promptclarity-sdk 0.1.0

A quality, clarity, and risk validation SDK for LLM inputs.

PromptClarity SDK

Guard prompts before they become bad outputs.

PromptClarity SDK is a pre-LLM validation layer that detects unclear, incomplete, risky, or low-quality prompts before they reach an AI system.

Positioning

PromptClarity SDK helps teams validate LLM inputs for:

• prompt quality scoring
• missing context detection
• ambiguity detection
• dataset metadata validation
• agent-readiness checks
• RAG-readiness checks
• cost and token waste reduction
• safer enterprise AI workflows

Enterprise tagline:

A quality, clarity, and risk validation SDK for LLM inputs.

Install

pip install promptclarity-sdk

Usage

from promptclarity import PromptClarity

guard = PromptClarity()

result = guard.validate("Analyze this data and give insights")

print(result.to_dict())

Example output:

{
    "status": "needs_clarification",
    "prompt_score": 42,
    "risk_level": "low",
    "missing_items": [
        "success criteria",
        "dataset details",
        "business objective",
        "output format",
    ],
    "recommendations": [
        "Define the success criteria for a useful answer.",
        "Describe the dataset, including source, fields, and size.",
        "State the business objective or decision the answer should support.",
        "Define the expected output format.",
        "Specify the type of analysis required.",
        "Mention whether you need EDA, prediction, reporting, or recommendations.",
    ],
}

Package Layout

promptclarity/
|-- analyzer.py          # prompt quality analyzer
|-- rules.py             # rule-based checks
|-- risk.py              # unsafe / sensitive / vague input checks
|-- metadata.py          # dataset/file metadata analysis
|-- recommender.py       # missing-detail suggestions
|-- prompt_builder.py    # improved prompt generator
`-- __init__.py

Development

python -m pytest

Release Checklist

1. Update the version in pyproject.toml and promptclarity/__init__.py.
2. Run python -m pytest.
3. Build with python -m build.
4. Check the distribution with python -m twine check dist/*.
5. Upload to TestPyPI first, then PyPI.