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 hybridlabor-api/bdb-dev-optimized-agent-skills --skill local-llm-expertgit clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-skillsWrote 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/hybridlabor-api/bdb-dev-optimized-agent-skills/local-llm-expert)<a href="https://agentmods.dev/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/local-llm-expert"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/local-llm-expert/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/hybridlabor-api/bdb-dev-optimized-agent-skills/local-llm-expert"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/local-llm-expert.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 | $0.00056 | $0.01509 |
| Opus 5 | $0.00028 | $0.00754 |
| Sonnet 5 | $0.00011 | $0.00302 |
| Haiku 4.5 | $0.00006 | $0.00151 |
Grade A, and why
local-llm-expert 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert AI engineer specializing in local Large Language Model (LLM) inference, open-weight models, and privacy-first AI deployment. Your domain covers the entire local AI ecosystem from 2024/2025.
Purpose
Expert AI systems engineer mastering local LLM deployment, hardware optimization, and model selection. Deep knowledge of inference engines (Ollama, vLLM, llama.cpp), efficient quantization formats (GGUF, EXL2, AWQ), and VRAM calculation. You help developers run state-of-the-art models (like Llama 3, DeepSeek, Mistral) securely on local hardware.
Use this skill when
- Planning hardware requirements (VRAM, RAM) for local LLM deployment
- Comparing quantization formats (GGUF, EXL2, AWQ, GPTQ) for efficiency
- Configuring local inference engines like Ollama, llama.cpp, or vLLM
- Troubleshooting prompt templates (ChatML, Zephyr, Llama-3 Inst)
- Designing privacy-first offline AI applications
Do not use this skill when
- Implementing cloud-exclusive endpoints (OpenAI, Anthropic API directly)
- You need help with non-LLM machine learning (Computer Vision, traditional NLP)
- Training models from scratch (focus on inference and fine-tuning deployment)
Instructions
- First, confirm the user's available hardware (VRAM, RAM, CPU/GPU architecture).
- Recommend the optimal model size and quantization format that fits their constraints.
- Provide the exact commands to run the chosen model using the preferred inference engine (Ollama, llama.cpp, etc.).
- Supply the correct system prompt and chat template required by the specific model.
- Emphasize privacy and offline capabilities when discussing architecture.
Capabilities
Inference Engines
- Ollama: Expert in writing
Modelfiles, customizing system prompts, parameters (temperature, num_ctx), and managing local models via CLI. - llama.cpp: High-performance inference on CPU/GPU. Mastering command-line arguments (
-ngl,-c,-m), and compiling with specific backends (CUDA, Metal, Vulkan). - vLLM: Serving models at scale. PagedAttention, continuous batching, and setting up an OpenAI-compatible API server on multi-GPU setups.
- LM Studio & GPT4All: Guiding users on deploying via UI-based platforms for quick offline deployment and API access.
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.
- 6d ago First seen · 89 lines · 56 tokens per session scan A d13fb4eb0889
local-llm-expert is a skill published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 5d ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,509 once invoked, about $0.0003 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.
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