SkylarKitchen/skills
Learn how a small, well‑structured skill set can turn generic LLM output into actionable design documentation.
The core of the repository is a collection of declarative skill definitions. Each skill bundles a prompt template, a JSON schema for inputs, and a post‑processing routine that emits HTML. The design follows the function‑calling pattern popularized by modern LLM APIs, so an agent can invoke a skill by name and receive a typed response directly.
At present the library includes a 3D build guide skill that takes a target object description and returns a numbered assembly sequence, part dimensions, and a rendered preview. The preview is generated as an SVG‑based HTML snippet, allowing immediate embedding in web dashboards or design tools. Additional skills cover material estimation and export to common CAD formats, all built on the same schema‑driven framework.
Because the skills are self‑contained, developers can drop them into any LangChain, LlamaIndex, or custom LLM orchestration layer with a single import. The repository ships a thin Python wrapper that registers the skills with the OpenAI function‑call API, handling validation and error mapping automatically. This lowers the barrier for engineers who want to prototype design assistants without writing prompt engineering code from scratch.
The repo has attracted 62 stars, indicating a niche but growing interest among makers, CAD plugin authors, and AI‑augmented design teams. Users appreciate the reusable, HTML‑first output that sidesteps the need for separate rendering pipelines. The trade‑off is that the current skill set is limited to static instruction generation; dynamic simulation or parametric modeling would require extending the framework with more complex back‑ends.
TakeawaySkylarKitchen/skills provides plug‑in‑style function‑callable design skills that let agents output ready‑to‑use 3D build guides as HTML with minimal integration effort.