IoT · Global library

TinyML Development for Microcontrollers

Deploy ML models on resource-constrained microcontrollers for on-device inference

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use TinyML Development for Microcontrollers when you need to deploy ML models on resource-constrained microcontrollers for on-device inference, especially when the work is driven by tinyml and microcontroller.

Trigger signals

tinymlmicrocontrollerarduinoesp32embeddedon-device

Validation hooks

memory-fitinference-speed

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show tinyml-development-for-microcontrollers

Use

orchestrator-mcp skills export tinyml-development-for-microcontrollers --to ./skillforge-packs
# copy the exported pack into your preferred agent environment

Export

cp -R skills/tinyml-development-for-microcontrollers ./your-agent-skills/tinyml-development-for-microcontrollers
# or open skills/tinyml-development-for-microcontrollers/SKILL.md in a markdown-first client

File patterns

*tinyml*.{py,cpp}*micro*.{py,c}*arduino*.{cpp,ino}*esp32*.{cpp,py}

Model preferences

claude-sonnet-4gpt-4oclaude-haiku

Related skills

Adjacent packs to compose next.

Deploy and serve ML models at the edge with auto-scaling, A/B testing, and monitoring

CodexClaude Code

Optimize ML models for edge deployment with quantization, pruning, and hardware acceleration

CodexClaude Code

Train ML models collaboratively across edge devices without centralizing sensitive data

CodexClaude Code

Apache-2.0 licensedUse it, fork it, redistribute it. No strings attached.Safety-first by designSecret redaction, release scanning, and no shell execution. Constraints are public.GitHub nativeEvery pack is a real folder. Fork, edit, PR. No hidden backend or proprietary pipeline.

TinyML Development for Microcontrollers

Copy the exact command path you need.

Adjacent packs to compose next.

Edge AI Model Deployment & Serving

Edge Model Optimization & Quantization

Federated Learning for Edge Devices