Upload plugins/mlintern/skills/hf-docs/SKILL.md with huggingface_hub

plugins/mlintern/skills/hf-docs/SKILL.md

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+---
+name: hf-docs
+description: "Search and fetch current Hugging Face documentation for Transformers, TRL, PEFT, Datasets, Trackio, and other libraries."
+disable-model-invocation: false
+---
+
+# hf-docs — Hugging Face Documentation
+
+## Purpose
+
+Look up current API usage patterns, trainer configs, and library documentation before writing ML code. Do not rely on memory — HF library APIs change frequently.
+
+## Tools
+
+- `hf_doc_search`: Search HF documentation by product and query.
+- `hf_doc_fetch`: Fetch the full content of a specific doc page by URL.
+
+## Products Available
+
+| Product | Use For |
+|---|---|
+| transformers | Model loading, tokenizers, trainers, inference |
+| trl | SFT, DPO, GRPO, PPO trainers |
+| peft | LoRA, adapters, parameter-efficient fine-tuning |
+| datasets | Dataset loading, streaming, processing |
+| accelerate | Distributed training, mixed precision |
+| hub | Repo operations, auth, upload/download |
+| trackio | Experiment tracking, metrics, dashboards |
+| sentence_transformers | Embedding models, training, similarity |
+| diffusers | Diffusion pipelines, fine-tuning |
+| optimum | Hardware optimization, model export |
+
+## Workflow
+
+1. Use `hf_doc_search` to find relevant pages.
+2. Use `hf_doc_fetch` to read the full content of the most relevant page.
+3. Extract the exact imports, class names, config parameters, and argument names.
+4. Use the current API in your implementation.
+
+## Example
+
+```
+_hf_doc_search(product="trl", query="SFTConfig parameters")
+_hf_doc_fetch(url="https://huggingface.co/docs/trl/sft_trainer")
+```
+
+## Docs-First Rule
+
+Before writing any training, fine-tuning, inference, or evaluation code:
+- Find at least one current working implementation pattern from HF docs or a relevant repo.
+- Verify import paths, trainer class names, and config field names.
+- Check that the example matches your library version constraints.