Meta Picos Generator

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 PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.
• Packaged executable path(s): scripts/validate_skill.py.
• 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

See ## Usage above for related details.

cd "20260316/scientific-skills/Data Analytics/meta-picos-generator"
python -m py_compile scripts/validate_skill.py
python scripts/validate_skill.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/validate_skill.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/validate_skill.py.
• 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/validate_skill.py --help

Meta PICOS Generator

This skill extracts PI(E)COS (Population, Intervention, Comparator, Outcomes, Study Design) elements from a clinical study or meta-analysis title.

Usage

When the user provides a study title, follow this two-step process to extract the information.

Step 1: Clean Title

First, remove any characters related to the type of research from the input title (e.g., "systematic review", "meta-analyses"), which are typically found at the end of the title.

Example:

• Input: "The Impact of Exercise on Cardiovascular Health: A Systematic Review"
• Output: "The Impact of Exercise on Cardiovascular Health"

Step 2: Extract PICOS

Based on the cleaned title from Step 1, act as a clinical research expert and extract the following information:

1. P: Population/Participants with a certain disease.
2. I(E): Intervention/Exposure.
3. C: Comparator/Context.
4. O: Outcomes.
5. S: Study Characteristics/Design.
   • Identify the types of clinical studies that should be included.
   • If the user inputs a meta-analysis title, output the types of studies included (e.g., RCTs, observational studies).
   • Rule: If the type is not mentioned in the title, default to 'Randomized Controlled Trial'.
6. Other: Keep empty if not applicable.

Constraint:

• Strictly extract fields from the title without creative interpretation.
• If any PI(E)COS entry is not present, write nothing for that field.

Output Format

Return the result in the following JSON format (no XML tags):

{
  "P": "...",
  "I(E)": "...",
  "C": "...",
  "O": "...",
  "S": "...",
  "Other": "..."
}

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.