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 skills add graniet/kheish --skill ggufgit clone --depth 1 https://github.com/graniet/kheishWrote 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/graniet/kheish/gguf)<a href="https://agentmods.dev/skills/graniet/kheish/gguf"><img src="https://agentmods.dev/badge/skills/graniet/kheish/gguf/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/graniet/kheish/gguf"><img src="https://agentmods.dev/badge/skills/graniet/kheish/gguf.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00042 | $0.03289 |
| Opus 5 | $0.00021 | $0.01644 |
| Sonnet 5 | $0.00008 | $0.00658 |
| Haiku 4.5 | $0.00004 | $0.00329 |
Grade A, and why
gguf 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 9d ago.
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.
This is a copy
91% identical to gguf-quantization — 32 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
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
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.
- 9d ago First seen · 452 lines · 42 tokens per session scan A 057f5af7b819
gguf is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 3,289 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to gguf-quantization, differing in 32 lines, and is treated as a copy.
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