A Python framework for modular, self-contained skill management for machines.

Mission • Architecture • Quick Start • Documentation • Contributing • Comparison • Changelog • Contact

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Skillware is an open-source framework and registry for modular, actionable Agent capabilities. It treats Skills as installable content, decoupling capability from intelligence. Just as apt-get installs software and pip installs libraries, skillware installs know-how for AI agents.

"I know Kung Fu." - Neo

Mission

The AI ecosystem is fragmented. Developers often re-invent tool definitions, system prompts, and safety rules for every project. Skillware supplies a standard to package capabilities into self-contained, installable units that work across Gemini, Claude, Ollama, GPT, and Llama. For the full story and roadmap, see our Vision.

A Skill in this framework provides everything an Agent needs to master a domain:

1. Logic: Executable Python so agents run real work, not guess it.
2. Cognition: System instructions and cognitive maps so any logical system uses the capability as intended.
3. Governance: Constitution, safety boundaries, and hard limits baked into the bundle.
4. Interface: Standardized tool schemas for any LLM or agent runtime.

Skill library

Browse capabilities by category in the Skill library or on our site ↗.

Architecture

This repository is organized into a core framework, a registry of skills, and documentation. Runnable provider scripts are indexed in examples/README.md.

Skillware/
├── docs/                       # Introduction, testing, skill catalog, usage guides (docs/usage/)
├── examples/                   # Provider reference scripts (Gemini, Claude, OpenAI, Ollama, ...)
├── skills/                     # Skill Registry
│   └── category/               # Domain boundaries (e.g., finance)
│       └── skill_name/         # The Skill bundle
│           ├── manifest.yaml   # Definition, schema, and constitution
│           ├── skill.py        # Executable Python logic
│           ├── instructions.md # Cognitive map for the LLM
│           ├── card.json       # Optional UI presentation metadata
│           └── test_skill.py   # Unit tests and schema validation
├── skillware/                  # Core Framework Package
│   ├── cli.py                  # Command-line interface
│   └── core/
│       ├── base_skill.py       # Abstract Base Class for skills
│       ├── env.py              # Environment Management
│       └── loader.py           # Universal Skill Loader and Model Adapter
├── templates/                  # Boilerplate templates for new skills
│   └── python_skill/           # Standard template with required files
└── tests/                      # Automated test suite

Quick Start

1. Installation

You can install Skillware directly from PyPI:

pip install skillware

Or for development, clone the repository and install in editable mode:

git clone https://github.com/arpahls/skillware.git
cd skillware
pip install -e .

Note: Individual skills may have their own dependencies. The SkillLoader validates manifest.yaml and warns of missing packages (e.g., requests, pandas) upon loading a skill.

2. Verify your installation

pip install "skillware[cli]"
skillware list

This prints a table of all locally available skills and confirms the install and path resolution are working. Running skillware with no arguments opens the interactive menu.

3. Configuration

Create a .env file with your API keys (e.g., Google Gemini API Key):

GOOGLE_API_KEY="your_key"

4. Usage Example (Gemini)

This example requires the Google SDK optional extra: pip install "skillware[gemini]" (local dev: pip install -e ".[gemini]"). See the Gemini usage guide for setup details.

import os
import google.genai as genai
from google.genai import types
from skillware.core.loader import SkillLoader
from skillware.core.env import load_env_file

# Load Environment
load_env_file()

# 1. Load the Skill from the Registry
# The loader reads the code, manifest, and instructions automatically
skill_bundle = SkillLoader.load_skill("finance/wallet_screening")
skill = skill_bundle["module"].WalletScreeningSkill(
    config={"ETHERSCAN_API_KEY": os.environ.get("ETHERSCAN_API_KEY")}
)

# 2. Client & Tool Setup
client = genai.Client()
tool = SkillLoader.to_gemini_tool(skill_bundle)       # The "Adapter"
system_instruction = skill_bundle['instructions']     # The "Mind"

# 3. Agent Loop
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Screen wallet 0xd8dA... for risks.",
    config=types.GenerateContentConfig(
        tools=[tool],
        system_instruction=system_instruction,
    ),
)

for part in response.candidates[0].content.parts:
    if part.function_call:
        result = skill.execute(dict(part.function_call.args))
        follow_up = client.models.generate_content(
            model="gemini-2.5-flash",
            contents=[
                "Use this tool result to answer the original request.",
                {
                    "function_response": {
                        "name": part.function_call.name,
                        "response": {"result": result},
                    }
                },
            ],
            config=types.GenerateContentConfig(
                tools=[tool],
                system_instruction=system_instruction,
            ),
        )
        print(follow_up.text)
    else:
        print(part.text)

For other providers and shared integration patterns, see the usage guides index, agent loops, Gemini, Claude, OpenAI, DeepSeek, Ollama, API keys for skills, and the skill usage template for contributors.

Documentation

Topic	Links
Introduction	Introduction · Vision · Comparison
Usage guides	Skill Library · Usage Guide · Examples · Agent Loops · API Keys · CLI
Contributing	Contributing · Agent Native Workflow · Testing · Changelog

Contributing

We are building the "App Store" for Agents. Skills are the main contribution, but documentation, tests, and framework fixes are welcome too. Human operators and supervised agents follow the same standards: scoped PRs, deterministic behavior, and verified tests.

See the Contributing row in Documentation for the full path, Contributing (types, skill standard, PR process), Agent Native Workflow (for autonomous and semi-autonomous agents), Testing (Black, Flake8, framework and skill pytest, pre-PR checklist), and Changelog (user-facing entries under [Unreleased]).

Also read the Agent Code of Conduct. Open PRs with the pull request template and complete only the sections that apply.

Comparison

Skillware differs from the Model Context Protocol (MCP), and Anthropic's Skills repository in several ways:

• Model Agnostic: Native adapters for Gemini, Claude, Ollama, and OpenAI.
• Code-First: Skills are executable Python packages, not just server specs.
• Runtime-Focused: Provides tools for the application, not just recipes for an IDE.

Read the full comparison here.

Contact

For questions, suggestions, or contributions, please open an issue or reach out to us:

• Email: skillware-os@arpacorp.net
• Enterprise: skills@arpacorp.net — enterprise skills, chaining, and forward deployed engineering
• Security: security@arpacorp.net — report bugs, vulnerabilities, or other sensitive issues (see SECURITY.md)
• Issues: GitHub Issues

For skill-specific questions or reaching a skill's maintainer, check issuer and author details on the skill card, in the repo Skill Library, or on our website's skills catalog ↗.

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Built & Maintained by ARPA Hellenic Logical Systems & the Community