How do I install the Fleet Model Optimizer skill?

Run npx skillmds@latest add chf3198/fleet-model-optimizer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Fleet Model Optimizer safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Fleet Model Optimizer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Fleet Model Optimizer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Fleet Model Optimizer?

chf3198 (@chf3198) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Fleet Model Optimizer by chf3198 — Agent Skill

Name: Fleet Model Optimizer
Rating: 4.2 (5 reviews)
Author: chf3198

File contents

Fleet Model Optimizer Skill

Use this skill to audit and improve Ollama model selections across a device fleet. Invoke when models feel stale, performance is low, or after a major LLM release cycle.

Scope Boundary

Owns: hardware-to-tier mapping, model scoring, pull/delete command generation. Hands off to: Admin for actual ollama pull/delete execution on remote devices.

Hard Constraints

Never reference specific device IPs, hostnames, or user credentials.
Never recommend proprietary closed-weight models — Ollama registry only.
Always produce a pull-before-delete transition plan (no service gaps).
Respect RAM budget: leave ≥20% free after all models loaded.
Verify Ollama registry availability before recommending any model.

Tier Classification

Classify each device from merged fleet inventory (node scripts/global/fleet-config.js fleet) by available RAM:

Available RAM	Tier	Max model size	Examples
< 1.5 GB	slm	~500 MB	gemma3:270m, qwen3.5:0.5b
1.5–4 GB	slm+	~1.5 GB	gemma4:e4b, qwen3.5:1.5b
4–10 GB	mid	7–8 B	qwen3:8b, mistral-nemo:12b*
10–20 GB	mid+	8–14 B	qwen3:8b, phi4:14b
20+ GB	high	30–70 B	deepseek-r1:32b, qwen3:30b

*Only if swap or GPU offload is available.

Step-by-Step Execution

1. Inventory Read

Run node scripts/global/fleet-config.js fleet (or read ~/.megingjord/devices.json overlay). For each device extract: id, ram.available, gpu (if present), ollamaModels, ollamaWarmTokPerSec.

2. Tier Classification

Apply the table above to assign each Ollama-enabled device a tier. Note any GPU presence — it enables larger models and faster inference.

3. Model Scoring (per tier)

Search Ollama registry trends (pull counts, recency) for top candidates:

Prioritize: reasoning quality, coding capability, pull count > 1M (community validation).
Include 1 primary + 1–2 fallback models per device.
Flag models with < 100K pulls as experimental.

4. Delta Analysis

Compare recommended models to current ollamaModels list. Identify: models to keep, models to add (pull), models to remove (delete).

5. Command Generation

Output per-device commands in this format:

# <device-id> (<tier>)
ollama pull <new-model>
ollama rm <old-model>   # only after pull confirmed

6. Transition Plan

State the safe sequence: pull new → verify via GET /api/tags → delete old. Confirm at least one fallback model remains at all times.

7. Inventory Update Guidance

Provide the updated ollamaModels array and estimated ollamaWarmTokPerSec (use tier baselines: slm≈5, mid≈15, mid+≈20, high≈25+) for the operator to apply to ~/.megingjord/devices.json overlay (never commit operator topology to git).

Verification

Every recommended model confirmed present in Ollama registry.
No device left with zero models after transition.
RAM budget respected: model size ≤ 80% of ram.available.
Pull/delete commands syntactically valid (ollama pull <name> format).
~/.megingjord/devices.json overlay update values provided with source rationale.

---name: fleet-model-optimizerdescription: Analyze fleet overlay inventory and recommend optimal Ollama models per device based on hardware constraints, inference tier, and current LLM landscape. Generates pull/delete commands and a safe transition plan.--- # Fleet Model Optimizer Skill Use this skill to audit and improve Ollama model selections across a device fleet. Invoke when models feel stale, performance is low, or after a major LLM release cycle. ## Scope Boundary **Owns**: hardware-to-tier mapping, model scoring, pull/delete command generation. **Hands off to**: Admin for actual `ollama pull/delete` execution on remote devices. ## Hard Constraints - Never reference specific device IPs, hostnames, or user credentials. - Never recommend proprietary closed-weight models — Ollama registry only. - Always produce a pull-before-delete transition plan (no service gaps). - Respect RAM budget: leave ≥20% free after all models loaded. - Verify Ollama registry availability before recommending any model. ## Tier Classification Classify each device from merged fleet inventory (`node scripts/global/fleet-config.js fleet`) by available RAM: | Available RAM | Tier | Max model size | Examples | |---|---|---|---| | < 1.5 GB | slm | ~500 MB | gemma3:270m, qwen3.5:0.5b | | 1.5–4 GB | slm+ | ~1.5 GB | gemma4:e4b, qwen3.5:1.5b | | 4–10 GB | mid | 7–8 B | qwen3:8b, mistral-nemo:12b* | | 10–20 GB | mid+ | 8–14 B | qwen3:8b, phi4:14b | | 20+ GB | high | 30–70 B | deepseek-r1:32b, qwen3:30b | *Only if swap or GPU offload is available. ## Step-by-Step Execution ### 1. Inventory Read Run `node scripts/global/fleet-config.js fleet` (or read `~/.megingjord/devices.json` overlay). For each device extract: `id`, `ram.available`, `gpu` (if present), `ollamaModels`, `ollamaWarmTokPerSec`. ### 2. Tier Classification Apply the table above to assign each Ollama-enabled device a tier. Note any GPU presence — it enables larger models and faster inference. ### 3. Model Scoring (per tier) Search Ollama registry trends (pull counts, recency) for top candidates: - Prioritize: reasoning quality, coding capability, pull count > 1M (community validation). - Include 1 primary + 1–2 fallback models per device. - Flag models with < 100K pulls as experimental. ### 4. Delta Analysis Compare recommended models to current `ollamaModels` list. Identify: models to keep, models to add (pull), models to remove (delete). ### 5. Command Generation Output per-device commands in this format: ``` # <device-id> (<tier>) ollama pull <new-model> ollama rm <old-model> # only after pull confirmed ``` ### 6. Transition Plan State the safe sequence: pull new → verify via `GET /api/tags` → delete old. Confirm at least one fallback model remains at all times. ### 7. Inventory Update Guidance Provide the updated `ollamaModels` array and estimated `ollamaWarmTokPerSec` (use tier baselines: slm≈5, mid≈15, mid+≈20, high≈25+) for the operator to apply to `~/.megingjord/devices.json` overlay (never commit operator topology to git). ## Verification - [ ] Every recommended model confirmed present in Ollama registry. - [ ] No device left with zero models after transition. - [ ] RAM budget respected: model size ≤ 80% of `ram.available`. - [ ] Pull/delete commands syntactically valid (`ollama pull <name>` format). - [ ] `~/.megingjord/devices.json` overlay update values provided with source rationale.

Frequently asked questions

Fleet Model Optimizer