AI model or service
llama.cpp agent skills, page 2
llama.cpp skills, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 49 | 49.Pi Agent Builds with and operates Pi, the minimal terminal coding harness. | K-Dense-AI/ | 48k | 1 repo | ~2.1k | Automated safety check: Pass | MIT | 2 days ago |
| 50 | Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. | dpearson2699/ | 1.2k | — | ~3.4k | Automated safety check: Pass | Unknown | 2 mo ago |
| 51 | A skill your agent uses when changing mesh-llm's llama.cpp patch queue, upstream pin, prepare/build scripts, or carried RPC, MoE, and mesh-hook llama.cpp patches. | Mesh-LLM/ | 3.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | today |
| 52 | A skill your agent uses 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… | Mesh-LLM/ | 3.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | today |
| 53 | A skill your agent uses when benchmarking Skippy exact-prefix cache across model families, comparing Skippy against llama-server, producing README benchmark tables, updating… | Mesh-LLM/ | 3.5k | — | ~764 | Automated safety check: Pass | Apache-2.0 | today |
| 54 | A skill your agent uses when inspecting GGUF models, planning layer ranges, generating or validating skippy package artifacts, fake packages for direct GGUFs, materialized stage cache behavior, or… | Mesh-LLM/ | 3.5k | — | ~588 | Automated safety check: Pass | Apache-2.0 | today |
| 55 | Transcribes a single PCM16 WAV file to plain UTF-8 text locally through a standard-library Python wrapper around native SenseVoice Small F16 GGUF FunASR llama.cpp runtimes, preferring cross-vendor… | godot-fun/ | 182 | — | ~707 | Automated safety check: Pass | MIT | today |
| 56 | Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. | sickn33/ | 47k | 1 repo | ~1.1k | Automated safety check: Pass | Apache-2.0 | today |
| 57 | A skill your agent uses when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior. | Mesh-LLM/ | 3.5k | — | ~260 | Automated safety check: Pass | Apache-2.0 | today |
| 58 | Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. | sickn33/ | 47k | 2 repos | ~1.6k | Automated safety check: Pass | MIT | today |
| 59 | A skill your agent uses when certifying a GGUF model family for skippy stage-split serving, reviewing capability data, promoting family evidence into topology policy, or updating staged split… | Mesh-LLM/ | 3.5k | — | ~568 | Automated safety check: Pass | Apache-2.0 | today |
| 60 | Fine-tune and post-train LLMs with Unsloth Core on a single consumer GPU: VRAM sizing, LoRA/QLoRA, GRPO/DPO, chat-template correctness, and GGUF export. | sickn33/ | 47k | 1 repo | ~4.1k | Automated safety check: Pass | Apache-2.0 | today |
| 61 | Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. | wshobson/ | 40k | — | ~2k | Automated safety check: Pass | MIT | 3 days ago |
| 62 | Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. | waybarrios/ | 533 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 63 | 63.Cost Local Cost per million tokens on hardware you own (Ollama, llama.cpp, vLLM, LM Studio) from watts, electricity price, hardware price and measured tokens/second, and the utilisation at which local beats a… | ruvnet/ | 74k | — | ~336 | Automated safety check: Notes | MIT | today |
| 64 | Build WAN 2.2 First-Last-Frame video workflows. An agent skill from artokun/comfyui-mcp. | artokun/ | 795 | — | ~5.1k | Automated safety check: Pass | MIT | 2 days ago |
| 65 | 65.Commit Trace Trace a commit to its published npm versions, including transitive SDK resolution with time-aware accuracy | tetherto/ | 681 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | today |
| 66 | Explains how model files, checkpoints, GGUF quantization, and the Model Manager work in HOT-Step CPP. | scragnog/ | 171 | — | ~6.6k | Automated safety check: Pass | MIT | today |
| 67 | [omh] Self-hosted LLM serving on GPUs: choose the serving engine and quantization from decision tables, prepare deployment as an idempotent runbook with observed-only verification, and measure the… | rlaope/ | 3.2k | — | ~2.2k | Automated safety check: Pass | MIT | today |
| 68 | Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. | agentsope/ | 459 | — | ~6.1k | Automated safety check: Pass | MIT | 1 mo ago |
| 69 | Decision SOP for serving LLMs with vLLM. An agent skill from agentsope/SkillAlchemy. | agentsope/ | 459 | — | ~6.1k | Automated safety check: Pass | MIT | 1 mo ago |
| 70 | Redact, anonymize, sanitize, or remove PII locally with Distil-PII and llama.cpp; keep personal data and secret values out of model context, logs, and chat. | HybridAIOne/ | 158 | — | ~1k | Automated safety check: Pass | MIT | today |
| 71 | Converts one or more images into faithful text descriptions or OCR with the local 1.3B MiniCPM-V 4.6 GGUF model through llama.cpp, automatically preferring an available Vulkan GPU and falling back… | godot-fun/ | 182 | — | ~813 | Automated safety check: Pass | MIT | today |
| 72 | Serve an MLX or Hugging Face safetensors model already on this Mac with mlx-lm's server and join it to the person's fleet. | autonomous-ai/ | 1.1k | — | ~1.9k | Automated safety check: Pass | MIT | today |
| 73 | Reuse what Ollama already has on this computer: adopt a running Ollama server into the person's fleet, or serve an Ollama-downloaded model without Ollama. | autonomous-ai/ | 1.1k | — | ~2.2k | Automated safety check: Notes | MIT | today |
| 74 | 74.Local Models Run quick, offline, private LLM tasks on local models via llama.cpp, reusing models already downloaded by Ollama. | glebis/ | 389 | — | ~1.4k | Automated safety check: Pass | MIT | 11 days ago |
| 75 | Prepare export and downstream evaluation handoff for a planned or completed Quark PTQ run. | amd/ | 181 | — | ~1.5k | Automated safety check: Pass | MIT | 10 days ago |
| 76 | 在本机 Mac 或 Apple Silicon 上部署 Gemma 4 12B。本地安装/升级 llama.cpp,下载 GGUF 量化模型,用 llama-server 暴露 OpenAI-compatible API,或用 Ollama 暴露本地模型服务;按用户需求在默认 Q4KM、64K/128K 长上下文、QAT Q40 @ 256K、左右对比演示之间选择,配置 tmux… | majiayu000/ | 286 | — | ~875 | Automated safety check: Notes | MIT | today |
| 77 | Summarises the delta between a tool's latest release and the last summary the user saw. | sammcj/ | 162 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | today |
| 78 | 78.Setup Set up, install, and configure CONFIDE local de-identification — installs Python deps (natasha, scrubadub, phonenumbers, pymorphy2), ensures Ollama + pulls the default qwen2.5:3b model, detects… | glebis/ | 389 | — | ~1.1k | Automated safety check: Pass | MIT | 11 days ago |
| 79 | 79.Ollama A skill your agent uses 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… | ericrisco/ | 167 | — | ~2.8k | Automated safety check: Pass | MIT | today |
| 80 | 80.Litellm Operate, configure, secure, and troubleshoot the LiteLLM AI gateway (proxy) and Python SDK: run the proxy (litellm --config), route to 100+ providers through one OpenAI-compatible API, configure… | magnus919/ | 113 | — | ~4.2k | Automated safety check: Notes | MIT | yesterday |
| 81 | 81.Vllm Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API… | magnus919/ | 113 | — | ~4.1k | Automated safety check: Notes | MIT | yesterday |
| 82 | Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. | AnastasiyaW/ | 154 | — | ~794 | Automated safety check: Pass | MIT | today |
| 83 | 83.Llama Cpp Run quantized LLMs locally with llama.cpp — CPU+GPU inference, GGUF format, OpenAI-compatible server, and Python bindings. | AlexAI-MCP/ | 135 | — | ~2.3k | Automated safety check: Pass | MIT | 6 mo ago |
| 84 | 84.Ltxv2 Video Build Lightricks LTX-2 / LTX-2.3 video workflows covering text-to-video, image-to-video, GGUF and bundled checkpoints, distilled model, camera control LoRAs, synchronized audio, two-stage upscaling… | artokun/ | 795 | — | ~6.8k | Automated safety check: Warn | MIT | 2 days ago |
| 85 | 85.Finetuning A skill your agent uses when adapting an open-weight model to a target form or behavior — tone, output format, reasoning pattern — via LoRA/QLoRA or full fine-tuning with TRL SFTTrainer, then… | ericrisco/ | 167 | — | ~3.8k | Automated safety check: Pass | MIT | today |
| 86 | 86.Unsloth A skill your agent uses when fine-tuning an open-weight LLM fast on ONE GPU with low VRAM — Unsloth's fast model loaders with 4-bit QLoRA and the trl trainer, response-only loss masking so the… | ericrisco/ | 167 | — | ~3.6k | Automated safety check: Pass | MIT | today |
| 87 | 87.Llama Cpp Operate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. | magnus919/ | 113 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 88 | Plan and execute production ML engineering work — model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, lineage, feature parity… | magnus919/ | 113 | — | ~1.5k | Automated safety check: Pass | MIT | yesterday |
| 89 | 89.Nx Matting 使用本地 BiRefNet GGUF 模型完成图片或视频抠图、人物抠图、主体分割和背景移除,并输出透明 PNG、MOV 或 WebM。适用于用户提到图片抠图、照片去背景、人像透明图、视频抠图、透明视频、BiRefNet、JPG/PNG/BMP/WebP 图片,或 MP4/MOV/WebM 视频的场景;无需 Python、PyTorch 或 CUDA。 | aiskillstore/ | 430 | — | ~748 | Automated safety check: Pass | MIT | today |