llms.txt is an open proposal for a short Markdown file that lists a project's key docs and links, so AI tools can find them without crawling every page.

Where should llms.txt be deployed?

At /llms.txt on your documentation site, or in your repository root. Tools read it when they fetch it; nothing loads it automatically.

# Bun & Elysia High-Performance TypeScript Specialist

> Production standards for Bun and Elysia.js. Enforces TypeBox runtime schemas, Eden Treaty client typing, and zero-overhead native Bun APIs.

## Quick Reference

- **Framework**: Elysia.js / Hono

- **Language**: TypeScript (Bun runtime)

- **Styling & UI**: None / API Service

- **Database / Persistence**: PostgreSQL / SQLite / Drizzle

- **Architectural Philosophy**: modern

## Core Architectural Invariants

- **Schema-Driven Boundaries**: Validate all external data (API, forms, env) with Zod or Pydantic schemas; on failure, reject with a typed error and never pass unvalidated data onward.

- **Guard Clauses & Early Returns**: Exit early from functions to eliminate deeply nested if-else pyramids.

- **Flat & Pragmatic Layout**: Keep directory nesting minimal (max 2-3 levels) to avoid navigation friction.

## Operational Guardrails & Prohibited Patterns

- **Inspect Before Acting**: Always read and understand existing code and imports before suggesting changes.

- **Minimal Surgical Diffs**: Touch only lines relevant to prompt. Preserve existing comments and formatting.

- **Concise & Direct Tone**: Skip conversational pleasantries. Deliver focused diffs and a short rationale.

## Mandatory Project Directives

- Do not import node:crypto or express middlewares when native Bun equivalents exist.

- Always annotate response types on public API endpoints.

## Implementation Reference

### Preferred Patterns

```typescript

import { Elysia, t } from "elysia";

export const app = new Elysia()

.post("/api/users", async ({ body, set }) => {

const hash = await Bun.password.hash(body.password);

set.status = 201;

return { ok: true, email: body.email };

}, {

body: t.Object({

email: t.String({ format: "email" }),

password: t.String({ minLength: 8 }),

}),

});

```

### Discouraged Anti-Patterns

```typescript

import bcrypt from "bcrypt";

app.post("/users", async (req: any, res: any) => {

const hash = await bcrypt.hash(req.body.password, 10);

res.json({ success: true });

});

```

## Verification Commands & Workflows

1. Validate request bodies, parameters, and query strings using Elysia's native 't' schema builder (TypeBox).

2. Leverage native Bun APIs (Bun.serve, Bun.file, Bun.password) instead of slow Node polyfills.

3. Define explicit Eden Treaty export contracts for type-safe frontend client consumption.

4. Centralize error handling using Elysia .onError handler with typed response envelopes.

## Context Documents & Specs

- [System Architecture](ARCHITECTURE.md): Authoritative system design, data flows, and state machine invariants.

- [AI Multi-Agent Spec](AGENTS.md): Universal specification for autonomous coding agent operations.

Backend · Any LLM tool that fetches it

Bun & Elysia / Hono llms.txt

An llms.txt for a Bun & Elysia / Hono project: a short Markdown map of what it is and where the key docs and code live.

Path: llms.txt
Loads: Fetched on request
Format: Markdown index (.txt)
Used by: Any LLM tool that fetches it

All llms.txt presets and how llms.txt works

What this file tells the agent

An llms.txt gives language models and agents a short Markdown map of your Bun & Elysia / Hono project: what it is and where the key docs and code live, so a tool that fetches it can go straight to the right files.

Use Elysia's native t schema builder (powered by TypeBox) for request body, query, and parameter validation.
Leverage native Bun.serve(), Bun.file(), and Bun.password instead of slow Node.js polyfills or external dependencies.
Structure plugins using .use() and define explicit Eden Treaty contracts for frontend consumption.
Handle application errors using Elysia .onError(({ code, error, set }) => ...) with typed error codes.
Ensure zero unhandled promise rejections and use strict type inference for all endpoint responses.

Code to avoid, and what to write instead

The rule steers the agent away from the left and toward the right.

avoid

// Discouraged: Node.js bcrypt imports, unvalidated body, and Express paradigms
import bcrypt from "bcrypt";
app.post("/users", async (req: any, res: any) => {
  const hash = await bcrypt.hash(req.body.password, 10);
  res.json({ success: true });
});

write instead

import { Elysia, t } from "elysia";

export const app = new Elysia()
  .onError(({ code, set }) => {
    if (code === "NOT_FOUND") {
      set.status = 404;
      return { ok: false, error: "Resource not found" };
    }
  })
  .post(
    "/api/users",
    async ({ body, set }) => {
      const hash = await Bun.password.hash(body.password);
      set.status = 201;
      return { ok: true, email: body.email };
    },
    {
      body: t.Object({
        email: t.String({ format: "email" }),
        password: t.String({ minLength: 8 }),
      }),
    }
  );

Install it in one command

01
Go to your project root
The file goes at llms.txt, relative to the root.
02
Download the file
Run the command on the right. It downloads this preset and writes the file.
03
Make sure it gets used
Nothing loads llms.txt automatically. Serve it at your site root, or point agents to it from CLAUDE.md or AGENTS.md.

$ curl -fsSL "https://www.devscratchpad.tech/api/raw/llms-txt/bun-elysia" -o "llms.txt"

writes llms.txt

path

llms.txt

To write the file from your own code instead, use Read your code or Convert Legacy Rules in the editor above.

More for Bun & Elysia / Hono

Other formats

Other llms.txt presets

Questions

What is llms.txt?: llms.txt is an open proposal for a short Markdown file that lists a project's key docs and links, so AI tools can find them without crawling every page.
Where should llms.txt be deployed?: At /llms.txt on your documentation site, or in your repository root. Tools read it when they fetch it; nothing loads it automatically.

Target Standard & File Format

Identity & Activation Rules

Technology Stack Context

Engineering Philosophy

Architectural & Code Quality Conventions

Agent Behavioral Guardrails

Step-by-Step Workflow Procedures

Target File Location