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 agentlas-ai/agentlas-desktop --skill relsa-severity-assessmentgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/relsa-severity-assessment)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/relsa-severity-assessment"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/relsa-severity-assessment"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.05021 |
| Opus 5.5 | $0.00073 | $0.02008 |
| Sonnet 5.5 | $0.00036 | $0.01004 |
| Haiku 4.5 | $0.00018 | $0.00502 |
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 19d 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 — 191 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 — 372 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
7 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.
- 21d ago Changed · -44 lines 0580bbba8e54
- 1mo ago First seen · 372 lines · 182 tokens per session scan A 08c2abddda92
relsa-severity-assessment is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 182 tokens to every session and 5,021 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 191 lines, and is treated as a copy.
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