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name: scienceclaw-benchmark description: "Index of the 23 discipline skills (FoR30-FoR52) that mirror the disciplines of the companion ScienceClaw-Eval benchmark: find which discipline skill fits a scientific task (plant panoptic segmentation, protein fitness prediction, hippocampus segmentation, building load forecasting, molecule property prediction, music source separation, sepsis early warning, French dependency parsing, contract evidence retrieval, ...). Use to pick the right discipline skill and the scilib tools it names." metadata: { "openclaw": { "emoji": "🧪" } }

ScienceClaw discipline skills

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

1. Match the user's task to one row below and load that discipline skill.
2. 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.
3. Build the workflow on the canvas (see scienceclaw-canvas): read the inputs through the task's load_<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.