Lab 39.5 Solution: Loading and Using Agent Skills

Goal

This file contains the complete code for the agent.py script in the Agent Skills lab.

skills_agent/agent.py

import pathlib
from google.adk.agents import Agent
from google.adk.skills import load_skill_from_dir
from google.adk.tools.skill_toolset import SkillToolset
from google.adk.code_executors.unsafe_local_code_executor import UnsafeLocalCodeExecutor

# 1. Load the skill from the directory you created.
# We use pathlib to reliably point to the folder relative to this script.
greeting_skill = load_skill_from_dir(
    pathlib.Path(__file__).parent / "skills" / "greeting_skill"
)

# 2. Create the SkillToolset.
# The toolset acts as a manager for one or more skills.
# WARNING: UnsafeLocalCodeExecutor is used here for local execution.
# In a real production environment, you would use a secure, sandboxed executor.
my_skill_toolset = SkillToolset(
    skills=[greeting_skill],
    # We could add additional_tools=[] here if the skill needed python functions
    code_executor=UnsafeLocalCodeExecutor(),
)

# 3. Configure the Agent.
# The toolset is passed just like any other tool.
root_agent = Agent(
    model="gemini-2.5-flash",
    name="skill_user_agent",
    description="An agent that can use specialized skills.",
    tools=[my_skill_toolset]
)

Self-Reflection Answers

1. Look at your SKILL.md file. Which part of it is loaded into the agent's context before the user says hello? Which part is loaded after?

   • Answer: Before the user speaks, only the YAML frontmatter (name and description) is loaded into the agent's context. This teaches the agent what the tool is. After the user says hello (and the agent decides to "call" the skill tool based on the description), the rest of the file (the Markdown body with the actual "Step 1, Step 2..." instructions) is loaded and sent back to the agent as the tool's result, telling it how to perform the task.

2. Why do we have to wrap the skill in a SkillToolset before giving it to the agent? (Think about the other things a toolset might manage, like code executors or additional tools).

   • Answer: A single Skill often isn't just text; it can include executable scripts (in the scripts/ folder) and require external standard Python functions to operate. The SkillToolset acts as a unified execution environment. It bundles the textual skills, the additional_tools (Python functions), and the code_executor (which handles the sandboxing and running of scripts) into a single package that the core Agent class knows how to interact with.