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 Aperivue/medsci-skills --skill define-variablesgit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote 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/aperivue/medsci-skills/define-variables)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/define-variables"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/define-variables/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/aperivue/medsci-skills/define-variables"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/define-variables.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.02666 |
| Opus 5 | $0.00034 | $0.01333 |
| Sonnet 5 | $0.00014 | $0.00533 |
| Haiku 4.5 | $0.00007 | $0.00267 |
Grade A, and why
define-variables 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Define-Variables Skill
Purpose
Every observational study operationalizes abstract constructs (MASLD, CKD, emphysema, obesity, incidentaloma) into concrete rules against the available data dictionary. When that operationalization is invented ad-hoc from the dictionary alone, reviewers reject on construct validity regardless of downstream statistics.
This skill forces a literature-first pass: each variable is mapped to a canonical guideline/consensus definition, cross-checked against prior operationalizations in comparable cohorts, then mapped to available DB variables. Ad-hoc deviations are flagged explicitly and justified, not hidden.
Use it when:
- a study question is known and variables are being selected
- inclusion/exclusion criteria or phenotype definitions need citation backing
- a data dictionary has ambiguous or derived variables (eGFR formula, BMI class, liver steatosis criteria, etc.)
- a reviewer asked "why this cutoff?"
- a retrospective audit reveals drifted definitions across projects in the same cohort
Call after /design-study, before /write-protocol.
Communication Rules
- Communicate in the user's preferred language.
- All variable names, guideline names, cutoffs in English.
- Produce one artifact:
variable_operationalization.mdin the project root (or path the user specifies).
Inputs
- Research question (one sentence)
- Candidate variables — exposure, outcome, key covariates, eligibility filters
- Data dictionary path (xlsx / csv / markdown) OR explicit list of available DB columns
- Cohort type (e.g., health-screening, NHANES-like, claims, registry) — informs which prior-art cohort to compare against
Missing inputs → ask once, then proceed.
4-Tier Pipeline (DB codebook + token-efficient literature)
Tier 0 — DB codebook lookup (mandatory for DB-backed observational studies)
Trigger: project has a project.yaml::db.dictionary_path field pointing to a machine-readable codebook (xlsx/csv/markdown), OR user supplied a dictionary path in inputs. If neither, skip to Tier 1.
What ships with it
3 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.
- 9d ago First seen · 155 lines · 69 tokens per session scan A bcbc6775dbb7
define-variables is a skill published in the GitHub repository Aperivue/medsci-skills (290 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 2,666 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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