executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
optimateRoutes OptiMate workflows for Speedster inference optimization, NebullVM backend selection, Forward-Forward training, OpenAlphaTensor training, and ChatLLaMA RLHF setup.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill for the repository's public workflows around model optimization, backend selection, standalone training engines, and ChatLLaMA RLHF setup.
| If the user asks about... | Start here |
|---|---|
speedster, optimize_model, save_model, load_model, compiler/backend filtering, latency summaries, or model acceleration | sub-skills/speedster-optimization/SKILL.md |
nebullvm, DataManager, check_device, compiler selection, optional backend probing, or device parsing | sub-skills/nebullvm-backends/SKILL.md |
| Forward-Forward training, MNIST or Aesop Fables loaders, progressive/recurrent/NLP model types, or Python 3.9 compatibility | sub-skills/forward-forward-training/SKILL.md |
OpenAlphaTensor configs, checkpoints, matrix-multiplication search, or the main.py training CLI | sub-skills/open-alpha-tensor/SKILL.md |
| ChatLLaMA datasets, config YAMLs, actor/reward/RLHF training, DeepSpeed, or PEFT setup | sub-skills/chatllama-rlhf/SKILL.md |
references/repo-provenance.mdreferences/package-map.mdreferences/installation-and-backends.mdreferences/troubleshooting.mdreferences/repo-routing-metadata.jsonUse the bundled helper to verify that the public package roots import and the host backend is visible:
python scripts/check_optimate_environment.py --modules speedster,nebullvm --check-cuda
For Forward-Forward and ChatLLaMA compatibility questions, prefer the sub-skill scripts because they encode the source-era Python and dependency constraints.