Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/davila7/claude-code-templates/optimization-ggufnpx skills add davila7/claude-code-templates --skill optimization-ggufgit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/davila7/claude-code-templates/optimization-gguf)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/optimization-gguf"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/optimization-gguf.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00048 | $0.03147 |
| Opus 5 | $0.00024 | $0.01573 |
| Sonnet 5 | $0.00010 | $0.00629 |
| Haiku 4.5 | $0.00005 | $0.00315 |
Grade A, and why
gguf-quantization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- gguf-quantization — 100% identical, 5 lines differ
- gguf-quantization — 100% identical, 0 lines differ
- gguf-quantization — 100% identical, 0 lines differ
- gguf-quantization — 100% identical, 0 lines differ
- gguf-quantization — 98% identical, 5 lines differ
- gguf-quantization — 97% identical, 12 lines differ
- gguf-quantization — 95% identical, 3 lines differ
- research-gguf — 92% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GGUF - Quantization Format for llama.cpp
The GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.
When to use GGUF
Use GGUF when:
- Deploying on consumer hardware (laptops, desktops)
- Running on Apple Silicon (M1/M2/M3) with Metal acceleration
- Need CPU inference without GPU requirements
- Want flexible quantization (Q2_K to Q8_0)
- Using local AI tools (LM Studio, Ollama, text-generation-webui)
Key advantages:
- Universal hardware: CPU, Apple Silicon, NVIDIA, AMD support
- No Python runtime: Pure C/C++ inference
- Flexible quantization: 2-8 bit with various methods (K-quants)
- Ecosystem support: LM Studio, Ollama, koboldcpp, and more
- imatrix: Importance matrix for better low-bit quality
Use alternatives instead:
- AWQ/GPTQ: Maximum accuracy with calibration on NVIDIA GPUs
- HQQ: Fast calibration-free quantization for HuggingFace
- bitsandbytes: Simple integration with transformers library
- TensorRT-LLM: Production NVIDIA deployment with maximum speed
Quick start
Installation
# Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
# Build (CPU)
make
# Build with CUDA (NVIDIA)
make GGML_CUDA=1
# Build with Metal (Apple Silicon)
make GGML_METAL=1
# Install Python bindings (optional)
pip install llama-cpp-python
Convert model to GGUF
# Install requirements
pip install -r requirements.txt
# Convert HuggingFace model to GGUF (FP16)
python convert_hf_to_gguf.py ./path/to/model --outfile model-f16.gguf
# Or specify output type
python convert_hf_to_gguf.py ./path/to/model \
--outfile model-f16.gguf \
--outtype f16
Quantize model
# Basic quantization to Q4_K_M
./llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M
# Quantize with importance matrix (better quality)
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 428 lines · 48 tokens per session scan A 11016e03b2b5
gguf-quantization is a skill published in the GitHub repository davila7/claude-code-templates (30,532 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 3,147 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.