Run Python Agent Queues on PostgreSQL with PgQueuer
Use PgQueuer to run Python agent background jobs from PostgreSQL with transactional enqueue, SKIP LOCKED concurrency, LISTEN/NOTIFY dispatch, recurring schedules, metrics, and a dashboard.
npx skills add agentskillexchange/skills --skill run-python-agent-queues-postgresql-pgqueuer
PgQueuer is useful when a Python agent service already uses PostgreSQL and needs reliable background work without adding Redis, RabbitMQ, or a separate scheduler. The repeatable workflow is to install PgQueuer, create its database tables and functions, define entrypoint consumers, enqueue jobs transactionally alongside application data, and run workers that claim jobs safely with PostgreSQL concurrency primitives.\n\nInvoke this skill when a user needs durable Python background jobs for agent workflows and wants the queue to share the same database consistency boundary as the application. Keep the scope to PostgreSQL-backed Python job processing; do not present PgQueuer as a generic database library or a replacement for every queue system.
What this skill actually does
Inputs and prerequisites: Python 3.10 or newer, PostgreSQL 13 or newer, pgqueuer package, asyncpg-compatible database connection.
Setup notes: Install with pip install pgqueuer, then run pgq install to create the database objects. Define PgQueuer entrypoint consumers and run them with pgq run module:function.
Source and verification boundary: use https://janbjorge.github.io/pgqueuer/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Custom Agents workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.