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Use when the user wants the best currently available LLM output from free-tier models. Installs FreeLLMAPI (a self-hosted multi-provider OpenAI-compatible router), registers it as a Hermes custom provider, discovers the current highest-ranked free models from the live FreeLLMAPI catalog, and configures a Hermes Mixture-of-Agents (MoA) preset using those models. The preset routes diverse reference models through the best available aggregator for SOTA-seeking output over pooled free-tier quota. Re
Passed 17 of 22 test cases with the skill, and 5 of 22 without it.
and 15 more in the full report
13 fixed4 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
What are the minimum runtime version requirements for Node.js, npm, and Python when setting up the FreeLLMAPI repository locally?
| Checked | Without skill | With skill |
|---|---|---|
| Requires Node.js version 20.x or 22.x LTS | Missed | Passed |
| Requires Python 3.10 or higher | Passed | Passed |
The instructions your model reads when the skill loads.
This skill installs and configures the FreeLLMAPI + Hermes MoA stack to produce the best currently available LLM output from free-tier pooled quota. It is designed to be repeatable at any future date: it queries the live model catalog rather than hardcoding model names, so the selected models are always the best currently available.
Scope caveat: This stack operates over pooled free-tier provider quotas. Output quality is bounded by what is available and non-exhausted in your catalog at any given moment. "SOTA-seeking" means: best available diverse panel from your current free-tier pool, not guaranteed frontier-model performance. Provider limits, ToS, and model availability all apply.
[User / Hermes session]
│
▼
Hermes Agent ←──── MoA preset "free-sota"
│ │
│ ┌─────┴──────┐
│ Reference Reference
│ model A model B
│ (no tools, (no tools,
│ advisory) advisory)
│ └─────┬──────┘
│ ▼
└──────────── Aggregator model
(full tool schema,
writes response,
emits tool calls)
test cases passed, vs 5 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.