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Rails Jobs

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marckohlbrugge
rails-jobs

Apply best practices for Rails background jobs using simple orchestration, idempotency, and safe retries. Use when creating, refactoring, or debugging Active Job and queue-backed workflows.

Overview

Publishermarckohlbrugge
Repository37signals-skills
Skill namerails-jobs
Stars
724
Forks
56
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by marckohlbrugge on GitHub. Read the source before you install it.

Installation

Install the Rails Jobs AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/marckohlbrugge/37signals-skills/tree/main/skills/rails-jobs
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/marckohlbrugge/37signals-skills.git /tmp/37signals-skills
mkdir -p .claude/skills
cp -r /tmp/37signals-skills/skills/rails-jobs .claude/skills/rails-jobs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rails Jobs in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rails Jobs on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rails Jobs is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Rails Jobs

Use for background job design and review. Patterns from Campfire (Resque, 4 thin jobs) and Fizzy (Solid Queue, multi-queue).

Defaults

  • Keep jobs shallow; the job body is one line calling a model method: def perform(card) = card.notify_recipients.
  • Make jobs idempotent and safe to retry (e.g. rescue RecordNotUnique in the domain op).
  • Fail loudly on real errors; avoid silent rescue patterns.
  • Set ActiveJob::Base.enqueue_after_transaction_commit = true in app defaults — fixes job-before-data races at the root.
  • Prefer Solid Queue (database-backed) over Redis-backed queues for new apps; co-locate with Puma (SOLID_QUEUE_IN_PUMA) in small deployments.

Naming Convention

  • _later enqueues, plain name does the work, _now when you need an explicit synchronous twin:
ruby
def notify_recipients          # the work — public API the job calls
def notify_recipients_later   # enqueues NotifyRecipientsJob (often private, called from callbacks)
  • Capture enqueue-time context as keyword args (Mention::CreateJob.perform_later(self, mentioner: Current.user)) — don't rely on Current at perform time.
  • In multi-tenant apps, serialize tenant context into every job: prepend an ApplicationJob extension that captures Current.account at enqueue (as a GlobalID) and wraps perform_now in Current.with_account.

Good Patterns

  • Small job payloads; pass records (GlobalID) or IDs, never big object graphs.
  • Queues split by criticality (default, backend, webhooks) with explicit priority order in queue config.
  • Separate orchestration jobs from heavy processing jobs.
  • Stagger recurring jobs at odd minutes (12, 27, 50) to avoid synchronized load spikes.
  • Bulk enqueue: due.in_batches { |batch| ActiveJob.perform_all_later(batch.map { DeliverJob.new(it) }) }.
  • Crash-safe long iteration with ActiveJob::Continuable: step :dispatch + find_each(start: step.cursor) + step.advance! — essential for fan-out (webhooks, broadcasts, backfills).
  • Serialize per-owner work with limits_concurrency to: 1, key: ->(owner) { owner } (Solid Queue).
  • Two-tier async for high fan-out HTTP (web push): one job for the domain event, then an in-process thread pool for the HTTP calls. Resolve all AR data before posting to the pool; drop on RejectedExecutionError for backpressure.
  • For after_destroy_commit async work, snapshot needed associations in before_destroy — the parent rows may be gone when the job runs.

Recurring & Maintenance

  • Recurring tasks invoke plain model class methods (command: "MagicLink.cleanup"), not dedicated job classes.
  • Retention sweeps use delete_all on scopes (stale.delete_all) — skip callbacks on stale rows.
  • Schedule cleanup for finished queue jobs, expired tokens, old delivery records.
  • Prefer reset-on-use over cron resets: check-and-reset inside the domain method (spend calls reset_if_due), not a scheduled job.
  • Trim recurring schedules in beta/staging environments to essentials.

Error Handling Policy

  • Retry transient failures with retry_on ..., wait: :polynomially_longer (timeouts, DNS, Net::SMTPServerBusy).
  • Don't retry permanent failures: rescue, classify by error class/message, log at :info severity (it's expected — bad address, full mailbox), and move on. Keep job queue resources for work that can succeed.
  • Distinguish "destination failed" (record outcome, complete the job) from "our code raised" (mark errored, re-raise for retry) — see rails-webhooks.
  • Package error taxonomies as concerns and include them into framework jobs (ActionMailer::MailDeliveryJob.include SmtpDeliveryErrorHandling).

Testing

  • Don't unit-test trivial job classes; test async behavior from model tests with perform_enqueued_jobs(only: Mention::CreateJob) { ... } and assert_enqueued_with(job: ...).
  • Assert the observable outcome (mention created, email sent), not job internals.

Red Flags

  • Jobs with complex branching and business rules embedded directly.
  • Non-idempotent side effects without guards.
  • Retrying permanently invalid inputs.
  • Enqueueing jobs before required records are committed.
  • Reading Current.user/Current.account inside perform without serializing it at enqueue.
  • Per-item perform_later in a loop where perform_all_later or Continuable iteration fits.
  • Cron-style reset jobs when reset-on-use logic is simpler and safer.
  • Background jobs triggering spurious Turbo broadcasts (wrap in suppressing_turbo_broadcasts).

Frequently asked questions

What does the Rails Jobs AI skill do?

Apply best practices for Rails background jobs using simple orchestration, idempotency, and safe retries. Use when creating, refactoring, or debugging Active Job and queue-backed workflows.

Why use Rails Jobs on TypingMind?

Because you install it once and use it with any model. Rails Jobs is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rails Jobs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marckohlbrugge/37signals-skills/tree/main/skills/rails-jobs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rails Jobs?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rails Jobs?

As many as you like. As long as a model supports skills, you can use Rails Jobs with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rails Jobs AI skill free?

It is published on GitHub by marckohlbrugge. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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