Guide · Agent skill
How to use the Python Background Jobs agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The python-background-jobs skill teaches an AI agent how to move slow or unreliable work out of a Python web request and into a background worker. This guide explains what the skill changes in the agent's output, how to install it from the ZIP, how to ask for it in plain language, and what to check before you rely on the code it writes. You do not need any prior experience with agent skills.
Python Background Jobswshobson/agents · MIT Download the skill Quick start
- 1Download the ZIP. Download the python-background-jobs skill ZIP from this page.
- 2Unzip it into a skills folder. Unzip it into ~/.claude/skills/ for a personal install, or into .claude/skills/ inside your project for a project-only install.
- 3Or upload it in the Claude apps. In the Claude apps, open Settings, go to Capabilities, then Skills, and upload the skill.
- 4Describe the slow task. Ask for the work in plain terms, such as moving an email, export or webhook off the request path, and name your Python stack.
- 5Review and test the output. Check retries, timeouts and idempotency in the generated code, fill in any placeholder branches, and test it against a real worker before shipping.
What does the Python Background Jobs skill make the agent do?
When you ask the agent to build something slow or unreliable in a Python app, the skill steers it toward a task queue pattern. The API accepts the request, enqueues a job, and returns immediately with a job ID while workers process the work asynchronously.
The skill also teaches the agent to track each job through a simple state machine (pending, running, succeeded or failed), to design tasks so they are safe to re-run, and to set timeouts and retry rules. Its examples use Celery with a Redis broker.
- Work it targets: emails, notifications, webhooks, report generation, data exports, uploads and media transformations, and calls to unreliable external services
- Patterns it covers: return a job ID immediately, Celery task configuration with retries and time limits, idempotent tasks, and job state persistence
- Advanced pattern in the reference file: a dead letter queue for tasks that fail permanently
How do you install the skill?
Download the skill ZIP from this page and unzip it. You can install it for yourself across projects or for a single project.
For a personal install, unzip it into ~/.claude/skills/. For a project-only install, unzip it into the .claude/skills/ folder inside your project. The skill's files are SKILL.md and references/details.md.
If you use the Claude apps instead, open Settings, go to Capabilities, then Skills, and upload the skill there.
- Personal: ~/.claude/skills/
- Project: .claude/skills/ inside your project folder
- Claude apps: Settings → Capabilities → Skills
How do you trigger the skill with natural requests?
You do not need a special command. Describe a task that is slow, depends on an outside service, or should not block the user, and say that you want background processing. The skill's description covers async task processing, job queues, long-running operations and decoupling work from request handling, so requests in those terms are the ones most likely to use it.
Name the Python stack and the kind of work. The more specific you are about what is slow, the more useful the generated structure will be.
- Add a background job that sends a welcome email after signup, and return a job ID to the client
- Our report export takes several minutes. Move it to a worker and let the frontend poll for status
- Process incoming webhooks in the background with retries and a dead letter queue for failures
- Make the payment call idempotent so a retried worker cannot charge a customer twice
What do you need before you start?
The skill assumes a Python codebase. Its examples use Celery as the task queue, with Redis as the message broker, and they store job records in a database using raw SQL with an asyncpg-style interface. The skill also names RQ, Dramatiq, AWS SQS and GCP Tasks as alternatives.
Adding a queue adds infrastructure: a broker and at least one worker process. If your app only needs a fast synchronous call, the skill's own framing suggests you may not need any of this.
- Python
- Celery, with a Redis broker in the examples
- A database for job records
- Alternatives named in the skill: RQ, Dramatiq, AWS SQS, GCP Tasks
from celery import Celery
app = Celery("tasks", broker="redis://localhost:6379")
@app.task
def send_email(to: str, subject: str, body: str) -> None:
# This runs in a background worker
email_client.send(to, subject, body)
# In your API handler
send_email.delay("user@example.com", "Welcome!", "Thanks for signing up")What are the safety notes and limits?
The skill does not run scripts and does not use the network. It only shapes the code the agent writes, so you still need to review and test that code yourself.
The snippets are illustrative, not a tested library. Some are incomplete: the idempotency example leaves a placeholder branch that does nothing, and it uses dependencies such as orders_repo and payment_provider that are not defined in the snippet. The quick-start example also uses an undefined email_client. Expect the agent to adapt and verify this code before it runs in production.
- Review every retry rule so permanent errors, such as invalid credentials or validation failures, are not retried
- Confirm the idempotency key is passed to any external payment or email provider that supports one
- Check that the time limits suit your longest real job
What tips make the output better?
The skill's checklist is a good review list even if you write the code yourself. Ask the agent to apply each item and explain where it did so.
The best practices in the skill include returning immediately, persisting job state so status polling and debugging work, setting soft and hard timeouts, using a dead letter queue, logging state transitions, and monitoring queue depth. Its reference file adds a dead letter queue example for webhooks that exhaust their retries.
- Ask for a job status table and a poll endpoint alongside the enqueue code
- Ask for both soft and hard time limits, and for retries with exponential backoff only on transient errors
- Ask for a dead letter queue step when failures must be inspected later
- Ask the agent to point out any placeholder or undefined names it left in the code
What people use it for
- Sending welcome emails or notifications after a signup without making the user wait for the mail server
- Generating a large report or data export in a worker and letting the frontend poll a job status endpoint
- Processing incoming webhooks with retries and a dead letter queue for failures that need manual review
- Charging a payment through an external provider with an idempotency key so a retried task does not double-charge
Frequently asked questions
How do I install the Python Background Jobs agent skill?
Download the ZIP from this page and unzip it into ~/.claude/skills/ for a personal install, or into .claude/skills/ in your project for a project-only install. In the Claude apps, you can instead upload it under Settings, then Capabilities, then Skills.
Does this skill require Celery and Redis?
The examples use Celery with a Redis broker, but the skill says alternatives such as RQ, Dramatiq, AWS SQS and GCP Tasks are equally valid. You need some task queue, and the examples show how it would look with Celery.
Is the Python Background Jobs skill free to use?
The skill is listed under the MIT licence, as shown on this page. Check the licence text in the source repository for the full terms before you reuse it in your own project.
Does the skill run code or use the network?
According to this page, the skill runs no scripts and uses no network. It provides instructions and illustrative code that the agent writes for you, which you then need to review and test.
Why does the agent's idempotency code look incomplete?
The skill's idempotency example includes a placeholder branch that does nothing and refers to dependencies that are not defined in the snippet. It is meant as a pattern to adapt, not a finished implementation, so ask the agent to fill in and verify those parts.
How is this different from just asking an agent to write a Celery task?
Without the skill, an agent may write a task with no retry rules, timeouts or idempotency handling. This skill adds a checklist that pushes the agent to return a job ID right away, track job states, set time limits, and avoid retrying permanent failures.
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Our verdict
Use this skill if you are a Python developer adding slow or unreliable work such as emails, exports, uploads or webhooks to a web API, or if you want a checklist of retry, timeout and idempotency patterns. Pick something else if your app is simple enough for synchronous calls, if your stack is not Python, or if you need a ready-made, tested queue library rather than snippets that you must adapt and verify.
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