Referral Letter Generator
by @aipoch-ai
Generate medical referral letters with patient summary, reason for referral.
clawhub install referral-letter-generatorπ About This Skill
name: referral-letter-generator description: Generate medical referral letters with patient summary, reason for referral. license: MIT skill-author: AIPOCH
Medical Referral Letter Generator
A tool for generating professional medical referral letters for healthcare providers.
When to Use
Key Features
scripts/main.py.references/ for task-specific guidance.assets/sample_referral.json.Dependencies
See ## Prerequisites above for related details.
Python: 3.10+. Repository baseline for current packaged skills.dataclasses: unspecified. Declared in requirements.txt.docx: unspecified. Declared in requirements.txt.enum: unspecified. Declared in requirements.txt.reportlab: unspecified. Declared in requirements.txt.Example Usage
See ## Usage above for related details.
cd "20260318/scientific-skills/Academic Writing/referral-letter-generator"
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.
scripts/main.py.references/ contains supporting rules, prompts, or checklists.assets/.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
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json
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 structured medical referral letters containing:
Use Cases
Usage
Command Line
python scripts/main.py --input patient_data.json --output referral_letter.pdf
Python API
from scripts.main import generate_referral_letterletter = generate_referral_letter(
patient_data={...},
referring_provider={...},
receiving_provider={...},
reason="...",
output_format="pdf" # or "docx", "html", "txt"
)
Input Parameters
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | patient_name | str | Yes | Patient full name | | patient_dob | str | Yes | Date of birth (YYYY-MM-DD) | | patient_id | str | Yes | Medical record number | | diagnosis | str | Yes | Primary diagnosis/reason for referral | | history | str | No | Relevant medical history | | medications | list | No | Current medications | | urgency | str | No | Routine/Urgent/Emergent | | referring_doctor | str | Yes | Referring physician name | | receiving_provider | str | Yes | Target specialist/facility |
Output Formats
Example
{
"patient_name": "John Doe",
"patient_dob": "1975-03-15",
"diagnosis": "Suspected coronary artery disease",
"reason": "Cardiology evaluation for chest pain",
"urgency": "Urgent"
}
Technical Notes
References
See references/ folder for:
Safety & Privacy
Risk Assessment
| Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |
Security Checklist
Prerequisites
Python dependencies
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
Test Cases
1. Basic Functionality: Standard input β Expected output 2. Edge Case: Invalid input β Graceful error handling 3. Performance: Large dataset β Acceptable processing timeLifecycle Status
Output Requirements
Every final response should make these items explicit when they are relevant:
Error Handling
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.Input Validation
This skill accepts requests that match the documented purpose of referral-letter-generator and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
> referral-letter-generator only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.
β‘ When to Use
π‘ Examples
{
"patient_name": "John Doe",
"patient_dob": "1975-03-15",
"diagnosis": "Suspected coronary artery disease",
"reason": "Cardiology evaluation for chest pain",
"urgency": "Urgent"
}
βοΈ Configuration
Python dependencies
pip install -r requirements.txt