8 posts tagged with "Hydra"

In part 5 you started Hydra by hand: build the images, run the supervisor, label an issue. That is the right way to learn Hydra, but it is not how it runs day to day. In production nobody runs hydra-supervisor.sh from a terminal. Instead the forge itself triggers the pipeline when an issue is labelled, pulls pre-built images from a registry, and runs each stage as a CI job. This part explains that path, and the three deployment models behind it.

Spec-driven development inverts the usual order. You don't sketch the feature, write the code, then maybe document what you built. You write the specification first — in Markdown, with RFC 2119 keywords and GIVEN/WHEN/THEN scenarios — and an AI agent (Hydra) implements code that satisfies it. The human's job moves up a level: you develop context, not code.

That sounds idealistic until you see it work. Conduction's apps are built this way in production today. This tutorial walks the workflow: what OpenSpec actually is, how ADRs at organisation and app level keep features coherent, what the explore and apply skills do, and how the quality-and-gatekeeping harness validates the result before anything reaches main.

Hydra is Conduction's internal agentic CI/CD platform: a factory that turns OpenSpec proposals into reviewed, tested code on a feature branch. This module explains in ten minutes what Hydra is, why it exists, and how it fits inside our app factory. It is the first part of a six-part series; by the end you're ready for part 2 on the three pipelines.

In part 1 you saw that Hydra has four personas. In this part we look at the pipeline: how those personas work on one issue one after another, which labels mark the transitions, and when the pipeline diverts to needs-input. By the end you'll know Hydra's label state machine inside out and you'll be able to steer an issue back to the right phase if it gets stuck somewhere.

In part 5 the pipeline was running. Now the final part: what do you do when it doesn't go green? This part gives you the decision tree — ordered from cheapest intervention to most expensive — and the patterns that get needs-input issues moving again.

The previous parts were about what Hydra does. This part is about how: the skills that let the personas do their job. By the end you'll know which skills the automated pipeline (the "Hydra factory") runs and which ones you call yourself as a human, you'll know the five families, and you'll be able to judge when a new skill is worth writing.

In part 2 you saw that the three personas are backed up by mechanical quality gates — checks that pass or fail deterministically, without AI in the loop. This part explains which gates we have, why they are mechanical, and how to handle the exception: the false positive.

The first four parts were about concept, pipelines, gates and skills. Time to actually run. In this part you start a full Hydra run on a target app, paying attention to the practical pitfalls: tokens, images, and — if you work with multiple devs against the same repos — the HYDRA_LABEL_PREFIX trick.