Stats
Actions
Tags
From litestar
Provides Docker-based database fixtures for integration testing with pytest. Supports PostgreSQL, MySQL, Oracle, MSSQL, CockroachDB, MongoDB, Redis, and more.
How this skill is triggered — by the user, by Claude, or both
Slash command
/litestar:pytest-databasesThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A pytest plugin providing ready-made database fixtures for testing using Docker containers.
A pytest plugin providing ready-made database fixtures for testing using Docker containers.
For detailed guides and code examples, refer to the following documents in references/:
Add to conftest.py:
pytest_plugins = ["pytest_databases.docker.postgres"]
def test_database(postgres_service):
# Use postgres_service.host, .port, etc.
pass
xdist isolation helpers. For parallel runs, select the database-level or server-level isolation fixtures from references/xdist.md instead of sharing one schema across workers.conftest.py declares only the database plugins you actually use (pytest_plugins = [...])postgres_service, mysql_service, etc.) rather than opening raw connectionspytest -n auto) produce isolated data — verified via references/xdist.mdimport pytest
pytest_plugins = ["pytest_databases.docker.postgres"]
@pytest.mark.anyio
async def test_user_insert(postgres_service, postgres_connection):
await postgres_connection.execute(
"INSERT INTO users (email) VALUES ($1)", "[email protected]"
)
row = await postgres_connection.fetchrow(
"SELECT email FROM users WHERE email = $1", "[email protected]"
)
assert row["email"] == "[email protected]"
AsyncTestClient.npx claudepluginhub litestar-org/litestar-skills --plugin litestarSearches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.