prompt-quality-score 1.2.0

The world's first named AI prompt quality score. Score and optimize prompts before LLM inference.

PQS SDK — Prompt Quality Score for Python

The world's first named AI prompt quality score.
Score and optimize prompts before LLM inference. Built for developers, AI agents, and CrewAI workflows.

"Cheaper than one bad prompt."

---

Install

pip install pqs-sdk

# With CrewAI integration
pip install pqs-sdk[crewai]

---

Get a Free API Key

pqs.onchainintel.net — free scoring, no credit card required.

---

Quick Start

Score a prompt

from pqs_sdk import PQSClient

client = PQSClient(api_key="your-pqs-api-key")

result = client.score("help me write a blog post", vertical="content")
print(result)
# → PQS Score: 8/40 | Grade: F | Fail | Prompt lacks context, audience, and goal

Optimize a weak prompt

optimized = client.optimize("help me write a blog post", vertical="content")
print(optimized)
# → Optimized: F (8/40) → B (31/40) [+23 pts]

print(optimized.optimized_prompt)
# → Write a 1,000-word blog post for senior software engineers about...

---

CrewAI Integration

Add PQS as a pre-flight quality gate to any CrewAI agent in 3 lines:

from crewai import Agent
from pqs_sdk import PQSScoreTool, PQSOptimizeTool

# Initialize tools
pqs_score = PQSScoreTool(api_key="your-pqs-api-key")
pqs_optimize = PQSOptimizeTool(api_key="your-pqs-api-key")

# Add to any agent
agent = Agent(
    role="Research Analyst",
    goal="Conduct quality research with well-crafted prompts",
    backstory="You always score prompts before sending them to an LLM.",
    tools=[pqs_score, pqs_optimize]
)

That's it. Your agent now scores every prompt before inference — catching weak inputs before they waste tokens or produce bad outputs.

---

Verticals

PQS scores prompts across 7 domain verticals:

Vertical	Use Case
software	Code generation, debugging, architecture
content	Blog posts, copywriting, social media
business	Strategy, analysis, emails
education	Tutoring, explanations, curriculum
science	Research, data analysis, hypothesis
crypto	Web3, DeFi, blockchain analysis
general	General purpose

---

Pricing

Endpoint	Cost
Score	Free
Optimize	$0.025 USDC
Compare (Claude vs GPT-4o)	$1.25 USDC

Payments via x402 on Base mainnet. Agents pay natively — no human in the loop.

---

Why PQS?

89% of real prompts score D or F. The AI input quality problem is real — and PQS named it.

Bad inputs produce bad outputs regardless of model quality. PQS is the missing pre-flight layer between human intent and model execution.

PQS is to AI agents what a linter is to code. You wouldn't push code without checking it. Don't send a prompt without scoring it.

---

Links

• Product
• GitHub
• npm package (Node.js)
• MCP Server

---

MIT License © 2026 Ken Burbary / OnChainIntel