# Development with LLMs
source: https://docs.chalk.ai/docs/development-with-llms

## Write Chalk code with LLMs using Chalk's agent prompts and skills.

Chalk provides specialized prompts to help Large Language Models (LLMs) write effective Chalk code.
These prompts are designed to guide AI assistants in understanding Chalk's patterns, best practices,
and API conventions.

### Getting started

- Run the chalk init agent-prompt command to add the prompt to your local repository.
- Provide specific context about your feature requirements.
- Review generated code for adherence to your team's conventions.
- Test the generated features in your development environment.

These prompts encode Chalk's conventions, so an assistant working from them produces more
idiomatic Chalk code.

### Agent prompts repository

The Chalk team maintains the public repository chalk-ai/agent-prompts
with prompts specifically designed for LLM-assisted Chalk development:

This repository contains prompts that are tested against LLM providers to help them understand:

- Chalk's feature definition patterns
- Built-in LLM integration capabilities
- Resolver implementation best practices
- Data source integration patterns
- Template interpolation syntax
- Model selection and configuration

### Agent skills

Chalk publishes six agent skills for specific tasks, alongside the general-purpose prompt above.

Install one with chalk install agent-skill:

```
chalk install agent-skill chalk-notebooks --claude
```

That writes the skill to .claude/skills/ in the current directory. Pass --global to install it
at the user level instead, so it applies in every project. The same command serves other agents
with --codex or --cursor, and --delete removes a skill you no longer want.

| Skill                         | Use it for                                                                                                                   |
| ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `writing-online-resolvers`    | Writing, refactoring, and debugging `@online` Python resolvers, including input and output signatures.                       |
| `writing-static-chalkdf`      | Writing `@online(static=True)` resolvers with chalkdf, and porting pandas or numpy logic to static acceleration.             |
| `chalk-resolver-acceleration` | Reducing feature-pipeline latency, and deciding whether to accelerate a Python resolver or migrate it to inline expressions. |
| `chalk-streaming`             | Writing, testing, and tuning stream resolvers against Kafka, Kinesis, or PubSub.                                             |
| `migrating-features-to-chalk` | Porting existing features into Chalk from dbt models, Airflow or Prefect pipelines, and Spark jobs.                          |
| `chalk-notebooks`             | Creating, running, and debugging [Chalk Notebooks](/docs/chalk-notebooks), including the CLI and installed dependencies.     |

Skills live alongside the prompts in that repository, under skills/.