Semantic Consistency Auditor

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• bert_score: unspecified. Declared in requirements.txt.
• comet: unspecified. Declared in requirements.txt.
• dataclasses: unspecified. Declared in requirements.txt.
• numpy: unspecified. Declared in requirements.txt.
• torch: unspecified. Declared in requirements.txt.
• yaml: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/semantic-consistency-auditor"
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

Semantic Consistency Auditor is a medical AI evaluation tool used to assess the semantic consistency between AI-generated clinical notes and expert-written gold standards from a semantic level. This tool is not limited to traditional string matching or bag-of-words models, but uses deep learning models to understand semantic entailment relationships, capable of identifying expressions with different wording but similar meaning.

Algorithms

1. BERTScore

BERTScore uses pre-trained BERT model contextual embeddings to calculate similarity between candidate text and reference text:

• Precision: How much semantics in the candidate text is covered by the reference text
• Recall: How much semantics in the reference text is covered by the candidate text
• F1 Score: Harmonic mean of Precision and Recall

2. COMET (Cross-lingual Optimized Metric for Evaluation of Translation)

COMET is a neural network-based evaluation metric originally used for machine translation evaluation, applicable to semantic entailment tasks:

• Uses XLM-RoBERTa encoder to capture deep semantics
• Outputs semantic consistency scores between 0-1
• Gives high scores to semantically equivalent but differently expressed text

Installation

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Linux/Mac

# Or venv\Scripts\activate  # Windows

# Install dependencies
pip install bertscore comet-ml transformers torch

Configuration

Configure in ~/.openclaw/skills/semantic-consistency-auditor/config.yaml:

# BERTScore Configuration