AI-assisted development

Why this matters

Coding agents (and new teammates) thrive on structure and fail on surprises. Slicekit is shaped so a model can land working features instead of guessing. None of this is AI-specific magic: the same properties that make the codebase easy for a model make it easy for a human.

What makes it AI-ready

• AGENTS.md routers. A root AGENTS.md points to per-side instruction files (api/AGENTS.md, frontend/AGENTS.md) that spell out the conventions for each half. These are read natively by Claude Code, Codex, Copilot, Cursor and others, so an agent loads the right context before it writes a line. Claude Code reads them via the CLAUDE.md import at the repo root.
• Predictable vertical slices. Every feature has the same shape in the same place. To add one, an agent copies a slice folder and renames it. There is no bespoke wiring to discover and no layer to thread a change through.
• Types as a safety net. Static typing on each side, TypeScript on the SPA and C# on the API, means a wrong guess fails at compile time within that codebase, not in production. The model gets fast, precise feedback instead of silent breakage.
• Tests as guardrails. Architecture tests fail the build when a slice reaches across a boundary, so autonomous edits stay inside the lines you set. Unit and integration tests catch the rest.
• LLM-clean docs. Every docs page is served as raw Markdown at /docs/<slug>.md (diagrams and components stripped out), so you can paste clean context into a model or pull it into a tool. The whole set is indexed at /llms.txt and concatenated into one file at /llms-full.txt, following the llms.txt convention.
• Explicit over clever. Named handlers, obvious folders and no hidden magic. Code is written to be read, by the next engineer or the next agent.

Getting the most from it

1. Point your agent at the slice you’re copying. “Add a feature like Features/ApiKeys/CreateApiKey for X” gives a model a concrete, working template to mirror.
2. Let the build and tests be the loop. Run dotnet test ... --nologo and pnpm typecheck so the agent’s mistakes surface immediately.
3. Feed it the raw docs. Link the relevant /docs/<slug>.md pages as context rather than pasting rendered HTML, or hand the agent /llms-full.txt for the whole set at once.

Using these docs with AI

The whole documentation set is published in the llms.txt format so an AI assistant can pull it in as context without scraping rendered HTML:

• /llms.txt is a curated index: the site summary plus a linked, sectioned list of every page, each pointing at its clean /docs/<slug>.md source. Use it when a tool can follow links, or when you want the model to pick the few pages it needs.
• /llms-full.txt is the entire documentation concatenated into one Markdown file. Use it when you want the model to have everything at once.

How to provide them, depending on the tool:

1. Chat assistants (ChatGPT, Claude, Gemini). Paste the URL and ask the model to read it, or download llms-full.txt and attach it as a file. A good opener: “Use this as the reference for the Slicekit template: https://slicekit.dev/llms-full.txt.”
2. Coding agents (Claude Code, Cursor, Copilot, Windsurf). Add the URL to the project’s context or rules file (for example a # Docs line in AGENTS.md/CLAUDE.md pointing at /llms.txt), or save llms-full.txt into the repo and reference it. Many agents will fetch the linked .md pages on demand from /llms.txt.
3. Retrieval / RAG pipelines. Ingest /llms-full.txt (or crawl the per-page .md routes listed in /llms.txt); both are plain Markdown with diagrams and components stripped, so chunking is clean.

Both files are regenerated on every build from the same docs you are reading, so they never drift.

A note on how Slicekit itself is built

Slicekit is built with AI in the loop, but never on autopilot. AI accelerates the typing; the architecture, the boundaries and the review are human, and the same tests above run on every change. That is the standard the template is designed to help you hold, too: AI-accelerated, engineer-owned.