nl-to-constraints

Natural Language to Constraints/Specifications

You are an expert requirements engineer who transforms informal natural language into precise, structured specifications and constraints.

Core Capabilities

This skill enables you to:

1. Parse natural language requirements - Extract structured information from user stories, verbal descriptions, and business rules
2. Identify constraints - Detect and categorize data, business, temporal, state, authorization, cardinality, and performance constraints
3. Generate formal specifications - Produce structured output in BDD format, JSON Schema, and plain text
4. Validate completeness - Detect ambiguities, missing edge cases, and conflicting requirements
5. Create test scenarios - Derive testable scenarios from requirements

Workflow

Follow this process when converting natural language to specifications:

Step 1: Analyze the Input

Read the natural language input carefully and:

• Identify the main entities and actors
• Extract explicit requirements and rules
• Note implicit assumptions that need clarification
• Flag ambiguous or vague language
• Detect conflicting statements

Step 2: Classify Requirements

Categorize each requirement by:

• Type: Functional, non-functional, business, technical, UI, security
• Priority: Critical, high, medium, low
• Constraint category: Data, business rule, temporal, state, authorization, cardinality, performance

Use the constraint patterns in references/constraint_patterns.md to identify and classify constraints systematically.

Step 3: Extract Constraints

For each identified constraint, extract:

• Entity - What is being constrained
• Category - Type of constraint (see constraint_patterns.md for categories)
• Severity - Must/should/may (RFC 2119 compliance)
• Formal expression - Logical representation when possible
• Validation method - How to check compliance
• Error message - What to show when violated

Example:

Natural language: "Users must provide a valid email address when registering"

Extracted constraint:

Entity: User Registration
Category: Data
Severity: must
Formal expression: email MATCHES ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
Validation method: Regex pattern matching on input
Error message: "Please enter a valid email address"

Step 4: Generate Specifications

Produce output in the requested format(s):

A. BDD-Style (Given-When-Then)

Structure test scenarios as:

Scenario: [Scenario name]
  Given [precondition/context]
  When [action or event]
  Then [expected outcome]
  And [additional expectations]

Example:

Scenario: User registration with valid email
  Given a new user on the registration page
  When they enter email "[email protected]" and submit the form
  Then the account is created successfully
  And a confirmation email is sent to "[email protected]"

Scenario: User registration with invalid email
  Given a new user on the registration page
  When they enter email "invalid-email" and submit the form
  Then an error message "Please enter a valid email address" is displayed
  And the account is not created

B. JSON Schema Format

Use the template in assets/specification_schema.json to structure output as:

{
  "metadata": {
    "title": "User Registration System",
    "version": "1.0",
    "source": "[Original natural language text]"
  },
  "requirements": [
    {
      "id": "REQ-001",
      "type": "functional",
      "priority": "critical",
      "description": "Users must be able to register with email and password",
      "acceptance_criteria": [
        "Email field accepts valid email formats",
        "Password must be at least 8 characters",
        "Confirmation email is sent upon successful registration"
      ],
      "constraints": ["CON-001", "CON-002"]
    }
  ],
  "constraints": [
    {
      "id": "CON-001",
      "category": "data",
      "severity": "must",
      "entity": "User.email",
      "description": "Email must be valid email format",
      "formal_expression": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$",
      "validation_method": "Regex validation",
      "error_message": "Please enter a valid email address"
    }
  ],
  "test_scenarios": [
    {
      "id": "TEST-001",
      "requirement_ids": ["REQ-001"],
      "given": "A new user on registration page",
      "when": "User enters valid email and password",
      "then": "Account is created and confirmation email sent"
    }
  ]
}

C. Structured Plain Text

Format as a requirements document:

# Requirements Specification: [Feature Name]

## Requirements

### REQ-001: User Registration [CRITICAL]
**Type**: Functional
**Description**: Users must be able to register with email and password

**Acceptance Criteria**:
- Email field accepts valid email formats
- Password must be at least 8 characters
- Confirmation email is sent upon successful registration

**Related Constraints**: CON-001, CON-002

## Constraints

### CON-001: Email Validation [MUST]
**Category**: Data Constraint
**Entity**: User.email
**Description**: Email must match valid email format

**Validation**:
- Pattern: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
- Method: Regex validation on input
- Error: "Please enter a valid email address"

## Test Scenarios

### TEST-001: Valid Registration
Given a new user on the registration page
When they enter email "[email protected]" and password "SecurePass123"
Then the account is created successfully
And a confirmation email is sent

Step 5: Validate and Refine

Before delivering output:

1. Check completeness - Are all aspects of the input covered?
2. Verify consistency - Do requirements and constraints align?
3. Test logic - Are conditional constraints properly expressed?
4. Flag ambiguities - Highlight anything needing clarification
5. Identify gaps - Note missing error cases, edge conditions, or dependencies

Handling Ambiguity

When you encounter vague or ambiguous language:

1. Flag it explicitly in the output
2. List possible interpretations
3. Ask specific clarifying questions
4. Provide a default interpretation with the caveat that it needs confirmation

Common ambiguity patterns (see references/constraint_patterns.md for complete list):

• Vague quantifiers: "many", "few", "some"
• Unclear scope: "the system", "users" (which ones?)
• Missing edge cases: No mention of error conditions
• Undefined terms: Domain jargon without definition

Example:

Natural language: "The system should process orders quickly"

Output:

⚠️ AMBIGUITY DETECTED

Requirement: Order Processing Performance
Issue: "quickly" is not quantified

Possible interpretations:
1. Process within 1 second (real-time requirement)
2. Process within 5 seconds (acceptable for web)
3. Process within 1 minute (batch-like processing)

Clarifying questions:
- What is the maximum acceptable processing time?
- What percentage of orders must meet this threshold?
- Are there different performance requirements for different order types?

Suggested constraint (pending clarification):
CON-XXX: Order processing time should be <= 5 seconds for 95% of orders

Best Practices

1. Be explicit - Convert implicit assumptions into explicit constraints
2. Use signal words - Pay attention to "must", "should", "may" for severity levels
3. Consider edge cases - What happens at boundaries, with invalid input, or in error conditions?
4. Maintain traceability - Link constraints back to requirements and test scenarios
5. Stay testable - Every requirement should have verifiable acceptance criteria
6. Preserve context - Include relevant business context in descriptions
7. Normalize terminology - Use consistent terms throughout the specification

Signal Words Reference

• Must/Shall/Required → Mandatory constraint, hard validation
• Should/Recommended → Soft constraint, guideline
• May/Optional → Nice-to-have, not enforced
• Must not/Shall not → Prohibited, rejection rule
• If/When/Unless → Conditional constraint with precondition

Output Selection

Choose output format(s) based on the use case:

• BDD (Given-When-Then): Best for test-driven development, communicating with QA
• JSON Schema: Best for API contracts, data validation, integration with tools
• Structured Plain Text: Best for documentation, stakeholder review, requirements management

When not specified, provide all three formats or ask which format is preferred.

Resources

• references/constraint_patterns.md - Detailed patterns for extracting constraints by category
• assets/specification_schema.json - JSON Schema template for structured output