nl-to-constraints

Translate natural-language requirements into formal constraints and property-based tests.

1|2|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/santosomar/general-secure-coding-agent-skills --skill nl-to-constraints-santosomar
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nl-to-constraints
Source: https://github.com/santosomar/general-secure-coding-agent-skills/tree/main/skills/requirements/nl-to-constraints
Command: npx skills add https://github.com/santosomar/general-secure-coding-agent-skills --skill nl-to-constraints-santosomar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill bridges the gap between human-readable requirements and machine-checkable formal constraints, ensuring software behaves as intended.

Core Features & Use Cases

  • Formal Constraint Generation: Translates natural language requirements into precise logical predicates, schemas, or property-based tests.
  • Validation & Verification: Enables automated checking of data shapes, single-state invariants, input/output relations, and multi-step behaviors.
  • Use Case: Convert a requirement like "Usernames must be between 3 and 32 alphanumeric characters, starting with a letter" into a JSON Schema pattern or a property test.

Quick Start

Use the nl-to-constraints skill to translate the requirement "The system must always maintain a non-negative balance" into a formal constraint.

Frequently Asked Questions about nl-to-constraints

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I translate natural language requirements into formal constraints for validation?

You can generate property-based tests from natural language requirements by translating specifications into property test generators that validate data shapes, single-state invariants, input/output relations, and multi-step behaviors.

Can I generate JSON Schema from text specifications for data shape validation?

Generating JSON Schema from text specifications is supported by translating natural language requirements into formal constraints, allowing you to validate data shapes like converting username rules into a JSON Schema pattern.

What is the best way to bridge requirements to formal verification logic?

Bridging requirements to formal verification logic involves translating natural language specifications into machine-checkable formal constraints, ensuring automated checking of data shapes, invariants, and multi-step behaviors.

Does property-based testing work for validating multi-step behaviors and input/output relations?

Property-based testing supports validating multi-step behaviors and input/output relations by generating formal constraints from natural language requirements to automate the verification of complex software behaviors.

When should I not use natural language processing for formal constraint generation?

You should avoid natural language processing for formal constraint generation when requirements are ambiguous or lack defined logical predicates, as translating unclear specifications may result in inaccurate schemas or property tests.