esp32-ai
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
It provides a structured, reproducible way to measure and compare LLM quality, with flexible options for language, scale, and weighting.
Design evaluation rubrics for LLM/agent outputs: criteria, scores, failure modes, examples.
中文简介: 你做可复现、可对比的模型输出评测表。 — 详见 README.zh.md.
After this package is on GitHub:
npx skills add / --skill eval-rubric
Local monorepo: point your agent at skills/eval-rubric/ (this folder).
| Option | Values | Default |
|---|---|---|
lang | en · zh · bilingual | en |
scale | 1-5 · 0-2 · pass-fail | 1-5 |
weighting | equal · custom | equal |
Runtime output language follows lang (default en). Skill docs stay EN-primary.
| Use | Don't |
|---|---|
| (see bullets) | (see bullets) |
Use
eval-rubric workflowDon't
See SKILL.md deliverable.
Full workflow, hard rules, and QA: see SKILL.md.
Monorepo sample (if present): ../../output/samples/eval-rubric-example-01.md
MIT — see LICENSE.
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A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.

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