autoresearch — Claude Code skill

Autonomously research an ML task and run many bounded experiments to find the best config — a fixed-budget edit → train → eval → keep-or-discard → iterate loop in the spirit of karpathy/autoresearch, wrapped in the orchestrator conventions of ml-intern and fanned out with a Claude Code dynamic workflow.

It runs long. Instead of one fixed sweep, it runs an iterative generational loop modelled on mims-harvard/AutoScientists: each generation, parallel agent teams read a shared findings board and propose new hypotheses, a peer-critic panel prunes them before any GPU is spent, survivors train + verify, and the board + champion are updated so the search compounds. The loop keeps going for hours or days until it hits its budget, stalls (stagnation), or converges.

What it does

1. Deep-researches existing solutions first — runs a fan-out internet survey (the deep-research workflow/skill when available, else manual multi-angle WebSearch/WebFetch) plus PapersWithCode (scripts/pwc_search.sh), arXiv and HF Papers. Cross-checks sources into a cited DEEPRESEARCH.md covering SOTA methods, benchmark + metric, reference code, and the tricks that already moved the metric — which become experiment hypotheses.
2. Asks where to get the "cards" (GPUs) and the data — confirms the compute provider (Kaggle notebooks / Local GPU / Cloud SSH) and dataset source before spending any compute.
3. Plans an experiment matrix — writes an editable program.md (you program this, not the Python) and PLAN.md of one-variable-at-a-time hypotheses.
4. Runs a long generational loop as a background workflow — parallel teams propose, a peer-critic panel prunes before compute, survivors train for a fixed time budget and eval the metric, kept winners are adversarially re-verified, and the shared board + champion update each generation. It loops until max_generations, stagnation, or the budget runs out. (For a one-shot sweep, set max_generations=1.)
5. Reports the best config — a leaderboard + shared FINDINGS.md board + RESULTS.md, optionally published to the HF Hub.

If no compute is reachable it falls back to design-only mode: it emits the matrix + a runnable harness for you to run yourself.

Install

This skill lives at ~/.claude/skills/autoresearch/. It reuses ml-intern's scripts for notifications and HF publishing, so install ml-intern too (optional — without it, alerts/publishing are skipped, the research + experiment loop still runs).

Use

/autoresearch beat the val_bpb baseline on enwik8 with a small GPT, 12 experiments x 5min
/autoresearch what improves accuracy on CIFAR-10 with a ResNet-18, kaggle GPU
/autoresearch ablate optimizer choices for a char-RNN on tiny-shakespeare, design-only
/autoresearch run overnight on enwik8 — keep proposing + critiquing until it stops improving

Run layout (~/autoresearch-runs/<slug>/)

Files

• SKILL.md — the behavioral spec (the skill itself).
• scripts/pwc_search.sh — PapersWithCode search with graceful web-search fallback.
• scripts/gpu_probe.sh — local CUDA / VRAM probe for compute auto-detect.
• assets/research_loop.template.js — the long-running generational loop (propose → critique → experiment → verify → share), the default fan-out.
• assets/experiment_workflow.template.js — single-pass fan-out template (one round, no loop).
• assets/board.template.md — the shared FINDINGS.md board skeleton.
• assets/program.template.md — the editable per-run spec.
• assets/research_card.template.md — the RESEARCH.md skeleton.