Use this skill as the mandatory starting point whenever inspecting, running, debugging, defining, editing, reviewing, or documenting MeshLLM CI/CD. It governs GitHub Actions workflows and local actions, triggers and routing, runners, caches, artifacts,…
Use this skill when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally built release bundles on user-approved real hosts and private meshes,…
Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.
Use this skill when deploying, installing, launching, or serving mesh-llm on a remote Linux GPU node (rented GPUs like Vast.ai or RunPod, or a self-managed CUDA server), including installing the CUDA build, choosing a model, keeping it alive under a…
Use this skill when deploying, installing, launching, or serving mesh-llm on a macOS machine (local or remote over SSH), including installing a release, shipping a dev build bundle, codesign/quarantine fixes, choosing a model, and verifying it serves.
Use when converting Hugging Face SafeTensors checkpoints into split BF16 GGUF model repos with skippy-quantize on Hugging Face Jobs or a local machine, then publishing the artifact to Hugging Face.
Use when creating, monitoring, validating, or documenting low-memory Hugging Face Jobs or local runs that quantize split BF16/FP16 GGUF model repos into custom quant GGUF repos with skippy-quantize.
Use when changing mesh-llm automation or CLI flows that discover Hugging Face GGUF models, plan CPU Hugging Face Jobs for layer-package splitting, estimate max cost, or publish skippy layer packages/catalog entries.