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 fiveonecode/agent-skills --skill local-model-servinggit clone --depth 1 https://github.com/fiveonecode/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/fiveonecode/agent-skills/local-model-serving)<a href="https://agentmods.dev/skills/fiveonecode/agent-skills/local-model-serving"><img src="https://agentmods.dev/badge/skills/fiveonecode/agent-skills/local-model-serving/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/fiveonecode/agent-skills/local-model-serving"><img src="https://agentmods.dev/badge/skills/fiveonecode/agent-skills/local-model-serving.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 92 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00079 | $0.01450 |
| Opus 5.5 | $0.00032 | $0.00580 |
| Sonnet 5.5 | $0.00016 | $0.00290 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
local-model-serving scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://127.0.0.1:<port>/v1/chat/completions \ How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local Model Serving
Use this skill whenever a task needs local LLM inference on this machine. It is engine- and model-generic: the measured numbers come from a Qwen3.8-27B-class dense model (8-bit, ~28-30 GB resident) on a 128 GB Apple Silicon Mac, but the decision rules and settings transfer to any model served by the same two engine families.
The Two Engine Families (same model, two backends)
| MLX + speculative decoding (e.g. mlx-dspark) | llama.cpp (e.g. LM Studio) | |
|---|---|---|
| Model file | MLX 8-bit (e.g. mlx-community/…-8bit) + drafter |
GGUF Q8_0 (e.g. lmstudio-community/…-GGUF) |
| Port | e.g. http://127.0.0.1:8080/v1 |
e.g. http://127.0.0.1:1234/v1 |
| Context | model native (e.g. 262,144) | model native |
| Thinking-off knob | "enable_thinking": false |
"reasoning_effort": "none" |
| Short-context decode | ~1.7-2.2× faster (measured) | baseline |
| Long-context (100k+) | slower (spec-decode verify rounds double the huge-KV cost) | ~1.9× faster decode, ~linear prefill |
Both are OpenAI-compatible (/v1/chat/completions), so the same client code
works against either — only the base URL and the thinking-off parameter
differ.
Drafters (DSpark, DFlash 2, and similar speculative-decoding heads) are
modes of the MLX family, not a third engine. Do not install NVIDIA-only
stacks (SGLang, vLLM) or a third MLX GUI to A/B a drafter. A better drafter
can raise short-context decode; it does not remove the long-context
crossover, because every verify round still walks the full KV. Use the
drafter the local serving policy or helper names. llama.cpp may be LM Studio
or llama-server; that is still the same family.
First: The Memory Rule (crash prevention)
Never run both engines with a model resident at the same time. On a 128 GB machine, two resident 28 GB models plus the OS drove the system into ~20-46 GB of swap and crushed both engines' throughput (a long run was aborted, another finished at half speed). The same exclusivity rule applies on a 64 GB machine that can fit only one ~28 GB model. Each engine fits alone; both do not.
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.
- 1mo ago First seen · 106 lines · 79 tokens per session scan A b719ea437a75
local-model-serving is a skill published in the GitHub repository fiveonecode/agent-skills (19 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,450 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
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training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…