Aug 13: Context layer webinar
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  1. Feature Engine
  2. Development with LLMs

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.

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Getting started

  1. Run the chalk init agent-prompt command to add the prompt to your local repository.
  2. Provide specific context about your feature requirements.
  3. Review generated code for adherence to your team’s conventions.
  4. 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.

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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

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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.

SkillUse it for
writing-online-resolversWriting, refactoring, and debugging @online Python resolvers, including input and output signatures.
writing-static-chalkdfWriting @online(static=True) resolvers with chalkdf, and porting pandas or numpy logic to static acceleration.
chalk-resolver-accelerationReducing feature-pipeline latency, and deciding whether to accelerate a Python resolver or migrate it to inline expressions.
chalk-streamingWriting, testing, and tuning stream resolvers against Kafka, Kinesis, or PubSub.
migrating-features-to-chalkPorting existing features into Chalk from dbt models, Airflow or Prefect pipelines, and Spark jobs.
chalk-notebooksCreating, running, and debugging Chalk Notebooks, including the CLI and installed dependencies.

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