Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
Validates acceptance criteria for testability, traces requirements to test cases, and generates BDD scenarios from user stories. Use when validating acceptance criteria, building requirements traceability matrices, managing Gherkin scenarios, or ensuring complete requirements coverage before development.
日本語の概要は準備中です。原文の説明を表示しています。
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Guide the use of v3's requirements validation capabilities including acceptance criteria parsing, requirements traceability, BDD scenario generation, and coverage gap identification.
# Parse acceptance criteria
aqe requirements parse --source jira --project MYAPP
# Build traceability matrix
aqe requirements trace --requirements reqs/ --tests tests/
# Generate BDD scenarios
aqe requirements bdd --story US-123 --output features/
# Check requirements coverage
aqe requirements coverage --sprint current
// Requirements validation
Task("Validate acceptance criteria", `
Review acceptance criteria for sprint stories:
- Check SMART criteria (Specific, Measurable, Achievable, Relevant, Testable)
- Identify ambiguous requirements
- Flag missing edge cases
- Suggest improvements
`, "qe-acceptance-criteria")
// Traceability matrix
Task("Build traceability", `
Create requirements traceability matrix:
- Map user stories to test cases
- Identify untested requirements
- Find orphan tests (no linked requirement)
- Calculate coverage metrics
`, "qe-traceability-builder")
await acceptanceCriteria.validate({
source: {
type: 'jira',
project: 'MYAPP',
stories: 'sprint=current'
},
validation: {
specific: true,
measurable: true,
achievable: true,
relevant: true,
testable: true
},
output: {
score: true,
issues: true,
suggestions: true
}
});
await traceabilityBuilder.build({
requirements: {
source: 'jira',
types: ['story', 'task', 'bug']
},
artifacts: {
tests: 'tests/**/*.test.ts',
code: 'src/**/*.ts',
documentation: 'docs/**/*.md'
},
output: {
matrix: true,
coverage: true,
gaps: true,
orphans: true
}
});
await bddGenerator.generate({
requirements: userStory,
format: 'gherkin',
scenarios: {
happyPath: true,
edgeCases: true,
errorCases: true,
dataVariations: true
},
output: {
featureFile: true,
stepDefinitions: 'skeleton'
}
});
await requirementsCoverage.analyze({
scope: 'sprint-23',
metrics: {
requirementsCovered: true,
testCasesCoverage: true,
automationCoverage: true,
riskAssessment: true
},
report: {
summary: true,
details: true,
recommendations: true
}
});
interface TraceabilityMatrix {
requirements: {
id: string;
title: string;
type: string;
priority: string;
status: string;
linkedTests: string[];
linkedCode: string[];
coverage: 'full' | 'partial' | 'none';
}[];
tests: {
id: string;
name: string;
type: 'unit' | 'integration' | 'e2e';
linkedRequirements: string[];
automated: boolean;
}[];
coverage: {
requirementsCovered: number;
requirementsPartial: number;
requirementsUncovered: number;
orphanTests: number;
};
gaps: {
requirement: string;
missingCoverage: string[];
risk: 'high' | 'medium' | 'low';
}[];
}
# Generated feature file
Feature: User Registration
As a new user
I want to create an account
So that I can access the platform
@happy-path
Scenario: Successful registration with valid details
Given I am on the registration page
When I enter valid email "user@example.com"
And I enter valid password "SecurePass123!"
And I click the register button
Then I should see a success message
And I should receive a confirmation email
@edge-case
Scenario: Registration with existing email
Given a user exists with email "existing@example.com"
When I try to register with email "existing@example.com"
Then I should see an error "Email already registered"
quality_checks:
acceptance_criteria:
has_given_when_then: preferred
is_testable: required
is_measurable: required
no_ambiguity: required
user_story:
follows_template: "As a <role>, I want <feature>, so that <benefit>"
has_acceptance_criteria: required
estimated: preferred
completeness:
edge_cases_identified: required
error_scenarios_covered: required
non_functional_considered: preferred
await requirementsValidator.sprintReview({
sprint: 'current',
checks: {
storiesComplete: true,
criteriaValidated: true,
testsLinked: true,
coverageAdequate: true
},
gates: {
minCoverage: 80,
maxUntested: 2,
requireDemo: true
}
});
Primary Agents: qe-acceptance-criteria, qe-traceability-builder, qe-bdd-specialist Coordinator: qe-requirements-coordinator Related Skills: qe-test-generation, qe-quality-assessment
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。