Content
# AI Dev Stack
<p align="center">
<img src="assets/hero.png" alt="AI Dev Stack — rules, docs, and patterns that make your AI coding tools build like a senior engineer" width="100%">
</p>
A production-grade kit of rules, docs, prompts, and patterns for AI-native development. It teaches your AI coding tools — **Cursor** and **Claude Code** — to think like a principal architect: pick the right layer, follow clean architecture, and ship deploy-ready code instead of demos. Drop it into any project and the assistant inherits a consistent operating model on the first prompt.
## Quick Start
### Cursor
```bash
curl -fsSL https://raw.githubusercontent.com/aiagentwithdhruv/ai-dev-stack/main/install.sh | bash
```
### Claude Code
```bash
curl -fsSL https://raw.githubusercontent.com/aiagentwithdhruv/ai-dev-stack/main/claude/CLAUDE.md -o CLAUDE.md
```
### Both (recommended)
```bash
curl -fsSL https://raw.githubusercontent.com/aiagentwithdhruv/ai-dev-stack/main/install.sh | bash
curl -fsSL https://raw.githubusercontent.com/aiagentwithdhruv/ai-dev-stack/main/claude/CLAUDE.md -o CLAUDE.md
```
## The mental model: substrate × two axes
Everything in the kit sits on one **substrate** and is organized along **two axes** — *how* you build and *what* you build. Reserved space (`_frontier/`) holds patterns that aren't stable yet.
```
ai-dev-stack/
├── foundations/ # SUBSTRATE — non-negotiables every build inherits
│ ├── rules/ # clean architecture, security, response style
│ ├── docs/ # PRD / ARCHITECTURE / API / SCHEMA / DEPLOY templates
│ ├── evals/ # measure before you trust — task + regression evals
│ ├── observability/ # tracing, cost, latency, structured logs
│ ├── guardrails/ # layered policy → input → output → monitor
│ └── prompts/ # reusable system + task prompt patterns
│
├── pillars/ # AXIS 1 — HOW you build
│ ├── software-development/ # backend, frontend, data, API contracts, DevOps
│ ├── agents/ # tools, schemas, orchestrator–worker, supervisor loops
│ └── automation/ # event/scheduled pipelines → companion repo below
│
├── domains/ # AXIS 2 — WHAT you build
│ ├── rag-knowledge/ # ingestion, chunking, retrieval, grounded answers
│ ├── data-analytics/ # NL-to-SQL, metrics, reporting, BI assistants
│ ├── voice/ # STT, TTS, real-time voice agents
│ ├── vision-doc-ai/ # OCR, document extraction, multimodal pipelines
│ ├── content-generation/ # long-form, structured, and media generation
│ └── decisioning-forecasting/# scoring, ranking, prediction, recommendations
│
└── _frontier/ # RESERVED — emerging patterns, not yet production-stable
```
**Read it as a grid.** Any project picks one or more **pillars** (the *how*) and one or more **domains** (the *what*), then stands the whole thing on **foundations**. A RAG support assistant is `pillars/agents` + `domains/rag-knowledge` on `foundations/{rules,evals,guardrails}`. A nightly report bot is `pillars/automation` + `domains/data-analytics`. The substrate never changes; the axes compose.
## The substrate — [foundations/](foundations/)
The defaults every build inherits, regardless of pillar or domain. Rules and doc templates tell the AI *how* to write code and *what* you're building; evals, observability, and guardrails keep it honest in production. Start here — see [foundations/](foundations/).
## Axis 1 — pillars (HOW you build)
| Pillar | What it covers |
|--------|----------------|
| [software-development/](pillars/software-development/) | Thin routes, services, repositories, typed API contracts, caching, CI/CD. |
| [agents/](pillars/agents/) | Tool schemas, validated outputs, orchestrator–worker and supervisor patterns. |
| [automation/](pillars/automation/) | Event-driven and scheduled pipelines — see the companion repo below. |
## Axis 2 — domains (WHAT you build)
| Domain | What it covers |
|--------|----------------|
| [rag-knowledge/](domains/rag-knowledge/) | Separate ingestion from generation; chunk metadata; grounded, cited answers. |
| [data-analytics/](domains/data-analytics/) | NL-to-SQL, read-only query agents, metrics, dashboards. |
| [voice/](domains/voice/) | Speech-to-text, text-to-speech, low-latency voice agents. |
| [vision-doc-ai/](domains/vision-doc-ai/) | OCR, document extraction, multimodal understanding. |
| [content-generation/](domains/content-generation/) | Long-form, structured, and media content with quality gates. |
| [decisioning-forecasting/](domains/decisioning-forecasting/) | Scoring, ranking, forecasting — classical models before LLMs for tabular data. |
## Companion repos
Focused repos that pair with this stack (kept separate so each stays searchable and reusable on its own):
- **[ai-automation-kit](https://github.com/aiagentwithdhruv/ai-automation-kit)** — n8n-first + general workflow-automation patterns. The `automation` pillar links out to it.
- **[skills](https://github.com/aiagentwithdhruv/skills)** — installable AI-agent skill packs (`npx skills add …`), cross-tool (Claude / Cursor / Copilot / Codex / Windsurf / Cline). The `foundations/` rules-and-skills layer links here.
- **[ghost-browser](https://github.com/aiagentwithdhruv/ghost-browser)** — AI-powered browser automation (web scraping, auto-posting); a runnable example that pairs with the `automation` pillar.
## Suggested GitHub Topics
`ai-agents` · `rag` · `llm` · `prompt-engineering` · `automation` · `mcp` · `llmops` · `evals` · `claude-code` · `cursor` · `ai-development`
## Contributing & changelog
- Adding a rule, prompt, or pattern? See [CONTRIBUTING.md](CONTRIBUTING.md) — including the generic, de-identified content rule.
- Version history lives in [CHANGELOG.md](CHANGELOG.md).
## License
MIT — use it, fork it, ship better code.
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