Selora AI: Library of Skills

Overview

Home Assistant blueprints let users share and reuse automations. OpenClaw skills let developers extend AI assistants with reusable capabilities. Selora AI Skills combine both ideas into a single concept: a curated, community-driven library of AI behaviors purpose-built for Home Assistant.

Each skill is a self-contained package that teaches Selora AI how to handle a specific domain — from energy optimization to security routines to comfort scenes. Skills are digested and configured by the embedded Claw running on each Selora instance, so they work locally, securely, and without cloud dependency.

Anyone can contribute a skill. The community curates, reviews, and improves the library over time — just like Home Assistant blueprints or OpenClaw’s skill ecosystem, but focused entirely on the smart home.

Example

User: “I want my home to save energy when nobody’s around.”

Selora AI: “There’s a community skill called Away Mode Energy Saver that uses your presence sensors and smart plugs to cut standby power when the house is empty. Want me to install it?”

User: “Yes, set it up.”

Selora AI: “Done — I’ve configured it with your motion sensors and smart plugs. It’ll activate after 30 minutes of no presence. You can adjust the delay anytime.”

Customer value

• Blueprints for AI: Like Home Assistant blueprints, but instead of static YAML automations, skills give Selora AI contextual intelligence — the AI understands intent, adapts to device availability, and handles edge cases conversationally.
• Community-driven: Anyone can author and share skills. The library grows organically with real-world use cases contributed by homeowners, installers, and developers.
• No code required: Users discover, install, and configure skills entirely through natural language in the Selora AI chat — no YAML, no templates, no dashboards.
• Local-first execution: Skills are digested by the embedded Claw on each Selora instance, ensuring they run locally and respect the user’s privacy.
• Hardware-aware: Skills declare their device requirements, so Selora AI only suggests skills compatible with the user’s actual setup.

Scope (first iterations)

• Skill specification: Define the format, metadata, and requirements for a Selora AI skill (device dependencies, configuration parameters, required integrations).
• Community repository: A public, Git-based repository where contributors submit, review, and version skills — inspired by the Home Assistant blueprints exchange and OpenClaw’s skill registry.
• Embedded Claw integration: The embedded Claw ingests skills from the library and makes them available to the local Selora AI instance.
• Conversational discovery & deployment: Users browse, install, and configure skills directly from the Selora AI chat interface — one conversation, zero YAML.
• Versioning & updates: Skills support semantic versioning; installed skills can be updated in-place when new versions are published.

Architecture (directional)

• Each skill is a structured package containing: metadata, device/integration requirements, prompt instructions, and optional configuration parameters.
• The community repository is a public Git repository with CI-based validation (linting, schema checks, security review).
• The embedded Claw periodically syncs the skill catalog and indexes available skills against the local device registry.
• When a user requests a skill, the Claw resolves device compatibility, walks the user through configuration, and activates the skill locally.

Target customers

• Homeowners: Access advanced AI behaviors with a single conversation — no technical knowledge needed.
• Installers: Deploy proven, standardized skill sets across client installations for consistent results.
• Developers & contributors: Share expertise with the community and build reputation through a public contribution model.

Open questions and risks

• Security & validation: How to sandbox and review community-contributed skills before they reach user instances — especially skills that interact with security-sensitive devices (locks, alarms, cameras).
• Skill quality: Establishing review criteria and a curation process to maintain library quality as contributions scale.
• Proactive recommendations: Integration with the Suggest Integrations & Devices feature to proactively recommend skills based on the user’s hardware.
• Attribution & incentives: Recognition or incentive models for active contributors.
• Offline catalog: How to handle skill discovery and updates when the instance has limited or no internet connectivity.

Related

• Selora AI: Embedded Claw (Epic #45): Roadmap — the runtime that digests and executes skills locally.
• Selora AI: Suggest Integrations & Devices (Epic #51): GitLab epic #51 — proactive hardware recommendations that can trigger skill suggestions.
• Selora AI: Proactive Automation Suggestions (Epic #47): Roadmap — complementary feature that suggests automations; skills provide the reusable building blocks.
• Selora AI: Create Scenes (Epic #46): Roadmap — skills can bundle scene-creation capabilities for specific use cases.