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 DrugClaw/DrugClaw --skill clinical-research-toolsgit clone --depth 1 https://github.com/DrugClaw/DrugClawWrote 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/drugclaw/drugclaw/clinical-research-tools)<a href="https://agentmods.dev/skills/drugclaw/drugclaw/clinical-research-tools"><img src="https://agentmods.dev/badge/skills/drugclaw/drugclaw/clinical-research-tools/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/drugclaw/drugclaw/clinical-research-tools"><img src="https://agentmods.dev/badge/skills/drugclaw/drugclaw/clinical-research-tools.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 | $0.00074 | $0.00945 |
| Opus 5 | $0.00037 | $0.00473 |
| Sonnet 5 | $0.00015 | $0.00189 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
clinical-research-tools 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 13d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Research Tools
Use this skill for group-level human research work, not bedside care.
Typical triggers:
- choose between RCT, cohort, case-control, cross-sectional, diagnostic, or single-arm designs
- define primary and secondary endpoints, estimands, eligibility criteria, or subgroup analyses
- select the right reporting guideline such as CONSORT, STROBE, PRISMA, STARD, TRIPOD, SPIRIT, or CARE
- draft protocol, SAP, CSR, or evidence-summary outlines
- review bias, confounding, missing data, and sample-size assumptions
- prepare trial or real-world-evidence summaries for drug-discovery programs
Working Rules
- Keep the task at the study or cohort level.
- Separate confirmed study facts from proposed design choices.
- State assumptions behind endpoint, power, and statistical-model choices.
- Call out data leakage, immortal-time bias, selection bias, and confounding whenever relevant.
- Do not present DrugClaw as giving medical advice, treatment recommendations, or diagnostic decisions.
Study Design Map
Use this quick routing:
RCT: intervention efficacy, causal inference, registration-ready protocolsProspective cohort: prognosis, exposure-outcome tracking, real-world evidenceRetrospective cohort: registry or EHR analyses with explicit confounding controlCase-control: rare outcomes or exploratory risk-factor workCross-sectional: prevalence, survey snapshots, baseline association workDiagnostic accuracy: sensitivity, specificity, ROC, calibration, decision curvesPrediction model: risk scores, survival models, treatment-response models with external validation plans
Reporting Guideline Map
Choose and state the governing framework early:
CONSORT: randomized trialsSPIRIT: trial protocolsSTROBE: observational studiesPRISMA: systematic reviews and meta-analysisSTARD: diagnostic accuracy studiesTRIPOD: prediction modelsCARE: case reportsICH E3: clinical study reports
Protocol Workflow
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.
- 13d ago First seen · 98 lines · 74 tokens per session scan A 7029fa57e841
clinical-research-tools is a skill published in the GitHub repository DrugClaw/DrugClaw (125 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 945 once invoked, about $0.0004 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.
Other skills, from other repositories
drug-repurposing
Systematic drug repurposing via signature matching, target-based analysis, network proximity, genetic evidence scoring, and clinical evidence mining.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
genomic-variants
Analysis of called genomic variants — filtering, annotation, GWAS, and population-genetics summaries from VCF and PLINK-format data.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.