$ open source · GPL-3.0 — need a commercial license? imqueue.com →
// AGENT RECIPES

Agent recipes for @imqueue codebases

Machine-oriented, verifiable procedures an AI coding assistant can follow to make a change in an @imqueue codebase — each with the facts it relies on, the commands to prove it worked, and the ways it goes wrong.

An agent recipe is written for a machine, not a reader. Where a blog article explains why an approach works and what the trade-offs feel like in production, a recipe states the contract precisely, gives the exact commands, and lists the failure modes an agent should expect — so an assistant can apply it to a codebase without inferring the parts nobody wrote down.

Each recipe follows the same shape:

  • When to apply this recipe — the trigger, so an agent can rule it out fast.
  • Facts these recipes rely on — the API contracts the steps depend on, stated explicitly rather than left to be guessed from a signature.
  • The recipes themselves — numbered, copy-pasteable procedures.
  • Verify — commands that prove the change actually took effect.
  • Failure modes — what breaks, what it looks like, and what to do instead.

Available recipes

  • Delayed & scheduled work — implementing "run this later": choosing between a delayed call, @imqueue/job and an external scheduler, the trailing-argument IMQDelay contract, self-re-arming recurrence, and the accuracy and cancellation limits to plan for.
  • Isolated imq CLI environments — running several @imqueue projects on one machine without collisions, using IMQ_CLI_HOME for a dedicated CLI home per fleet, plus disposable sandboxes for CI.

Why these pages are not in search results

Each recipe deliberately covers the same ground as a human-facing article, so the recipes carry noindex to keep the two from competing for the same queries. The narrative versions are the ones written to be read and the ones that rank: delayed and scheduled work and one isolated imq CLI home per project.

Staying out of the search index does not make the recipes any less available to an agent. They are listed in /llms.txt, every page has a plain-markdown mirror at <page-url>index.md, and the @imqueue MCP server's get_doc tool fetches them directly.

Getting an assistant set up

If you are pointing an AI assistant at @imqueue for the first time, start with using AI coding assistants with @imqueue for a paste-ready context block, or install the MCP server so your agent can search the docs and scaffold services itself.

// FROM THE BLOG