Medication Adherence Message Gen

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

Skill: Medication Adherence Message Gen

ID: 136
Name: medication-adherence-message-gen
Description: Uses behavioral psychology principles to generate SMS/push notification copy for reminding patients to take medication.
Version: 1.0.0

When to Use

• Use this skill when the task needs Use medication adherence message gen for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Use medication adherence message gen for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• enum: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/medication-adherence-message-gen"
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

See ## Workflow above for related 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/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• 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.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Overview

This skill generates personalized medication reminder messages based on behavioral psychology and behavioral economics principles. By applying psychological mechanisms such as social norms, loss aversion, implementation intentions, commitment consistency, etc., it improves patient medication adherence.

Psychological Principles Used

Usage

Command Line

python scripts/main.py [options]

Options

Examples

# Basic usage
python scripts/main.py -m "Atorvastatin" -n "Mr. Zhang"

# Specify psychology principle
python scripts/main.py -m "Metformin" -p "loss_aversion" -t "After breakfast"

# Generate JSON format
python scripts/main.py -m "Antihypertensive" -p "social_norms" -o json

# English output
python scripts/main.py -m "Metformin" -n "John" -l en -p "commitment"

Python API

from scripts.main import generate_message

message = generate_message(
    medication="Atorvastatin",
    patient_name="Mr. Zhang",
    dosage="20mg",
    time="After dinner",
    principle="social_norms",
    tone="encouraging"
)
print(message)

Output Format

Text Mode

【Medication Reminder】Mr. Zhang, it's time after dinner. 95% of patients taking Atorvastatin can adhere to daily medication, and you're one of them! Please take 20mg to keep your heart healthy.

JSON Mode

{
  "medication": "Atorvastatin",
  "patient_name": "Mr. Zhang",
  "principle": "social_norms",
  "tone": "encouraging",
  "message": "【Medication Reminder】Mr. Zhang, it's time after dinner...",
  "psychology_insight": "Uses social norms principle to enhance patient behavioral motivation by emphasizing high adherence rates"
}