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/atlasomnia/hermes-custom-pack/local-model-selectionnpx skills add AtlasOmnia/hermes-custom-pack --skill local-model-selectiongit clone --depth 1 https://github.com/AtlasOmnia/hermes-custom-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/atlasomnia/hermes-custom-pack/local-model-selection)<a href="https://agentmods.dev/skills/atlasomnia/hermes-custom-pack/local-model-selection"><img src="https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/local-model-selection.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 | $0.00050 | $0.11627 |
| Opus 5 | $0.00025 | $0.05813 |
| Sonnet 5 | $0.00010 | $0.02325 |
| Haiku 4.5 | $0.00005 | $0.01163 |
Grade C, and why
local-model-selection scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
- Fetch it via curl: `curl -sL "https://huggingface.co/<org>/<model>/resolve/main/model.safetensors.index.json" | python3 -c "import json, sys; d=json.load(sys.stdin); print(d.get('metadata',{}).get('total_size', 'not fo Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Fetch it via curl: `curl -sL "https://huggingface.co/<org>/<model>/resolve/main/model.safetensors.index.json" | python3 -c "import json, sys; d=json.load(sys.stdin); print(d.get('metadata',{}).get('total_size', 'not fo This is a copy
100% identical to local-model-selection — 0 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 — 568 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local Model Selection for Hermes
Use this skill when the user asks which local model to run, which quant to pick, which uncensored variant to use, how to pair models across GPUs, or what auxiliary model to use alongside their main model.
When to use
Trigger on requests like:
- "what's the best Qwen model for my hardware"
- "which quant should I use for 32GB VRAM"
- "what uncensored model for 16GB"
- "what should I run as a secondary/auxiliary model"
- "compare these three models for agent use"
- "is this model still the best or has something newer dropped"
- "where are my LM Studio models"
- "move/delete these LM Studio models to free space"
- "are there any Ollama models on this Mac"
- "uninstall Ollama"
For storage operations, distinguish the machine being cleaned from any remote model host. For LM Studio, inventory exact model directories, check lms ps before deletion, delete only explicitly named model repositories, and verify both path removal and real free-space change; see references/lm-studio-model-storage-operations.md. For Ollama on macOS, cross-check ollama list, allocated model blobs/manifests, and large files before claiming models exist, then use the process/app/CLI/package/data verification sequence in references/macos-ollama-storage-and-uninstall.md for a complete uninstall.
Core rule: verify model existence before recommending
Never fabricate model names. Model families have specific size tiers — not every number exists in every family. Before recommending, verify the model exists on HuggingFace or Ollama.
When a user asks for recommendations:
- Use exact model identifiers, e.g. "Qwen3.6-27B" not just "a Qwen 3 model."
- Never say "we can run about X parameters" without naming at least one concrete, released, GGUF-available candidate that actually fits.
- If you're unsure of the latest family/size, check via web_search or HuggingFace before answering — the user will spot vague or outdated claims immediately.
If you mix up versions (e.g., saying "Qwen 3.5" when Qwen 3.6 is current), correct yourself directly instead of hedging.
What ships with it
19 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.
- references/16gb-vram-recommendations.md 3.2 KB
- references/cloud-orchestrated-q4-delegates.md 4.5 KB
- references/dense-vs-moe-qwen36-comparison.md 4.8 KB
- references/hermes-compression-window-matching.md 6.6 KB
- references/hermes-local-routing-verification.md 4.4 KB
- references/jinja-template-patches.md 3.4 KB
- references/lm-studio-engine-protocol-threading.md 3.6 KB
- references/lm-studio-model-storage-operations.md 2.3 KB
- references/local-compression-economics.md 3.0 KB
- references/local-document-ocr-models.md 2.3 KB
- references/mac-megathread-research.md 3.4 KB
- references/mac-model-research.md 5.1 KB
- references/macos-ollama-storage-and-uninstall.md 2.7 KB
- references/prefill-optimization-notes.md 4.2 KB
- references/qwen-agentworld-field-notes.md 2.8 KB
- references/qwen-family-size-map.md 1.5 KB
- references/reap-pruned-models.md 3.5 KB
- references/shared-local-worker-compressor.md 5.3 KB
- references/single-gpu-long-context-compression.md 8.7 KB
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 · 568 lines · 50 tokens per session scan C c9be07496433
local-model-selection is a skill published in the GitHub repository AtlasOmnia/hermes-custom-pack (56 stars, last pushed 22d ago), licensed MIT. It adds 50 tokens to every session and 11,627 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). It is 100% identical to local-model-selection, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
local-model-selection
Use this skill when the user asks which local model to run, which quant to pick, which uncensored variant to use, how to pair models across GPUs, or what auxiliary model to use alongside their main model.
ollama
Use when running open-weight LLMs locally with Ollama — pulling and tagging models, calling the local API, picking a quantization or GGUF, writing Modelfiles, and sizing VRAM and RAM for the machine at hand. NOT remote or managed GPU serving and autoscaling (that is runpod), NOT downloading raw weights or datasets…
ai-portable-setup
Erstellt einen portablen KI-Arbeitsbereich auf einem USB-Stick oder beliebigen Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurierten Prompts.
ai-sdk
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI…
task_automation
Design effective automated workflows using scheduled tasks, prompt chaining, and delivery channels.
claude_code_delegation
Delegate coding, review, and refactor tasks to Claude Code CLI through Row-Bot's approval-gated shell workflow.