synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add synthetic-sciences/openscience --skill relsa-severity-assessmentgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/relsa-severity-assessment)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/relsa-severity-assessment"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/relsa-severity-assessment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/relsa-severity-assessment"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/relsa-severity-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00182 | $0.05092 |
| Opus 5.5 | $0.00073 | $0.02037 |
| Sonnet 5.5 | $0.00036 | $0.01018 |
| Haiku 4.5 | $0.00018 | $0.00509 |
Grade A, and why
relsa-severity-assessment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to relsa-severity-assessment — 198 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RELSA severity assessment and humane endpoint forecasting
Overview
Severity assessment in animal research is legally mandatory and scientifically load-bearing: it drives humane endpoint decisions, and poor welfare monitoring degrades reproducibility. The usual practice evaluates each readout in isolation — weight loss here, a clinical score there — which makes it hard to say how badly an individual animal is actually doing.
This skill implements two published procedures that address that:
- RELSA (Talbot et al., 2022) combines several outcome measures into one score per animal per time point, expressed relative to a reference set of known burden. RELSA = 0 is baseline; RELSA = 1 means the animal has reached the reference set's maximum deviation.
- foRcast (Lutscher et al., 2026) fits an ARIMA model to an individual animal's RELSA trajectory and forecasts the next score with a 95% prediction interval, so animals heading for a humane endpoint can be identified before they get there. Kernel density estimation on the RELSA scale supplies candidate attention and danger zones for interpretation.
The point is refinement: give at-risk animals attention earlier, and avoid euthanising animals that would have recovered. Both procedures are aids to severity assessment, not decision rules — see Boundaries.
When to use this skill
- Combining weight loss, temperature, clinical scoring, biomarkers, or telemetry into a single per-animal severity score
- Asking which animals in a cohort are at risk of reaching a humane endpoint, or predicting the severity score at a coming time point
- Comparing severity between treatment groups, interventions, or animal models on a common relative scale
- Defining thresholds or zones on a severity scale from the data
- Writing the severity-assessment section of an animal welfare report, a 3Rs/refinement analysis, or an application under EU Directive 2010/63/EU
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +5 lines 7751edcbcfd5
- 1mo ago First seen · 374 lines · 182 tokens per session scan A f05ec03ac824
relsa-severity-assessment is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 182 tokens to every session and 5,092 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to relsa-severity-assessment, differing in 198 lines, and is treated as a copy.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.