides/skill#customizing-system-prompts
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 (repo skill)
            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 is appended to the system prompt (always active)
    system_message_suffix="Always finish your response with the word 'yay!'",
    # user_message_suffix is appended to each user message
    user_message_suffix="The first character of your response should be 'I'",
    # You can also enable automatic load skills from
    # public registry at https://github.com/OpenHands/skills
    load_public_skills=True,
)

# 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("Now triggering public skill 'github'")
conversation.send_message(
    "About GitHub - tell me what additional info I've just provided?"
)
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}")

"""Example: Loading Skills from Disk (AgentSkills Standard)

This example demonstrates how to load skills following the AgentSkills standard
from a directory on disk.

Skills are modular, self-contained packages that extend an agent's capabilities
by providing specialized knowledge, workflows, and tools. They follow the
AgentSkills standard which includes:
- SKILL.md file with frontmatter metadata (name, description, triggers)
- Optional resource directories: scripts/, references/, assets/

The example_skills/ directory contains two skills:
- rot13-encryption: Has triggers (encrypt, decrypt) - listed in <available_skills>
  AND content auto-injected when triggered
- code-style-guide: No triggers - listed in <available_skills> for on-demand access

All SKILL.md files follow the AgentSkills progressive disclosure model:
they are listed in <available_skills> with name, description, and location.
Skills with triggers get the best of both worlds: automatic content injection
when triggered, plus the agent can proactively read them anytime.
"""

import os
from pathlib import Path

from pydantic import SecretStr

from openhands.sdk import LLM, Agent, AgentContext, Conversation
from openhands.sdk.context.skills import (
    discover_skill_resources,
    load_skills_from_dir,
)
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


def main():
    # Get the directory containing this script
    script_dir = Path(__file__).parent
    example_skills_dir = script_dir / "example_skills"

    # =========================================================================
    # Part 1: Loading Skills from a Directory
    # =========================================================================
    print("=" * 80)
    print("Part 1: Loading Skills from a Directory")
    print("=" * 80)

    print(f"Loading skills from: {example_skills_dir}")

    # Discover resources in the skill directory
    skill_subdir = example_skills_dir / "rot13-encryption"
    resources = discover_skill_resources(skill_subdir)
    print("\nDiscovered resources in rot13-encryption/:")
    print(f"  - scripts: {resources.scripts}")
    print(f"  - references: {resources.references}")
    print(f"  - assets: {resources.assets}")

    # Load skills from the directory
    repo_skills, knowledge_skills, agent_skills = load_skills_from_dir(
        example_skills_dir
    )

    print("\nLoaded skills from directory:")
    print(f"  - Repo skills: {list(repo_skills.keys())}")
    print(f"  - Knowledge skills: {list(knowledge_skills.keys())}")
    print(f"  - Agent skills (SKILL.md): {list(agent_skills.keys())}")

    # Access the loaded skill and show all AgentSkills standard fields
    if agent_skills:
        skill_name = list(agent_skills.keys())[0]
        loaded_skill = agent_skills[skill_name]
        print(f"\nDetails for '{skill_name}' (AgentSkills standard fields):")
        print(f"  - Name: {loaded_skill.name}")
        desc = loaded_skill.description or ""
        print(f"  - Description: {desc[:70]}...")
        print(f"  - License: {loaded_skill.license}")
        print(f"  - Compatibility: {loaded_skill.compatibility}")
        print(f"  - Metadata: {loaded_skill.metadata}")
        if loaded_skill.resources:
            print("  - Resources:")
            print(f"    - Scripts: {loaded_skill.resources.scripts}")
            print(f"    - References: {loaded_skill.resources.references}")
            print(f"    - Assets: {loaded_skill.resources.assets}")
            print(f"    - Skill root: {loaded_skill.resources.skill_root}")

    # =========================================================================
    # Part 2: Using Skills with an Agent
    # =========================================================================
    print("\n" + "=" * 80)
    print("Part 2: Using Skills with an Agent")
    print("=" * 80)

    # Check for API key
    api_key = os.getenv("LLM_API_KEY")
    if not api_key:
        print("Skipping agent demo (LLM_API_KEY not set)")
        print("\nTo run the full demo, set the LLM_API_KEY environment variable:")
        print("  export LLM_API_KEY=your-api-key")
        return

    # Configure LLM
    model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
    llm = LLM(
        usage_id="skills-demo",
        model=model,
        api_key=SecretStr(api_key),
        base_url=os.getenv("LLM_BASE_URL"),
    )

    # Create agent context with loaded skills
    agent_context = AgentContext(
        skills=list(agent_skills.values()),
        # Disable public skills for this demo to keep output focused
        load_public_skills=False,
    )

    # Create agent with tools so it can read skill resources
    tools = [
        Tool(name=TerminalTool.name),
        Tool(name=FileEditorTool.name),
    ]
    agent = Agent(llm=llm, tools=tools, agent_context=agent_context)

    # Create conversation
    conversation = Conversation(agent=agent, workspace=os.getcwd())

    # Test the skill (triggered by "encrypt" keyword)
    # The skill provides instructions and a script for ROT13 encryption
    print("\nSending message with 'encrypt' keyword to trigger skill...")
    conversation.send_message("Encrypt the message 'hello world'.")
    conversation.run()

    print(f"\nTotal cost: ${llm.metrics.accumulated_cost:.4f}")


if __name__ == "__main__":
    main()