Skillware
Skillware is a Python framework that equips AI agents with deterministic, modular skills instead of prompt-based instructions. The framework uses a standardized manifest and execute() contract, with model-agnostic SkillLoader adapters that let developers swap LLM providers while keeping the same skill interfaces. Skills are installed as pip packages with category-specific extras, such as a local background remover and a Gmail handler with preview-before-send confirmation.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI agents often rely on prompting to perform specific tasks, which can lead to inconsistent, non-deterministic outputs. Developers building custom agents also lack a standardized way to package and reuse tools across different LLM providers and agent frameworks.
Solution
Skillware provides a Python framework that equips agents with deterministic, modular skills instead of relying on prompt engineering. Each skill follows a standardized manifest and execute() contract, ensuring predictable outcomes. The model-agnostic SkillLoader adapters translate the same skill bundle for different hosts, so developers can swap models or providers while keeping the same skill paths and interfaces. Skills are installed as pip packages with category-specific extras, enabling a clean, component-based approach to extending agent capabilities. The framework is framework-agnostic, meaning it works without LangChain or other agent tooling. Developers can load skills with SkillLoader.load_skill("category/skill_name") and inject bundles directly into minimal agent loops.
Target Audience
Primary users are Python developers and engineers building custom AI agents or extending existing LLM-based systems, especially those who need reliable, tool-based execution across varying model providers.
Features
- Deterministic skill execution via a standardized manifest and execute() contract, eliminating prompt-based variability
- Model-agnostic SkillLoader adapters that translate the same skill bundle across different LLM hosts and providers
- Category-based pip install extras such as creative/bg_remover and office/gmail_handler for granular skill acquisition
- Framework-agnostic design that integrates directly into minimal agent loops without requiring LangChain or similar orchestration libraries
- Skills with real-world utility, e.g., transparent background removal executed locally via rembg with no cloud API, and deterministic email send workflow requiring explicit confirmation before transmission
- Email handler for agents using IMAP/SMTP with a dedicated Gmail account and App Password, with preview before send (confirmed: true parameter)