2 skills published by AlexWortega across 2 repositories. Together they weigh 37 713 tokens — that is what loading all of them at once would cost you in context.
2 skills 37 713 tokens total
Autonomously research, implement, train and ship ML code using the Hugging Face ecosystem. Port of huggingface/ml-intern as a Claude Code skill. Triggers when the user asks to implement, train, fine-tune, or reproduce an ML model / paper / dataset workflow (e.g. "implement DeepSeek-V3 at 100M", "fine-tune Qwen on dataset X", "reproduce paper Y"). Clarifies ambiguous tasks before starting, runs under an explicit experiment budget, explores multiple viable solution paths in parallel via implementation subagents, and diagnoses + retries failed runs. HF-native: pulls datasets/models/papers from the Hub, pushes trained checkpoints + run logs back to the Hub. Emits Telegram + Slack milestone alerts via scripts/notify.sh.
Autonomously research an ML task and run MANY bounded experiments to find the best config — a fixed-budget edit→train→eval→keep-or-discard loop in the spirit of karpathy/autoresearch, wrapped in the ml-intern orchestrator model and fanned out with a Claude Code dynamic workflow. Runs LONG: an iterative generational loop (mims-harvard/AutoScientists style) where parallel agent teams propose hypotheses, peer-critique them before spending any GPU, share findings on a common board, promote a champion, and keep going until budget/stagnation/convergence. Triggers when the user wants to "run many experiments", "sweep / search for the best config", "beat a benchmark", "do an ablation", "autoresearch X", "run for a long time / overnight / for days", or "find what improves metric Y on dataset Z". Deep-researches existing solutions across the internet FIRST (fan-out web search + PapersWithCode + GitHub, sources cross-checked into a cited DEEPRESEARCH.md), then ASKS where to get GPUs ("cards") and data before spending any compute, generates an experiment matrix seeded from diverse literature angles, runs it as a background workflow under an explicit budget, keeps a running leaderboard + shared findings board, verifies winners, and reports the best config. Reuses ml-intern's notify.sh + hf_push.sh for milestone alerts and HF Hub publishing.