Clinical Research Tools
SkillMediaClinical research workflow guide for protocol design, endpoint selection, evidence grading, reporting-guideline selection, statistical planning, and clinical-trial evidence synthesis. Use when the user asks to design or review human-subjects research, trial analyses, observational studies, study protocols, CSRs, or clinical evidence summaries without requesting patient-specific diagnosis or treatment decisions.
Use Clinical Research Tools in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Clinical Research Tools and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Clinical Research Tools skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by drugclaw/drugclaw in skills/medical/clinical-research-tools/SKILL.md and read by Ahel’s review.
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
For protocol or study-design requests:
- Define population, intervention or exposure, comparator, outcome, and timeframe.
- State inclusion and exclusion criteria.
- Define primary endpoint, key secondary endpoints, and censoring rules.
- Choose analysis populations: ITT, mITT, per-protocol, safety.
- Describe missing-data handling and sensitivity analyses.
- State sample-size assumptions clearly: alpha, power, effect size, event rate, dropout.
- Specify monitoring, ethics, registration, and data-governance requirements.
Statistical Planning Checklist
Always address:
- endpoint type: binary, continuous, count, time-to-event
- stratification variables and subgroup policy
- multiplicity control
- covariate adjustment policy
- temporal leakage and look-ahead bias
- external validation or temporal validation when building prediction models
- calibration, not only discrimination, for predictive work
Evidence Synthesis
For evidence summaries:
- identify study type and evidence level
- note patient population, line of therapy, biomarker context, and comparator
- distinguish efficacy, safety, and external-validity conclusions
- state what remains uncertain
- use cautious language for indirect or observational evidence
Outputs
Good outputs usually include:
- one-page design summary or protocol skeleton
- endpoint table
- statistical analysis outline
- bias and limitation section
- reporting-guideline checklist
Related Skills
For ClinicalTrials.gov, openFDA, or OpenAlex lookups, activate pharma-db-tools.
For cohort tables, biosignals, or DICOM datasets, activate medical-data-tools.
For citation cleanup, evidence matrices, or structured review drafting, activate literature-review-tools.
For hypothesis tests, regression, or effect-size reporting, activate stat-modeling-tools.
For Kaplan-Meier, log-rank, or Cox workflows, activate survival-analysis-tools.
For manuscript critique, hypothesis framing, or reproducibility checklists, activate scientific-workflow-tools.
For molecular, variant, pathway, or structure work, activate bio-tools, bio-db-tools, chem-tools, or docking-tools as appropriate.
Signals
- GitHub stars
- 125
- Forks
- 9
- Last commit
- Mar 2026
Advanced
- Item type
- skill
- Key
clinical-research-tools- Source
- github.com/drugclaw/drugclaw
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