Fleet model optimizer
Skill chf3198/megingjord-harness/skills/fleet-model-optimizer
AI agent governance harness: baton workflow, fleet LLM routing (Ollama/Claude/OpenRouter), and CI gates for Copilot, Claude Code, and Codex.
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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.
SKILL.md
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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.jsonoverlay update values provided with source rationale.