open-weights
From the author
Use when choosing an open-weight LLM and clearing it for use — which family and size fit the task, the hardware and the budget, and above all whether the license permits shipping. Owns the license-class map (OSI-open versus custom-community versus non-commercial), the always-verify-the-model-card rule, size-to-VRAM budgeting and the quant formats. This layer decides WHICH model and whether the license allows it, then hands off. NOT downloading or hosting on the Hub (that is `huggingface`), NOT running it locally (that is `ollama`), NOT serving at throughput (that is `vllm`), NOT fine-tuning it (that is `finetuning`).
Install
npx skills add ericrisco/rsc-harness --skill open-weights -a claude-code
Installs to .claude/skills/open-weights/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
No warnings.
Why 85
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- ollamaericrisco/rsc-harnessUse when running open-weight LLMs locally with Ollama — pulling and tagging models, calling the local API, picking a quantization or GGUF, writing Modelfiles, and sizing VRAM and RAM for the machine at hand. NOT remote or managed GPU serving and autoscaling (that is `runpod`), NOT downloading raw weights or datasets (that is `huggingface`), NOT retrieval pipeline design (that is `rag`).Design★ 255
- serving-llms-vllmOrchestra-Research/AI-Research-SKILLsServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.DevOps★ 13.9K
- huggingfaceericrisco/rsc-harnessUse when running open models or working on the Hugging Face platform — the Inference Providers router or InferenceClient, Hub repos via the hf CLI, a dedicated Inference Endpoint with scale-to-zero, a Gradio Space with ZeroGPU, picking an open model by task/license/size, or loading one locally with transformers. NOT serving locally on your own machine (that is `ollama`), NOT renting your own GPU box (that is `runpod`), NOT hosted creative image APIs (that is `replicate-images`), NOT fine-tuning with trl/peft (that is `finetuning`).Media★ 255
- together-fireworksericrisco/rsc-harnessUse when calling open-weight LLMs on Together AI or Fireworks AI's OpenAI-compatible endpoints — `base_url` plus namespaced model id, the cheapest model that clears the bar, per-1M-token cost math, serverless vs batch vs dedicated. NOT renting GPUs to self-host weights (that is `runpod`), NOT running a model locally for free (that is `ollama`).Other★ 255
- agentsop-vllmagentsope/SkillAlchemyDecision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.Engineering★ 446
- agentsop-llm-engine-selectionagentsope/SkillAlchemyCross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.Other★ 446
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in