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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-redcap-cdiscgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-redcap-cdisc)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-redcap-cdisc"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-redcap-cdisc/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/alterlab-ieu/alterlab-academic-skills/alterlab-redcap-cdisc"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-redcap-cdisc.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 | $0.00230 | $0.02866 |
| Opus 5 | $0.00115 | $0.01433 |
| Sonnet 5 | $0.00046 | $0.00573 |
| Haiku 4.5 | $0.00023 | $0.00287 |
Grade A, and why
alterlab-redcap-cdisc 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 5d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
REDCap & CDISC — Research Data Capture and Standards Alignment
Designs validated electronic data capture (EDC) instruments in REDCap and aligns a study's variables to CDISC submission standards. It owns two jobs that overlap with sibling skills only at the edges:
- REDCap project & instrument design — turn a study's requirements into a REDCap data dictionary (the importable 18-column CSV), with correct field types, validation, branching logic, longitudinal events, and survey settings.
- CDISC alignment — map collected variables to CDASH (collection) and SDTM (tabulation) domains, and tie coded values to CDISC Controlled Terminology (NCI-EVS), so the data is submission- and reuse-ready.
This is design + standards work, not a live-API connector. Producing the CSV and the mapping is in scope; importing it into a running REDCap server is the instance owner's job (REDCap's own API/UI).
Quick Start
Build me a REDCap data dictionary for a 3-arm RCT screening + follow-up.
Add branching logic so the pregnancy question only shows when sex = female.
Lint this data_dictionary.csv before I import it.
Map my CRF variables to CDISC SDTM domains and CDASH fields.
Which SDTM domain and controlled-terminology codelist does "adverse event" go in?
→ Draft or read the dictionary, run scripts/lint_data_dictionary.py to catch
structural errors, then (for CDISC) produce a variable → CDASH → SDTM mapping
table. Always state which facts are design conventions vs. instance-specific.
When to Use This Skill
Use it when the request is about building the instrument or making it standards-compliant:
- "Build / design a REDCap project, form, or data dictionary."
- "Write the importable REDCap CSV for these variables."
- "Set up branching logic / show-field logic / field validation / required fields."
- "Configure longitudinal events / arms / repeating instruments / a survey."
- "Lint / validate / debug my data dictionary before import."
- "Map my study to CDISC — which SDTM domain / CDASH field / codelist?"
- "Make my CRF submission-ready (SDTM/CDASH/CDISC CT)."
What ships with it
5 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.
- 5d ago First seen · 209 lines · 230 tokens per session scan A a4d0630ae97c
alterlab-redcap-cdisc is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 5d ago), licensed MIT. It adds 230 tokens to every session and 2,866 once invoked, about $0.0011 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 2026-09-05.
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