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/waybarrios/opencode-power-pack/huggingface-local-modelsnpx skills add waybarrios/opencode-power-pack --skill huggingface-local-modelsgit clone --depth 1 https://github.com/waybarrios/opencode-power-packWrote 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/waybarrios/opencode-power-pack/huggingface-local-models)<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/huggingface-local-models"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/huggingface-local-models.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 5.1 | $0.00059 | $0.01113 |
| Opus 5 | $0.00030 | $0.00557 |
| Sonnet 5 | $0.00012 | $0.00223 |
| Haiku 4.5 | $0.00006 | $0.00111 |
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 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.
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 \ This is a copy
92% identical to huggingface-local-models — 5 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 — 113 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
With llama-server running (as started above), verify it responds:
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
3 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.
- 6d ago First seen · 113 lines · 59 tokens per session scan A 364c17e5e12f
huggingface-local-models is a skill published in the GitHub repository waybarrios/opencode-power-pack (490 stars, last pushed 3d ago), licensed MIT. It adds 59 tokens to every session and 1,113 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to huggingface-local-models, differing in 5 lines, and is treated as a copy.
Other skills, from other repositories
当用户需要对PDF文件进行任何操作时,请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片,以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF,请使用此技能。.
make_plan
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via listagents and chatwithagent, emphasizing that the plan is executed by the requester, not by the consulted Agent.
hermes-history-ingest
Ingest Hermes agent history into the Obsidian wiki. Use this skill when the user wants to mine their past Hermes sessions for knowledge, import their /.hermes folder, extract insights from previous Hermes conversations, or says things like "process my Hermes history", "add my Hermes memories to the wiki", "ingest…
wiki-context-pack
Produce a token-bounded, citation-ready context slice from an existing Obsidian vault for a downstream agent or task. Use for "/wiki-context-pack", "use my vault as context", "context slice for X", "pack the wiki for my agent", or "bounded context for Y".
clio-webhooks
Receive and verify Clio (Clio Manage) webhooks. Use when setting up Clio webhook handlers, debugging X-Hook-Signature verification, completing the X-Hook-Secret handshake, or handling legal practice events like matter.created, contact.updated, activity.created, or bill events.
microsoft-graph-webhooks
Receive and verify Microsoft Graph change notifications (webhooks). Use when setting up a Microsoft Graph webhook / subscription handler, completing the validationToken endpoint validation handshake, validating clientState, decrypting rich notifications (includeResourceData), handling lifecycle events…