Skaro: how one developer built a full-cycle AI orchestrator through sleepless nights

When AI coders like Cursor, Copilot, and Claude Code became everyday tools for thousands of developers, an unexpected paradox emerged. The more powerful language models become, the sharper the need—not for code generation itself, but for managing it wisely. It is precisely this problem that a Russian developer set out to solve, presenting on Habr the Skaro project—an AI orchestrator that promises to take control of the entire development cycle, from specification to finished code.

The history of Skaro began, as often happens in the open-source community, with a discussion in the comments. The project's author had previously published an article about his experience working with AI coding, where he shared practical approaches to code generation. Readers pointed him toward the SDD methodology—Specification-Driven Development, development through specifications. The idea resonated so strongly with his own experience that he literally built a working prototype of the tool implementing this philosophy in a matter of sleepless nights.

The concept of Skaro appears simple at first glance, but behind this simplicity lies a serious architectural challenge. The developer formulates specifications—clear descriptions of what the code should do, what structure it should have, and what requirements it should meet. Next, AI comes into play, generating an implementation based on these specifications. However, the key difference from ordinary prompting is that Skaro assumes the role of orchestrator: it ensures that context is not lost between iterations, that the project structure remains consistent, and that code quality does not degrade as the codebase grows. These are precisely the problems faced by anyone who has tried to build something serious with the help of AI assistants.

Context loss is perhaps the primary pain point of modern AI coding. Language models operate within a limited context window, and as a project expands, the model begins to "forget" previously made decisions, duplicate logic, and violate established patterns. Developers are forced to spend a significant portion of their time not on productive work, but on repeatedly explaining context to the model. Skaro attempts to solve this problem systemically, acting as an intermediary that stores and conveys relevant context to the model at every step.

This project fits into a broader trend gaining momentum in the development industry. More and more engineers are coming to the conclusion that the future of AI coding lies not in smarter models per se, but in the infrastructure around them. Tools for prompt management are emerging, systems for quality control of generated code, frameworks for task decomposition. Essentially, a new layer is forming in the development stack—an orchestration layer between humans and AI. Skaro is one of the early representatives of this direction in the Russian-speaking community.

It is important to note the methodological foundation of the project as well. SDD as a development approach assumes that specification is primary and implementation is secondary. This echoes classical practices like TDD (Test-Driven Development) and design by contract, but adapted to the realities of working with generative AI. When a model receives a clear specification instead of a vague description of a task, the quality of results is predictably higher. And when an automated orchestrator oversees adherence to specifications, the human factor in the form of forgotten context or inconsistent instructions is minimized.

Of course, Skaro is still in its early stages, and it is premature to judge its maturity. The project was created by a single developer in a hackathon mode, and ahead lies a long path from prototype to a reliable tool that can be trusted with production development. The question of the approach's scalability remains open: how well will the system handle truly large projects, where the number of specifications and their interdependencies is measured in the hundreds.

Nevertheless, the mere fact of the emergence of such projects speaks to an important shift in developers' thinking. The era of naive enthusiasm for AI coding, when it was enough to ask the model "write me an application," is giving way to a more mature approach. Developers are realizing that AI is a powerful but discipline-demanding tool, and they are beginning to build systems that provide that discipline. Skaro is a characteristic symptom of this maturation, and regardless of the fate of any particular project, the direction it represents will only grow stronger.