Claude Skill MartBrowse skillsWhat is a Skill?
Back to list

RELSA Severity Assessment & Humane Endpoint Forecasting

Combines body weight, temperature, clinical scores and biomarkers into a single RELSA severity score, then forecasts humane endpoints with ARIMA.

Data & AnalyticsAdvanced33,0303,248AI score 8/10Last updated: Aug 9, 2026

What it does

  • RELSA scoring: merges multiple welfare readouts (weight loss, temperature, clinical scores, inflammatory biomarkers, activity, heart rate, burrowing) into one relative severity score per animal per time point (0 = baseline, 1 = reference set maximum).
  • Humane endpoint forecasting (foRcast): fits an ARIMA model to an individual animal's RELSA trajectory, predicts the next score with a 95% prediction interval, and evaluates it with RMSE, PICP and MPIW.
  • Severity zones: uses kernel density estimation to locate density minima as candidate attention/danger thresholds, with a required bandwidth sensitivity sweep.
  • Reporting discipline: documents the four decisions that drive results (directionality, baseline normalization, reference set, zero-baseline score mapping), nine common pitfalls, explicit regulatory boundaries and a reproducibility checklist.

Who it's for

  • Researchers, veterinarians and animal facility staff performing severity assessment.
  • Anyone writing the severity section of a 3Rs/refinement analysis or an EU Directive 2010/63/EU application.
  • Teams wanting to compare multivariate welfare data (telemetry, clinical scoring) on one common scale.

Examples

  1. Score a cohort: run relsa_score.py on assets/example_cohort.csv (6 mice, 9 days) with weight, temperature, a 0–8 clinical score and IL-6, select the endpoint group as reference, save it to JSON and export per-day RELSA plus per-variable weights.
  2. Flag at-risk animals early: train up to the time point before euthanasia and predict the endpoint score (e.g. 0.93 [0.67–1.19] vs actual 1.00); for live monitoring use --mode rolling one-step-ahead forecasts.
  3. Define zones: run kde_thresholds.py to find a threshold at 0.703 and report it together with a bandwidth sweep, since a 10% larger bandwidth can remove the minima entirely.

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/relsa-severity-assessment folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/relsa-severity-assessment/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/relsa-severity-assessment ~/.claude/skills/

This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and change into your working directory.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Copy the skill into Claude's skills folder: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/relsa-severity-assessment ~/.claude/skills/
  4. Install the Python dependencies (Python >= 3.10): uv pip install "numpy>=1.26" "pandas>=2.0" "scipy>=1.11" "statsmodels>=0.14" matplotlib
  5. Restart Claude Code and ask something like "compute RELSA severity scores for my mouse cohort" to trigger the skill.
  6. Run the documented example commands against assets/example_cohort.csv first to verify your setup, then switch to your own CSV.