{ "ok": true, "resource": { "id": "RES_F4BC27D3DF87", "resource_type": "skill", "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/relsa-severity-assessment", "name": "relsa-severity-assessment", "summary_en": "Multivariate severity assessment and humane endpoint prediction for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint or when one will be reached, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality (\"turned\" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.", "summary_zh": "使用RELSA（相对严重程度评估）评分和基于ARIMA的foRcast预测，对实验动物研究进行多变量严重程度评估和人道终点预测。当需要将福利读数——体重或体重减轻、体温、临床或筑巢评分、生物标志物、活动、心率、挖洞、跑轮——合并为每只动物每天的单一严重程度评分时，当询问哪些动物有达到人道终点的风险或何时会达到时，当通过核密度估计在严重程度量表上定义关注/危险区域或阈值时，或在3Rs、细化、动物福利或欧盟指令2010/63/EU严重程度评估背景下报告严重程度时使用。涵盖方向性（\"翻转\"变量）、基线归一化、参考集、RELSA权重、ARIMA预测区间以及RMSE/PICP/MPIW评估。", "description_en": "This Agent Skill implements two published procedures for severity assessment in laboratory animal studies: RELSA (Talbot et al., 2022) combines multiple welfare readouts into a single per-animal severity score relative to a reference set, and foRcast (Lutscher et al., 2026) fits ARIMA models to individual RELSA trajectories to forecast humane endpoints with prediction intervals. The skill provides scripts for computing RELSA scores, forecasting, and defining severity zones via kernel density estimation. It requires Python >=3.10 with numpy, pandas, scipy, statsmodels, and matplotlib. The skill is distributed under the MIT license and is part of the Scientific Agent Skills library by K-Dense. 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