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 changing mesh-llm's patched llama.cpp Skippy ABI, runtime hooks, model introspection, tensor filtering, activation-frame execution, GGUF writer surface, upstream pin, or patch queue.
Use when changing mesh-llm's llama.cpp patch queue, upstream pin, prepare/build scripts, or carried RPC, MoE, and mesh-hook llama.cpp patches.
Use this skill when validating skippy staged execution against full-model execution, adding model families, changing split boundaries, testing activation wire dtypes, or diagnosing mismatch behavior.
Use this skill when inspecting GGUF models, planning layer ranges, generating or validating skippy package artifacts, fake packages for direct GGUFs, materialized stage cache behavior, or GGUF writer integration.
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.