adk-skills-agent

ADK Skills

Bring Agent Skills to Google's Agent Development Kit (ADK)

adk-skills is a Python library that enables Google ADK agents to discover, load, and use skills in the standard Agent Skills format. Write skills once, use them across Claude, ADK, and any platform that supports the Agent Skills standard.

🚀 Quick Start

Installation

From PyPI:

uv pip install adk-skills-agent

Development Version:

git clone https://github.com/manojlds/adk-skills.git
cd adk-skills
uv sync

Basic Usage

from google.adk.agents import Agent
from adk_skills_agent import SkillsRegistry

async def create_agent() -> Agent:
    registry = SkillsRegistry()
    registry.discover(["./skills"])

    # Pre-render the available skills once during async setup so the model can
    # see what it may activate from the use_skill tool description.
    available_skills_xml = await registry.to_prompt_xml()

    return Agent(
        name="assistant",
        model="gemini-2.5-flash",
        instruction="You are a helpful assistant.",
        tools=[
            registry.create_use_skill_tool(available_skills_xml=available_skills_xml),
            registry.create_read_reference_tool(), # Optional: read reference files
        ],
    )

# Agent can now discover and activate skills as needed!

Runtime read methods are async in 0.4.0+: await registry reads, prompt formatters, helper functions, SkillsAgent.build(), and the generated ADK tool callables.

✨ Features

• 🎯 Standard Compliance: 100% compatible with agentskills.io specification
• 📦 On-Demand Loading: Skills activated only when needed (~50-100 tokens per skill)
• 🔌 Pluggable Skill Sources: Plug the built-in filesystem source alongside your own sources (database, remote registry, object storage, …) through a single SkillSource interface
• 📁 Multi-file Skill Packages: Read SKILL.md, references, and binary assets from any backend via the generic list_files / read_file API
• 🚀 Simple Integration: Tool-based pattern following OpenCode's approach
• 🤖 Custom Agent Class: SkillsAgent for easy agent creation with built-in skills support
• 💉 Prompt Injection: Inject skills directly into system prompts (XML or text format)
• ✅ Validation: Validate skills against the agentskills.io specification
• 🛠️ Helper Functions: Convenient utilities like with_skills(), create_skills_agent()
• 📚 Well Documented: Based on reference implementations

📖 What are Agent Skills?

Agent Skills are folders of instructions, scripts, and resources that AI agents can discover and use. They follow an open standard published at agentskills.io, making capabilities portable across different AI platforms.

Skill Structure

my-skill/
├── SKILL.md           # Instructions and metadata
├── scripts/           # Executable Python/Bash scripts
├── references/        # Documentation and resources
└── assets/            # Templates and binary files

Example SKILL.md

---
name: web-scraper
description: Extract content from websites efficiently and ethically
---

# Web Scraping Skill

Use this skill to extract structured data from websites.

## When to Use
- Extracting product information
- Gathering research data
- Content monitoring

## Guidelines
- Respect robots.txt
- Use rate limiting
- Cache responses

File References (references/)

Use references/ for long-form documentation and link to it from SKILL.md using relative paths:

See `references/api-docs.md` for the full API guide.

When a skill is activated, the use_skill tool returns a base_directory. Use that path (or call read_reference) to load reference files on-demand:

from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
registry.discover(["./skills"])

use_skill = registry.create_use_skill_tool()
read_reference = registry.create_read_reference_tool()

skill = await use_skill("web-scraper")
ref = await read_reference("web-scraper", "api-docs.md")
print(ref["content"])

🎓 Examples

Discover Skills

from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
count = registry.discover(["./skills", "~/.adk/skills"])

print(f"Found {count} skills")
for meta in await registry.list_metadata():
    print(f"  - {meta.name}: {meta.description}")

Tool-Based Activation

from google.adk.agents import Agent
from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
registry.discover(["./skills"])
available_skills_xml = await registry.to_prompt_xml()

# Skills are listed in the use_skill tool's description
# Agent activates them on-demand by calling the tool
agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
    tools=[
        registry.create_use_skill_tool(available_skills_xml=available_skills_xml),
        registry.create_read_reference_tool(),
    ]
)

# When agent calls use_skill(name="calculator"),
# it receives the full skill instructions

Multi-Agent with Different Skills

from google.adk.agents import Agent
from adk_skills_agent import SkillsRegistry

# Each agent gets its own registry with different skills

# Customer service agent
cs_registry = SkillsRegistry()
cs_registry.discover(["./skills/customer-service"])
cs_available_skills_xml = await cs_registry.to_prompt_xml()

cs_agent = Agent(
    name="customer_service",
    model="gemini-2.5-flash",
    tools=[cs_registry.create_use_skill_tool(available_skills_xml=cs_available_skills_xml)]
)

# Research agent
research_registry = SkillsRegistry()
research_registry.discover(["./skills/research"])
research_available_skills_xml = await research_registry.to_prompt_xml()

research_agent = Agent(
    name="researcher",
    model="gemini-2.5-flash",
    tools=[
        research_registry.create_use_skill_tool(
            available_skills_xml=research_available_skills_xml,
        )
    ]
)

🔥 Advanced Usage

Prompt Injection Utilities

Inject skills directly into system prompts instead of using tools:

from google.adk.agents import Agent
from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
registry.discover(["./skills"])

# Get skills as XML for prompt injection
xml_prompt = await registry.to_prompt_xml()
# Returns: <available_skills>...</available_skills>

# Get skills as plain text
text_prompt = await registry.to_prompt_text()
# Returns: Available Skills: - skill-name: description

# Or inject directly into an instruction (recommended)
base_instruction = "You are helpful."
full_instruction = await registry.inject_skills_prompt(base_instruction, format="xml")
# Returns: "You are helpful.\n\n<available_skills>...</available_skills>"

# Use with agent
agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction=full_instruction,
)

Pluggable Skill Sources

Starting in 0.2.0, SkillsRegistry is composed of one or more skill sources. A source is anything that implements the SkillSource abstract base class and knows how to list metadata, load a full skill, and (optionally) expose the files that back a skill package.

The registry ships with one built-in source:

• FilesystemSkillSource — default source backing registry.discover(...). Use this for engineering-owned skills that live in your repo as directories of SKILL.md files.

Everything else — database schemas, remote registries, managed catalogues, object storage — is a custom source. This keeps adk-skills-agent focused on the generic abstractions; applications own the storage layer that best fits their data model (versioning, publishing, binary assets, access control, …). A working, multi-file SQLite-backed source lives in tests/integration/test_sqlite_skill_source.py and is the recommended starting point for writing your own.

The minimal shape of a custom source:

from adk_skills_agent import SkillSource, SkillsRegistry
from adk_skills_agent.core.models import Skill, SkillMetadata


class MyDatabaseSource(SkillSource):
    name = "my-db"

    async def list_metadata(self) -> list[SkillMetadata]:
        ...

    async def load_skill(self, name: str) -> Skill:
        ...

    # Implement list_files / read_file if your source stores multi-file
    # skill packages (references, assets, binaries). ``read_reference`` is
    # provided by the registry automatically on top of ``read_file`` — no
    # override needed.


registry = SkillsRegistry()
registry.discover(["./skills"])            # filesystem source
registry.add_source(MyDatabaseSource())    # your source

Collision policy

Skill names are globally unique across all registered sources. If two sources advertise a skill with the same name, await registry.list_metadata() (and any lookup that needs to enumerate metadata) raises SkillSourceCollisionError. This is intentional: the registry refuses to guess which version to serve and expects you to resolve the conflict at the source level (rename, namespace, or remove).

What sources implement

Each source owns its own storage and decides which capabilities it can offer:

Dynamic sources backed by databases, remote registries, object storage, or other mutable stores should override refresh() and clear_cache() so freshness stays with the storage implementation. Static or prompt-only sources can keep the default no-op implementations.

Sources that cannot honour an optional capability should leave the default in place; the registry converts the resulting NotImplementedError into a SkillExecutionError with a clear "source does not support …" message. For example, a prompt-only database source that does not implement list_files / read_file will cause any call that needs them to fail cleanly rather than silently.

Reference reads are a registry-level concern

read_reference is not part of the SkillSource contract. The registry implements it once, on top of whatever source owns the skill, by:

1. Normalising the caller's input with adk_skills_agent.core.paths.normalize_skill_reference ("guide.md" -> "references/guide.md", "assets/template.json" passes through, "SKILL.md" passes through, etc.).
2. Delegating to source.read_file(skill_name, normalized_path).
3. Refusing binary payloads with a clear "use read_file() for binary assets" message.
4. Wrapping the text content in a ReferenceFile whose path is skill-root-relative (e.g. "references/guide.md") — source-agnostic.

Custom sources only need a correct read_file; the reference UX is free. Path helpers are also exported for source authors that need them directly:

from adk_skills_agent import (
    normalize_skill_reference,
    validate_skill_root_relative_path,
)

Generic file access (list_files / read_file)

Every source that stores multi-file skill packages should expose two methods:

• list_files(skill_name) -> list[SkillFile] enumerates every file in a skill package (SKILL.md, anything under references/, assets/, and anything else the skill author included) with path, size, MIME type and a content hash.
• read_file(skill_name, relative_path) -> SkillFile fetches a single file and returns a SkillFile with exactly one of text_content / binary_content populated.

FilesystemSkillSource implements both methods. Custom sources that back a richer storage layer (a database schema that stores every skill file separately, an object-storage adapter, a remote catalogue, …) should implement these to expose the full skill contents. Sources that cannot enumerate or read files leave the defaults in place, and the registry surfaces a clear SkillExecutionError when a caller needs them.

from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
registry.discover(["./skills"])

for meta in await registry.list_files("pdf-processing"):
    print(meta.relative_path, meta.mime_type, meta.size_bytes)

template = await registry.read_file("pdf-processing", "assets/template.json")
assert template.is_text
print(template.text_content)

Relaxed read_reference semantics

registry.read_reference(skill, path) accepts any path relative to the skill root:

• "guide.md" -> resolves to references/guide.md (backwards compat)
• "guides/intro.md" -> resolves to references/guides/intro.md (nested refs)
• "references/guide.md" -> resolves as-is
• "assets/template.json" -> resolves as-is (useful for LLM-readable assets)
• "SKILL.md" -> resolves as-is

Binary files are rejected with a SkillExecutionError — use read_file for those. Paths with .. segments are refused before source access, and the filesystem source also refuses symlinks that leave the skill root. When a reference is missing, the error message includes an Available: [...] hint listing sibling files in the same directory (when the owning source supports list_files).

Skills Validation

Validate skills against the agentskills.io specification:

from adk_skills_agent import SkillsRegistry

registry = SkillsRegistry()
registry.discover(["./skills"])

# Validate all skills
results = await registry.validate_all(strict=True)
for name, result in results.items():
    if not result.valid:
        print(f"{name}: {result.errors}")
    if result.warnings:
        print(f"{name}: {result.warnings}")

# Validate specific skill
result = await registry.validate_skill_by_name("my-skill")
if result.valid:
    print("Skill is valid!")

SkillsAgent - Custom Agent Class

Use the SkillsAgent class for easy agent creation with built-in skills support:

from adk_skills_agent import SkillsAgent

# Create agent with skills integrated
agent = SkillsAgent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction="You are a helpful assistant.",
    skills_directories=["./skills"],
    auto_inject_prompt=True,  # Inject skills into prompt
    prompt_format="xml",       # or "text"
    validate_skills=True,      # Validate during build()
    include_reference_tool=True,
)

# Get the configured ADK agent
adk_agent = await agent.build()

Helper Functions

with_skills()

Add skills to an existing agent:

from google.adk.agents import Agent
from adk_skills_agent import with_skills

# Create standard agent
agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
)

# Add skills support
agent = await with_skills(agent, ["./skills"])

create_skills_agent()

Create an agent with skills in one call:

from adk_skills_agent import create_skills_agent

agent = await create_skills_agent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction="You are helpful.",
    skills_directories=["./skills"],
)

inject_skills_prompt()

Inject skills into an instruction string. Supports two patterns:

from adk_skills_agent import inject_skills_prompt, SkillsRegistry

# Pattern 1: Directory-based (discovers skills)
instruction = "You are a helpful assistant."
full_instruction = await inject_skills_prompt(
    instruction,
    directories=["./skills"],
    format="xml"  # or "text"
)

# Pattern 2: Registry-based (more efficient, reuses existing registry)
registry = SkillsRegistry()
registry.discover(["./skills"])
full_instruction = await inject_skills_prompt(
    instruction,
    registry=registry,
    format="xml"
)

# Or use the registry method directly
full_instruction = await registry.inject_skills_prompt(instruction, format="xml")

Integration Patterns

Choose between two alternative patterns (not both simultaneously):

Pattern 1: Tool-Based (Default - OpenCode Pattern) ✅

# Skills listed in tool description, activated on-demand
registry = SkillsRegistry()
registry.discover(["./skills"])
available_skills_xml = await registry.to_prompt_xml()
agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction="You are helpful.",  # NO skills in prompt
    tools=[
        registry.create_use_skill_tool(available_skills_xml=available_skills_xml),
        registry.create_read_reference_tool(),
    ]
)

Pattern 2: Prompt Injection 🆕

# Skills in system prompt, NOT in tool description (avoids duplication)
registry = SkillsRegistry()
registry.discover(["./skills"])
prompt = await registry.to_prompt_xml()

agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction=f"You are helpful.\n\n{prompt}",  # Skills in prompt
    tools=[
        registry.create_use_skill_tool(),  # No XML duplicated in tool description
        registry.create_read_reference_tool(),
    ]
)

skills_agent = SkillsAgent(
    name="assistant",
    model="gemini-2.5-flash",
    skills_directories=["./skills"],
    auto_inject_prompt=True,  # Automatically omits skills from tool description
)
agent = await skills_agent.build()

Why Not Both? Listing skills in both prompt and tool description wastes tokens with no benefit. Choose one pattern based on your needs.

🏗️ Project Status

Current Phase: MVP Complete ✅ → Phase 2 in Progress

• Core models and parsers
• Skills discovery and registry
• Validation system
• use_skill tool for activation
• read_reference tool
• Working examples
• 300+ tests passing
• Advanced executors and sandboxing
• Advanced features
• Public release

🎯 Try It Now

Run the examples to see it in action:

Basic Example:

uv run python examples/basic_example.py

This demonstrates:

• Discovering 2 example skills
• Creating ADK tools
• Activating a skill on-demand
• Reading reference files

Advanced Example:

uv run python examples/advanced_example.py

This demonstrates:

• Prompt injection utilities (XML and text formats)
• Skills validation features
• SkillsAgent custom agent class
• Helper functions (with_skills, create_skills_agent, inject_skills_prompt)
• Common integration patterns

Example skills live in examples/skills/.

📚 Documentation

• This README contains the primary usage guide.
• Example scripts: examples/basic_example.py, examples/advanced_example.py
• Example skills: examples/skills/

🤝 Contributing

We welcome contributions! This project is in active development.

Development Setup

git clone https://github.com/manojlds/adk-skills.git
cd adk-skills
uv sync --all-extras  # Creates venv and installs all dependencies
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows
uv run pytest  # Run tests

🔗 Related Projects

• Google ADK Python - Agent Development Kit
• Agent Skills Spec - Open standard for agent capabilities
• Anthropic Skills - Public skills repository

📄 License

Apache 2.0 License - see LICENSE file for details

🙏 Acknowledgments

• Google for creating the Agent Development Kit
• Anthropic for pioneering the Agent Skills standard
• The agentskills.io community for maintaining the specification

---

Status: MVP Complete | Version: 0.4.1 | Python: 3.9+

For questions or support, please open an issue on GitHub.