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 PracticalSwan/agent-skills --skill huggingface-local-modelsgit clone --depth 1 https://github.com/PracticalSwan/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/practicalswan/agent-skills/huggingface-local-models)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-local-models"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-local-models/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/practicalswan/agent-skills/huggingface-local-models"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-local-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Prompt Injection · line 117 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Data Exfiltration · line 84 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 | $0.00059 | $0.01679 |
| Opus 5 | $0.00030 | $0.00839 |
| Sonnet 5 | $0.00012 | $0.00336 |
| Haiku 4.5 | $0.00006 | $0.00168 |
Grade A, and why
huggingface-local-models 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 3d 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 http://localhost:8080/v1/chat/completions \ How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Face Local Models
Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with llama-cli or llama-server.
Default Workflow
- Search the Hub with
apps=llama.cpp. - Open
https://huggingface.co/<repo>?local-app=llama.cpp. - Prefer the exact HF local-app snippet and quant recommendation when it is visible.
- Confirm exact
.gguffilenames withhttps://huggingface.co/api/models/<repo>/tree/main?recursive=true. - Launch with
llama-cli -hf <repo>:<QUANT>orllama-server -hf <repo>:<QUANT>. - Fall back to
--hf-repoplus--hf-filewhen the repo uses custom file naming. - Convert from Transformers weights only if the repo does not already expose GGUF files.
Quick Start
Install llama.cpp
brew install llama.cpp
winget install llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make
Authenticate for gated repos
hf auth login
Search the Hub
https://huggingface.co/models?apps=llama.cpp&sort=trending
https://huggingface.co/models?search=Qwen3.6&apps=llama.cpp&sort=trending
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending
Run directly from the Hub
llama-cli -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
Run an exact GGUF file
llama-server \
--hf-repo unsloth/Qwen3.6-35B-A3B-GGUF \
--hf-file Qwen3.6-35B-A3B-UD-Q4_K_M.gguf \
-c 4096
Convert only when no GGUF is available
hf download <repo-without-gguf> --local-dir ./model-src
python convert_hf_to_gguf.py ./model-src \
--outfile model-f16.gguf \
--outtype f16
llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M
Smoke test a local server
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"messages": [
{"role": "user", "content": "Write a limerick about exception handling"}
]
}'
What ships with it
5 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.
- 3d ago Changed bf4fb062b81d
- 4d ago Changed 6ff270e571df
- 7d ago First seen · 165 lines · 59 tokens per session scan A 4a58676134b8
huggingface-local-models is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 59 tokens to every session and 1,679 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
huggingface-hub
HuggingFace hf CLI: search/download/upload models, datasets.
huggingface-hub
Hugging Face Hub — model discovery, download, inference, and upload.
huggingface-hub
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
free-model-manager
Free AI model management - discover, download, and manage free/open-source AI models from Ollama, HuggingFace, and other sources.
huggingface-hub
HuggingFace hf CLI: search/download/upload models, datasets.
huggingface-hub
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.