claude

autoresearch

Karpathy-style autonomous LLM research loop adapted for generic ML codebases. Ships two skills — setup and run — plus an `ar` helper CLI that owns launch orchestration, metric extraction, atomic checkpoint commit, and chain-mode transitions so the main Claude session stays context-light across hundreds or thousands of iterations. v0.3.0 drops the wandb/accelerate monoculture: metric backend is pluggable (wandb / tensorboard / log-scan / custom with auto-detection), distributed-framework resolution is auto-detected (accelerate/deepspeed/fsdp/ddp/lightning/none), a new `hydra` entry pattern renders a Hydra-override-style train wrapper, and `--checkpoint-glob` gives priority-0 control over checkpoint discovery for Lightning / plain-torch / custom layouts.

0 on GitHubby jahnv0.3.0MITupdated 6 months ago

Not in a marketplace yet, so it loads for one session with --plugin-dir.

Plugin autoresearch in Dev-Jahn/autoresearch-plugin. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (2)

  • autoresearch:run

    This skill should be used when the user asks to "start the autoresearch loop", "kick off overnight iteration", "begin autonomous experiment runs", "run /autoresearch:run", "run the autoresearch expr <slug>", "continue the autoresearch loop", "resume autoresearch", "chain through follow-up experiment

  • autoresearch:setup

    Scaffolds a new autonomous-research experiment directory (`.autoresearch/{YYMMDD}-{slug}/`) inside a deep-learning project so Claude can run a long train.py-mutation loop without blowing context. This skill should be used when the user asks to "start an autoresearch experiment", "set up autonomous r

View source on GitHub

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