Agent skill

Python Background Jobs

by wshobson in wshobson/agents

Python background job patterns including task queues, workers, and event-driven architecture.

MITAuto-check passedBackend & APIs

Install Python Background Jobs

skills CLI
$ npx skills add wshobson/agents --skill python-background-jobs -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wshobson/agents python-background-jobs --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/python-development/skills/python-background-jobs .claude/skills/python-background-jobs && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
python-background-jobs
GitHub stars
40k
Token cost
~1.8k tokens
SKILL.md length
356 words
Files
2 (incl. references)
Skills in repo
142
Repo updated
First seen
Licence
MIT

At a glance

Python background job patterns including task queues, workers, and event-driven architecture.

  • Works in 4 steps: Task Queue Pattern → Idempotency → Job State Machine → …
  • Implementing async task processing
  • SKILL.md covers When to Use This Skill, Core Concepts, Quick Start and Fundamental Patterns, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Python Background Jobs is an agent skill from wshobson/agents. Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/details.md`).

It sits in Backend & APIs, covering Background jobs. It works with Python. The repository describes itself as: Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi. The licence is MIT.

When your agent uses it

  • Implementing async task processing
  • Long-running operations
  • Decoupling work from request/response cycles

Example prompts

  • “/python-background-jobs”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Task Queue Pattern
  2. Idempotency
  3. Job State Machine
  4. At-Least-Once Delivery

What it can do on your machine

Read from SKILL.md and the folder at commit 46891e7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Python Background Jobs loads about 1.8k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 356 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 356 words, ~1,827 tokens.

Download SKILL.mdSave it as .claude/skills/python-background-jobs/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
python-background-jobs
description
Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Python Background Jobs & Task Queues

Decouple long-running or unreliable work from request/response cycles. Return immediately to the user while background workers handle the heavy lifting asynchronously.

When to Use This Skill

  • Processing tasks that take longer than a few seconds
  • Sending emails, notifications, or webhooks
  • Generating reports or exporting data
  • Processing uploads or media transformations
  • Integrating with unreliable external services
  • Building event-driven architectures

Core Concepts

1. Task Queue Pattern

API accepts request, enqueues a job, returns immediately with a job ID. Workers process jobs asynchronously.

2. Idempotency

Tasks may be retried on failure. Design for safe re-execution.

3. Job State Machine

Jobs transition through states: pending → running → succeeded/failed.

4. At-Least-Once Delivery

Most queues guarantee at-least-once delivery. Your code must handle duplicates.

Quick Start

This skill uses Celery for examples, a widely adopted task queue. Alternatives like RQ, Dramatiq, and cloud-native solutions (AWS SQS, GCP Tasks) are equally valid choices.

python
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")

Fundamental Patterns

Pattern 1: Return Job ID Immediately

For operations exceeding a few seconds, return a job ID and process asynchronously.

python
from uuid import uuid4
from dataclasses import dataclass
from enum import Enum
from datetime import datetime

class JobStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"
    SUCCEEDED = "succeeded"
    FAILED = "failed"

@dataclass
class Job:
    id: str
    status: JobStatus
    created_at: datetime
    started_at: datetime | None = None
    completed_at: datetime | None = None
    result: dict | None = None
    error: str | None = None

# API endpoint
async def start_export(request: ExportRequest) -> JobResponse:
    """Start export job and return job ID."""
    job_id = str(uuid4())

    # Persist job record
    await jobs_repo.create(Job(
        id=job_id,
        status=JobStatus.PENDING,
        created_at=datetime.utcnow(),
    ))

    # Enqueue task for background processing
    await task_queue.enqueue(
        "export_data",
        job_id=job_id,
        params=request.model_dump(),
    )

    # Return immediately with job ID
    return JobResponse(
        job_id=job_id,
        status="pending",
        poll_url=f"/jobs/{job_id}",
    )
Pattern 2: Celery Task Configuration

Configure Celery tasks with proper retry and timeout settings.

python
from celery import Celery

app = Celery("tasks", broker="redis://localhost:6379")

# Global configuration
app.conf.update(
    task_time_limit=3600,          # Hard limit: 1 hour
    task_soft_time_limit=3000,      # Soft limit: 50 minutes
    task_acks_late=True,            # Acknowledge after completion
    task_reject_on_worker_lost=True,
    worker_prefetch_multiplier=1,   # Don't prefetch too many tasks
)

@app.task(
    bind=True,
    max_retries=3,
    default_retry_delay=60,
    autoretry_for=(ConnectionError, TimeoutError),
)
def process_payment(self, payment_id: str) -> dict:
    """Process payment with automatic retry on transient errors."""
    try:
        result = payment_gateway.charge(payment_id)
        return {"status": "success", "transaction_id": result.id}
    except PaymentDeclinedError as e:
        # Don't retry permanent failures
        return {"status": "declined", "reason": str(e)}
    except TransientError as e:
        # Retry with exponential backoff
        raise self.retry(exc=e, countdown=2 ** self.request.retries * 60)
Pattern 3: Make Tasks Idempotent

Workers may retry on crash or timeout. Design for safe re-execution.

python
@app.task(bind=True)
def process_order(self, order_id: str) -> None:
    """Process order idempotently."""
    order = orders_repo.get(order_id)

    # Already processed? Return early
    if order.status == OrderStatus.COMPLETED:
        logger.info("Order already processed", order_id=order_id)
        return

    # Already in progress? Check if we should continue
    if order.status == OrderStatus.PROCESSING:
        # Use idempotency key to avoid double-charging
        pass

    # Process with idempotency key
    result = payment_provider.charge(
        amount=order.total,
        idempotency_key=f"order-{order_id}",  # Critical!
    )

    orders_repo.update(order_id, status=OrderStatus.COMPLETED)

Idempotency Strategies:

  1. Check-before-write: Verify state before action
  2. Idempotency keys: Use unique tokens with external services
  3. Upsert patterns: INSERT ... ON CONFLICT UPDATE
  4. Deduplication window: Track processed IDs for N hours
Show full SKILL.md (123 more words)Show less
Pattern 4: Job State Management

Persist job state transitions for visibility and debugging.

python
class JobRepository:
    """Repository for managing job state."""

    async def create(self, job: Job) -> Job:
        """Create new job record."""
        await self._db.execute(
            """INSERT INTO jobs (id, status, created_at)
               VALUES ($1, $2, $3)""",
            job.id, job.status.value, job.created_at,
        )
        return job

    async def update_status(
        self,
        job_id: str,
        status: JobStatus,
        **fields,
    ) -> None:
        """Update job status with timestamp."""
        updates = {"status": status.value, **fields}

        if status == JobStatus.RUNNING:
            updates["started_at"] = datetime.utcnow()
        elif status in (JobStatus.SUCCEEDED, JobStatus.FAILED):
            updates["completed_at"] = datetime.utcnow()

        await self._db.execute(
            "UPDATE jobs SET status = $1, ... WHERE id = $2",
            updates, job_id,
        )

        logger.info(
            "Job status updated",
            job_id=job_id,
            status=status.value,
        )

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

  1. Return immediately - Don't block requests for long operations
  2. Persist job state - Enable status polling and debugging
  3. Make tasks idempotent - Safe to retry on any failure
  4. Use idempotency keys - For external service calls
  5. Set timeouts - Both soft and hard limits
  6. Implement DLQ - Capture permanently failed tasks
  7. Log transitions - Track job state changes
  8. Retry appropriately - Exponential backoff for transient errors
  9. Don't retry permanent failures - Validation errors, invalid credentials
  10. Monitor queue depth - Alert on backlog growth

© wshobson, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/python-development/skills/python-background-jobs of wshobson/agents.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit 46891e7

Compare with similar skills

Python Background Jobs next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Python Background Jobs this skillwshobson/agents40k—~1.8kAutomated safety check: PassMIT
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K8e Sandboxxiaods/k8e499—~6kAutomated safety check: PassApache-2.0
Temporal Developertemporalio/skill-temporal-developer230—~2.5kAutomated safety check: PassMIT
FbaZhongye1/KnowAgenticRAG143—~758Automated safety check: PassNone
Dv Adminmicrosoft/Dataverse-skills241—~5.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Python Background Jobs

What does Python Background Jobs do?

Python background job patterns including task queues, workers, and event-driven architecture. Python Background Jobs is an agent skill from wshobson/agents. Python background job patterns including task queues, workers, and event-driven architecture.

When should I use Python Background Jobs?

Python Background Jobs fits situations like: implementing async task processing; long-running operations; decoupling work from request/response cycles.

How do I install Python Background Jobs in Claude Code?

Run `npx skills add wshobson/agents --skill python-background-jobs -a claude-code`. Or copy the skill folder (plugins/python-development/skills/python-background-jobs in wshobson/agents) into .claude/skills/python-background-jobs in your project. Claude Code loads it when a task matches its description.

How do I install Python Background Jobs in Codex?

Run `npx skills add wshobson/agents --skill python-background-jobs -a codex`. Or copy the skill folder (plugins/python-development/skills/python-background-jobs in wshobson/agents) into .agents/skills/python-background-jobs in your project. Codex loads it when a task matches its description.

Can I use Python Background Jobs in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wshobson/agents --skill python-background-jobs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-background-jobs, .gemini/skills/python-background-jobs, .github/skills/python-background-jobs and .opencode/skills/python-background-jobs in your project.

What does Python Background Jobs need to run?

SKILL.md names no scripts, command-line tools or credentials: Python Background Jobs is instructions for the agent only. Our summary lists: Python 3.

Does Python Background Jobs access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Python Background Jobs safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Python Background Jobs use?

Python Background Jobs is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Python Background Jobs use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 858 tokens, read only when the agent opens those files.

What are the alternatives to Python Background Jobs?

Skills that share tags, products or a category with Python Background Jobs: Trigger.dev Configuration (papermark/papermark, 9.2k stars), K8e Sandbox (xiaods/k8e, 499 stars), Temporal Developer (temporalio/skill-temporal-developer, 230 stars) and Fba (Zhongye1/KnowAgenticRAG, 143 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Python Background Jobs?

wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,254 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.

Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.