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name: tooluniverse-clinical-trial-protocol description: Generate a full clinical trial protocol document (ICH E6(R2)/FDA-style) for a drug or medical device, including background, objectives/endpoints, study design, population/eligibility, intervention details, assessment schedule, statistical analysis plan with sample-size justification, safety monitoring, and regulatory/ethical sections. Use when users say "generate a clinical trial protocol", "draft a protocol for [drug/device]", "write up the study design as a protocol document", "create an FDA submission protocol", "help me write section X of a trial protocol", or "calculate the sample size for this trial". Produces a stakeholder-ready Markdown deliverable, not a feasibility score. disable-model-invocation: true

Clinical Trial Protocol Generation

Turns a trial concept into an actual protocol DOCUMENT, grounded in real precedent-trial and regulatory-database lookups plus a genuine statistical sample-size calculation.

This skill is a downstream step, not a replacement for tooluniverse-clinical-trial-design. That skill answers "is this trial feasible, and what should endpoint/population/comparator/effect-size/duration/regulatory-pathway be?" (a 0-100 feasibility score with evidence grading). This skill takes those six answers — freshly run or already available from a prior tooluniverse-clinical-trial-design report — and drafts the actual protocol sections around them. Do not re-derive feasibility scoring, endpoint selection reasoning, or regulatory-pathway strategy here — call tooluniverse-clinical-trial-design for that and consume its output.

When to Use This Skill

• "Write me a clinical trial protocol for [drug/device]"
• "Generate an FDA submission protocol for our Phase 2 study"
• "Draft the statistical analysis plan / sample size section"
• "I have the feasibility assessment — now turn it into a protocol document"
• "What sample size do I need for a [proportion/continuous/survival] endpoint?"

NOT for (use other skills instead)

• Feasibility scoring, endpoint/population/comparator selection strategy -> tooluniverse-clinical-trial-design (run this FIRST if not already done)
• Patient-to-trial matching for an individual patient -> tooluniverse-clinical-trial-matching
• Drug mechanism / preclinical investigation -> tooluniverse-drug-research or tooluniverse-drug-mechanism-research
• Jurisdiction-aware approval/exclusivity lookups outside the protocol context -> tooluniverse-drug-regulatory

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Workflow

Step 1 — Confirm feasibility inputs exist

Ask the user (or check the conversation) whether a tooluniverse-clinical-trial-design feasibility report already exists for this indication/intervention.

• If yes: reuse its 6-dimension findings directly (endpoint, population, comparator, effect size, duration, regulatory pathway, plus the evidence grades A-D).
• If no: run tooluniverse-clinical-trial-design first. Do not guess these six answers yourself — that skill's precedent-based reasoning exists precisely so this step doesn't fabricate them.

Step 2 — Identify intervention type

Ask (if not already clear): is the intervention a drug/biologic or a medical device? The regulatory pathway, applicable FDA databases, and several protocol sections differ:

	Drug/Biologic	Device
Pathway	IND -> NDA (505(b)(1)/(2)) or BLA	510(k), De Novo, or PMA
Key FDA tools	FDA_OrangeBook_search_drug, FDA_OrangeBook_get_approval_history, OpenFDA_search_drug_approvals, OpenFDA_get_approval_history, FDAGSRS_search_substances	OpenFDADevice_get_classification, OpenFDADevice_search_510k, OpenFDADevice_search_pma, OpenFDADevice_search_recalls, OpenFDADevice_search_adverse_events

See references/regulatory_pathway_notes.md for the full decision guide.

Step 3 — Draft each protocol section with grounded evidence

Work through the 9 sections in references/protocol_sections_guide.md. For every factual claim (precedent endpoint use, comparator standard of care, prior trial enrollment/duration, safety signals, regulatory precedent), call the cited real ToolUniverse tool — do not state a regulatory fact from memory. Core tools used throughout:

• search_clinical_trials, ClinicalTrials_search_studies, ClinicalTrials_get_study, ClinicalTrials_search_by_intervention, get_clinical_trial_eligibility_criteria, get_clinical_trial_outcome_measures, extract_clinical_trial_adverse_events — precedent trials, eligibility patterns, outcome measures, safety signals
• Drug pathway: FDA_OrangeBook_search_drug, FDA_OrangeBook_get_approval_history, FDA_OrangeBook_get_exclusivity, OpenFDA_search_drug_approvals, OpenFDA_get_approval_history, OpenFDA_get_approved_products, FDAGSRS_search_substances
• Device pathway: OpenFDADevice_get_classification, OpenFDADevice_search_510k, OpenFDADevice_search_pma, OpenFDADevice_search_recalls, OpenFDADevice_search_adverse_events, OpenFDADevice_search_udi
• FDAPurpleBook_search_products — for biologics/biosimilars
• PubMed_search_articles — literature support for background/rationale and endpoint validation

LOOK UP, DON'T GUESS: never state an eligibility criterion, a comparator's approval status, a device's regulatory class, or a precedent effect size from memory — every one of those is a tool call away.

Step 4 — Calculate sample size

Run scripts/sample_size_calculator.py with the effect-size assumptions established in Step 1 (from the feasibility report) or looked up from precedent trials in Step 3. Three modes are supported:

# Binary endpoint (e.g. response rate, ORR)
python3 scripts/sample_size_calculator.py two-proportion --p1 0.3 --p2 0.5 --alpha 0.05 --power 0.8

# Continuous endpoint (e.g. change in a biomarker or score)
python3 scripts/sample_size_calculator.py two-mean --mean-diff 0.5 --sd 1.0 --alpha 0.05 --power 0.8

# Time-to-event endpoint (e.g. PFS, OS) -- event_rate must come from precedent trials, not a guess
python3 scripts/sample_size_calculator.py survival --hr 0.7 --event-rate 0.6 --alpha 0.05 --power 0.8

Verify the implementation itself with python3 scripts/sample_size_calculator.py --self-test (checks all three formulas against hand-derived reference values). The calculator never fabricates a sample size — if you don't have a defensible effect-size assumption yet, go back to Step 3 and find one in precedent trials before running it.

Step 5 — Assemble the final document

Fill templates/protocol_template.md with the Step 3 content and the Step 4 statistical justification. Deliver as a single Markdown file, e.g. [INTERVENTION]_clinical_trial_protocol.md. Do not show raw tool JSON to the user — write directly into the document sections as you gather evidence (progressive report writing, same pattern as tooluniverse-clinical-trial-design).

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Reference Files

File	Content
references/protocol_sections_guide.md	What belongs in each of the 9 protocol sections, and which tool to call for evidence in each
references/regulatory_pathway_notes.md	Drug (IND->NDA/BLA) vs device (510(k)/PMA/De Novo) pathway decision guide with real FDA tool names
templates/protocol_template.md	Fill-in-the-blank Markdown template for the final document
scripts/sample_size_calculator.py	Two-proportion, two-mean, and survival (Schoenfeld) sample-size calculator with --self-test

Integration with Other Skills

• tooluniverse-clinical-trial-design: run first for feasibility scoring and the 6-dimension inputs this skill consumes
• tooluniverse-drug-regulatory: deeper jurisdiction-aware regulatory status lookups (FDA vs EMA, exclusivity, generics) if the protocol needs more regulatory depth than Step 2/3 cover
• tooluniverse-clinical-trial-matching: once the protocol exists, match individual patients against its eligibility criteria