Clinic Research Design

Source: https://github.com/aipoch/medical-research-skills

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Generates a structured prompt framework for clinical study protocols. Supports Diagnostic, Efficacy, Etiology, and Prognosis studies. Calculates sample size and provides logic guides for LLMs.
• Packaged executable path(s): scripts/calculators/sample_size.py plus 4 additional script(s).
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Protocol Design/clinic-research-design"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/calculators/sample_size.py with additional helper scripts under scripts/.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Validation Shortcut

Run this minimal command first to verify the supported execution path:

python scripts/main.py --help

Clinic Research Design (Agentic Version)

This skill serves as a Logic & Structure Engine for AI Agents. Instead of outputting a finished text document, it generates a Structured Prompt / Writing Guide.

An Agent (like a specialized medical writer bot) should call this skill to get the "Skeleton" and "Logic", and then use its own LLM capabilities to "Flesh out" the content based on the detailed instructions provided in the output.

Capabilities

1. Logic Structuring: Automatically selects the correct protocol template (e.g., STARD for diagnostics, SPIRIT for trials) based on input.
2. Calculation: Performs deterministic sample size calculations (which LLMs are bad at).
3. Prompt Engineering: Generates context-aware instructions for each section (Introduction, Methods, Stats) tailored to the specific P/I/C/O.

Usage for Agents

When an Agent receives a request like "Write a protocol for a diabetes drug trial", it should:

1. Call this skill:

   python scripts/main.py --type efficacy --P "Type 2 Diabetes" --I "Metformin" --C "Placebo" --O "HbA1c" --study_design "RCT"

2. Read the Output: The output file (e.g., output/protocol.md) will contain sections like:

   [LLM Instruction]: Write a 3-4 paragraph introduction. Discuss gaps in understanding risk factors for...

3. Execute Instructions: The Agent should then read these instructions and generate the final, polished content for the user.

Arguments

• --type: diagnostic, efficacy, etiology, prognosis
• --P, --I, --C, --O: PICO elements.
• --study_design: Specific design (e.g., RCT, cohort).
• --sensitivity, --specificity, --alpha, --power: Statistical parameters.

Output Format

The output is a Markdown file containing:

• Headers: Standard protocol sections.
• Blockquotes: > [LLM Instruction]: ... specific guidance for the LLM on what to write and how to write it for that specific section.
• Hard Data: Pre-calculated values (Sample Size) that the LLM must strictly follow.

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.