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Generate realistic test data (SQL, CSV, JSON) for any schema. Use when the user needs seed data, fixtures, or fake records.
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Full QA audit orchestrator. Use when the user asks to "test my app", "run a full QA audit", "test everything", or invokes /qa-audit. Discovers what kind of application this is, selects the relevant QA agents (ui-inspector, api-sentinel, security-scout, perf-guardian, data-validator, regression-watcher), runs them, and merges their reports into one master QA report.
Onboard the QA toolkit onto any project. Use on first contact with a new codebase, when the user says "set up QA", "qa init", "/qa-init", or when any QA agent finds no qa-profile.md. Studies the project deeply and writes the persistent QA profile that every other agent reads first — the toolkit's long-term memory for this project.
Wire the QA suite into CI. Use when the user wants tests running automatically on every PR or on a schedule: "add this to CI", "run tests on every push", "/qa-ci". Generates GitHub Actions workflows for the generated Playwright suite and (optionally) scheduled full audits.
Self-healing for the generated test suite. Use when previously-green Playwright tests are failing, the user says "my tests broke", "fix the tests", "/qa-heal", or after a UI refactor breaks selectors. Classifies each failure (real bug vs test drift) and repairs the drift — never papers over real bugs.
Compare two QA audit runs and report what improved, what regressed, and what persists. Use when the user asks "did it improve since last time?", "compare audits", "QA trend", or invokes /qa-trend. Requires at least two timestamped runs under qa-reports/runs/.
Audit web pages for WCAG 2.1 compliance. Use when the user asks about accessibility, a11y, WCAG, screen readers, or contrast issues.
Baseado na classificação ocupacional SOC
| name | test-data-factory |
| description | Generate realistic test data (SQL, CSV, JSON) for any schema. Use when the user needs seed data, fixtures, or fake records. |
Generate realistic, consistent test data for any schema or domain.
This skill generates realistic test data based on schemas, table definitions, or domain descriptions. It creates data that respects constraints, relationships, and business rules while covering edge cases that matter for testing.
When the user provides a schema or describes the data they need:
', ", <, >, &, \n)INSERT INTO users (id, email, name, created_at, status) VALUES
(1, 'maria.garcia@example.com', 'Maria Garcia', '2024-01-15 08:30:00', 'active'),
(2, 'james.chen@example.com', 'James Chen', '2024-02-20 14:15:00', 'active'),
(3, 'test+special@example.co.uk', 'O''Brien, Ana-Maria', '2024-03-01 00:00:00', 'pending');
id,email,name,created_at,status
1,maria.garcia@example.com,Maria Garcia,2024-01-15T08:30:00Z,active
[
{
"id": 1,
"email": "maria.garcia@example.com",
"name": "Maria Garcia",
"created_at": "2024-01-15T08:30:00Z",
"status": "active"
}
]
TEST_USERS = [
{"id": 1, "email": "maria.garcia@example.com", "name": "Maria Garcia"},
]
Generate 50 test users with orders. Each user should have 1-5 orders.
Schema: users(id, email, name, plan), orders(id, user_id, total, status, created_at)
Output as SQL INSERTs for PostgreSQL.
Create edge case test data for a registration form:
fields: name (required, max 100), email (required, unique), age (optional, 13-120), bio (optional, max 500)