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Project Templates

Overview

When I first started working with AI coding assistants, I expected the experience to be completely different from collaborating with a team of human developers. I quickly realized it is not. On the contrary, working with AI coding assistants is remarkably similar to leading a human development team.

An AI assistant operates much like an extremely well-read junior developer. And I mean well-read! However, much like a human junior, it often struggles to apply that vast knowledge practically. It lacks innate creativity and the ability to connect the dots autonomously. It occasionally makes errors and illogical suggestions (we call these errors "hallucinations," though the term hardly makes them less disruptive).

To successfully and effectively employ AI coding agents, your role must shift toward that of a software architect and team lead. You will increasingly focus on high-level design and directing your agents, delegating the manual, craftsman aspects of coding to them.

So, how do these project templates support this new workflow? The code generator tool (repo) pre-populates an empty repository with scaffolding files, guidelines for human developers, and specific instructions for AI coding assistants like Claude Code.

1. Scaffolding as an immediate benefit The generated scaffolding gives developers a significant head start. It eliminates the need to manually create folder structures across various languages, write build files, or configure CI pipelines. By standardizing repository structures, build configurations, and CI pipelines, you ensure consistency across all your projects.

2. Unified guidelines for humans and AI The generated files provide a curated set of industry best practices—such as principles of Clean Architecture—tailored for developers,designers, and AI alike. Rather than overwhelming the team with an exhaustive rulebook, I selected only the most impactful guidelines. These guidelines are inherently readable for human developers, but they are also translated into customized skills and rules to effectively align your AI agents.

3. Enforcing Spec-Driven Development The folder structure deliberately omits a traditional docs/ folder. Typically, documentation is an afterthought, written only once coding is complete—if at all. Instead, I replaced it with a specs/ folder. While this might seem like mere semantics, it signifies a paradigm shift. Both the human guidelines and the AI instructions mandate that architectural decisions and specifications be written first, and code second.

4. Multi-level testing and Behavior-Driven Development (BDD) The templates enforce testing across multiple levels: unit, component, acceptance, and architecture checks. A test/ folder is included by default, fully integrated with a test target in the build files and a corresponding job in the CI pipeline. Crucially, the specs/ directory contains feature files written in Gherkin. The generator provides the framework for BDD testing, effectively turning your specifications into executable tests.

AI Assistant Configuration

Claude Code

Claude Code configuration cascades from committed, team-shared files to personal, uncommitted overrides:

  • CLAUDE.md (project root) — committed, shared instructions for the whole team
  • CLAUDE.local.md (project root, gitignored) — personal additions, loaded alongside CLAUDE.md
  • .claude/rules/ — committed, project-wide or path-scoped rules (extracted from specs/guidelines/)
  • .claude/settings.json — committed, shared tool and permission settings
  • .claude/settings.local.json (gitignored) — personal overrides
  • .mcp.json — committed, team-shared MCP server config. GitLab projects come with GitLab's official MCP server preconfigured (OAuth, no secret to set up — approve it once via /mcp); GitHub projects start with an empty config, since GitHub's MCP server is still limited-rollout — add it yourself with claude mcp add once available on your account.

See the Claude Code Memory Documentation for the full loading order and precedence rules.

GitHub Copilot

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