Tone Adjuster

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

Medical Tone Adjuster

Convert medical text between academic rigor and patient-friendly language while preserving clinical accuracy.

When to Use

• Use this skill when the task needs Use when converting medical text between academic and patient-friendly tones, translating medical jargon for patients, adapting research papers for public audiences, or rewriting clinical notes for patient handouts. Maintains medical accuracy while adjusting readability level.
• 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 when converting medical text between academic and patient-friendly tones, translating medical jargon for patients, adapting research papers for public audiences, or rewriting clinical notes for patient handouts. Maintains medical accuracy while adjusting readability level.
• 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

• 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 "20260318/scientific-skills/Academic Writing/tone-adjuster"
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 demo

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.

Quick Start

from scripts.tone_adjuster import ToneAdjuster

adjuster = ToneAdjuster()

# Academic → Patient-friendly
patient_text = adjuster.convert(
    text="The patient presents with acute myocardial infarction...",
    target_tone="patient-friendly"
)

# Patient-friendly → Academic
academic_text = adjuster.convert(
    text="I had a heart attack...",
    target_tone="academic"
)

Core Capabilities

1. Academic to Patient-Friendly

adjuster = ToneAdjuster()
result = adjuster.to_patient_friendly(
    "The patient exhibits tachycardia with irregular rhythm
     consistent with atrial fibrillation",
    reading_level="8th_grade"
)

Conversion Rules:

• Replace medical terms with common equivalents
• Shorten sentence length (aim for <15 words)
• Use active voice
• Remove unnecessary qualifiers

Examples:

2. Patient-Friendly to Academic

result = adjuster.to_academic(
    "My stomach hurts after eating spicy food",
    add_citations=True
)

# Output: "The patient reports postprandial abdominal pain

#          exacerbated by capsaicin-containing foods"

3. Reading Level Assessment

metrics = adjuster.assess_reading_level(text)
print(f"Grade level: {metrics.grade_level}")
print(f"Medical terms: {metrics.jargon_count}")
print(f"Recommendations: {metrics.suggestions}")

Reading Levels:

• 5th-6th Grade: Young patients, general public
• 8th Grade: Most adult patients
• 12th Grade: Educated lay audiences
• College: Healthcare professionals