Each scienceclaw-benchmark-forNN skill describes one scientific task family: its inputs, the deliverable, how
quality is judged, and the scilib tools and pretrained weights that fit it. The 23 skills correspond to the 23
disciplines of the companion ScienceClaw-Eval benchmark; its evaluation data is hosted on Hugging Face
(https://huggingface.co/datasets/beita6969/scienceclaw-eval).
How to use
- Match the user's task to one row below and load that discipline skill.
- Confirm the tools it names with
scienceclaw_tools(search,show,status,weights) and do not plan around a tool or weight that is reported as unavailable on this host. - Build the workflow on the canvas (see
scienceclaw-canvas): read the inputs through the task'sload_<name>tools, keep the deliverable's type, shape and unit, and replay before finishing.
Disciplines
| Code | Discipline (ANZSRC division) | Task | Quality metric | Skill |
|---|---|---|---|---|
| FoR30 | Agricultural, veterinary and food sciences | PhenoBench hierarchical panoptic segmentation | PQ+ (higher is better) | scienceclaw-benchmark-for30 |
| FoR31 | Biological sciences | ProteinGym substitutions | mean Spearman (higher is better) | scienceclaw-benchmark-for31 |
| FoR32 | Biomedical and clinical sciences | MSD Task04 Hippocampus | DSC (higher is better) | scienceclaw-benchmark-for32 |
| FoR33 | Built environment and design | BuildingsBench | balanced NRMSE (%) (lower is better) | scienceclaw-benchmark-for33 |
| FoR34 | Chemical sciences | OGB ogbg-molhiv | ROC-AUC (higher is better) | scienceclaw-benchmark-for34 |
| FoR35 | Commerce, management, tourism and services | Monash Tourism Monthly | mean MASE (lower is better) | scienceclaw-benchmark-for35 |
| FoR36 | Creative arts and writing | MUSDB18 four-stem separation | mean target-median SDR (dB) (higher is better) | scienceclaw-benchmark-for36 |
| FoR37 | Earth sciences | WeatherBench2 | 2m temperature RMSE (K) (lower is better) | scienceclaw-benchmark-for37 |
| FoR38 | Economics | World Bank macro forecasting | mean sMAPE (%) (lower is better) | scienceclaw-benchmark-for38 |
| FoR39 | Education | Eedi NeurIPS 2020 Task 4 | organizer 10-mask accuracy (higher is better) | scienceclaw-benchmark-for39 |
| FoR40 | Engineering | DCASE 2024 Task 2 | official DCASE score (higher is better) | scienceclaw-benchmark-for40 |
| FoR41 | Environmental sciences | NEON aquatics forecasting | mean CRPS (oxygen + temperature) (lower is better) | scienceclaw-benchmark-for41 |
| FoR42 | Health sciences | PhysioNet/CinC 2019 sepsis | normalized clinical utility (higher is better) | scienceclaw-benchmark-for42 |
| FoR43 | History, heritage and archaeology | HIPE-OCRepair 2026 | weighted cMER-micro (lower is better) | scienceclaw-benchmark-for43 |
| FoR44 | Human society | ACIC 2016 | response-SD normalized RMSE (lower is better) | scienceclaw-benchmark-for44 |
| FoR45 | Indigenous studies | AmericasNLP 2026 | mean sentence chrF++ (higher is better) | scienceclaw-benchmark-for45 |
| FoR46 | Information and computing sciences | HumanEval → MBPP | execution pass@1 (higher is better) | scienceclaw-benchmark-for46 |
| FoR47 | Language, communication and culture | CoNLL-2018 UD | LAS (higher is better) | scienceclaw-benchmark-for47 |
| FoR48 | Law and legal studies | ContractNLI | mAP (higher is better) | scienceclaw-benchmark-for48 |
| FoR49 | Mathematical sciences | SMT-COMP 2025 QF_NIA | oracle-agreement accuracy (higher is better) | scienceclaw-benchmark-for49 |
| FoR50 | Philosophy and religious studies | SemEval-2023 Task 4 ValueEval | official F1 (higher is better) | scienceclaw-benchmark-for50 |
| FoR51 | Physical sciences | Matbench phonons | MAE (lower is better) | scienceclaw-benchmark-for51 |
| FoR52 | Psychology | Psych-201 discrete | micro accuracy (higher is better) | scienceclaw-benchmark-for52 |
Rules that apply to every discipline
- Use only the inputs the task declares; never use target information, and respect the temporal order of forecasting and clinical data.
- Prefer a frozen checkpoint whose input/output contract is documented over a model trained inside the workflow, and report which checkpoint (and version) produced a result. Pretrained weights may have seen similar data: say so.
- Keep a simple baseline next to a pretrained route so that the gain, if any, is visible.