> For the complete documentation index, see [llms.txt](/llms.txt).
> A full single-fetch corpus is available at [llms-full.txt](/llms-full.txt).
---
title: Build with AI coding agents
description: Install AGNT5 skills for your coding agent, then use copyable prompts to create, deploy, evaluate, or integrate workflows.
last_verified: 2026-06-07
category: Getting Started
---

Modern AGNT5 projects often start in an AI coding agent. This page gives Codex, Claude Code, Cursor, Windsurf, Cline, or Copilot AGNT5 skills for creating a **[workflow](/docs/build/workflows.md)**, running it with the **[CLI](/docs/install-cli.md)**, inspecting the **[trace](/docs/run/overview.md)**, and moving the result toward production. Use this path when you want the agent to do the setup work inside your repo while you review each file it changes.

## 1. Install AGNT5 skills

**AGNT5 skills** are project instructions plus task prompts for AI coding agents. The instruction file tells your coding agent how AGNT5 projects are structured, which commands to run, and how to handle secrets. Add the file that matches your tool, then start a new agent thread so the skills load.


  

  
### Codex

Add this to `AGENTS.md` in the target repo. Codex also reads nested `AGENTS.md` files, so place the file at the narrowest directory that owns the AGNT5 project.

```md
# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  

  
### Claude Code

Add this to `CLAUDE.md` or `.claude/CLAUDE.md` in the target repo.

```md
# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  

  
### Cursor

Create a project rule in `.cursor/rules/agnt5.mdc`, or create it from **Cursor Settings > Rules** and paste the body below.

```md
# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  

  
### Windsurf

Create a workspace rule in `.windsurf/rules/agnt5.md`.

```md
---
trigger: always_on
---

# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  

  
### Cline

Create a workspace rule in `.clinerules/agnt5.md`.

```md
# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  

  
### Copilot

Add this to `.github/copilot-instructions.md` in the target repo.

```md
# AGNT5 project instructions

- AGNT5 is a durable runtime and observability platform for agentic workflows.
- Use `agnt5 create --template <template> <project-name>` for new projects.
- Prefer the Python template unless the user asks for TypeScript.
- Run `agnt5 auth login` before cloud-backed CLI work.
- Keep secrets in `.env`; never commit `.env`.
- For model providers, use exactly one of `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
- Start local runs with `agnt5 dev`, then use the printed Studio URL to inspect runs and traces.
- After the quickstart works, propose deploy, evaluate, and integrate as the next tasks.
```
  


## 2. Give the agent a task

Pick the prompt that matches the work you want done. Each prompt tells the agent to use AGNT5 templates when they fit, ask before choosing a model provider, keep secrets out of git, and verify the result with `agnt5 dev` or the relevant docs path.


  

### Run the Hacker News quickstart

Create the digest workflow, configure Anthropic or OpenAI, start the dev server, and inspect the trace.

```text
Help me run the AGNT5 Hacker News digest quickstart in this repo.

Use Python unless I ask for TypeScript. Install the AGNT5 CLI if it is missing, authenticate with agnt5 auth login, scaffold the quickstart template, copy .env.example to .env, and ask whether I want Anthropic or OpenAI before editing the model provider key.

Start agnt5 dev, wait for the Studio URL, tell me how to run the digest workflow with {"limit": 5}, and verify that the trace shows fetch, summarize, and assemble steps. Do not commit .env.
```


  

### Build from a template

Start with the closest AGNT5 template, then adapt it to the app you describe.

```text
Help me choose and adapt an AGNT5 template for this application.

First ask me what event starts the workflow, what external systems it calls, whether a human approval step is required, and which model provider I want to use. Then choose the closest AGNT5 template, scaffold it with agnt5 create, and explain the files you changed.

Run the local worker with agnt5 dev. Keep secrets in .env, avoid committing .env, and leave me with the Studio URL and the exact input payload to test the first run.
```


  

### Add a webhook workflow

Create a workflow that starts from an external event source and records a trace.

```text
Add an AGNT5 workflow that starts from a webhook.

Use the AGNT5 webhook docs to design the handler, validate the incoming request, map the payload into a workflow input, and keep duplicate deliveries safe. Add a local test payload I can run. Start agnt5 dev and show me how to trigger the workflow from Studio or a local HTTP request.
```


  

### Add human approval

Insert a durable approval checkpoint before a side effect runs.

```text
Add human approval to this AGNT5 workflow.

Find the step that performs the side effect. Before that step runs, add an approval checkpoint that shows the proposed action and captures approve or reject. Make the workflow return a clear rejected result when approval is denied.

Run or describe the narrowest verification path, and point me to the trace node where the workflow pauses.
```


  

### Evaluate a workflow

Turn recent runs into a dataset, add scorers, and compare a prompt or model change.

```text
Create an AGNT5 evaluation plan for this workflow.

Inspect the workflow inputs, outputs, and traces. Propose a small dataset from recent or representative runs, define deterministic scorers first, and add an LLM-as-judge scorer only where deterministic checks are not enough.

Then make the smallest code or config changes needed to run the eval loop and compare one prompt or model change against the current behavior.
```


  

### Integrate an existing app

Connect an app event to AGNT5 and return a result the application can consume.

```text
Integrate AGNT5 into this existing application.

Find the application event that should start the workflow. Add the smallest integration layer that sends a structured workflow input to AGNT5, handles success and failure responses, and records the run ID for later trace lookup.

Keep the application boundary clear: app code starts the workflow, AGNT5 owns durable execution, and Studio owns run inspection.
```




## 3. Review the result

Treat the coding agent as the driver, not the reviewer. Before you keep the generated code:

1. Check that `.env` is ignored and no API key appears in git.
2. Run the command the agent used, usually **agnt5 dev** for local verification.
3. Open the Studio URL and confirm the trace has the expected workflow steps.
4. Read the changed files and remove unused scaffolding.
5. Ask the agent to summarize deploy, evaluate, and integrate follow-ups.

## Next steps



Once the agent has created a working workflow, move it through the same path as a manually built project.

- [Run the quickstart](/docs/quickstart): Build the Hacker News digest workflow and inspect its first trace.
- [Deploy the workflow](/docs/run/deploying): Set secrets, create an environment, and run the worker outside local dev.
- [Evaluate the output](/docs/improve/overview): Compare prompt and model changes with datasets and scorers.