literature-to-recipe

name	literature-to-recipe
description	Turn papers and docs into a concrete training or evaluation recipe (datasets, metrics, hyperparameters, fine-tuning code).
always	true

Literature to recipe

Goal: extract actionable training/eval recipes from sources, not summaries of abstracts.

Workflow

1. Anchor — identify 1–3 anchor papers or official docs for the task (use arxiv_search, arxiv_fetch, web_search, web_fetch, hf_hub_file_fetch for Hub configs).
2. Methodology — read sections on data, preprocessing, model, optimization, and evaluation metrics (not the abstract alone).
3. Extract — write down: dataset name/version, splits, preprocessing, loss, batching assumptions, LR schedule, epochs/steps, hardware used, and reported metric with the exact benchmark definition.
4. Map to workspace — translate into files or execution_* steps that fit this sandbox (paths, env vars, pinned package versions).
5. Validate data — inspect real files in the repo (read_file, glob_files, search_text); never assume column names.
6. Traceability — cite arXiv IDs or URLs for every non-obvious choice.

Output format for the user:

• Sources (bullets with links or ids)
• Recipe (numbered steps)
• Risks / unknowns (what was not stated clearly in the paper)