Skills Development Guide

Skills Development Guide

This guide provides detailed information for developing skills in IntentKit.

Overview

Skills are in the intentkit/skills/ folder. Each folder is a category. Each skill category can contain multiple skills. A category can be a theme or a brand.

Dependency Rules

To avoid circular dependencies, Skills can only depend on the contents of models, abstracts, utils, and clients.

Skill Category Structure

The necessary elements in a skill category folder are as follows. For the paradigm of each element, you can refer to existing skills, such as skills/twitter:

1. Base Class (base.py)

Base class inherit IntentKitSkill. If there are functions that are common to this category, they can also be written in BaseClass. A common example is get_api_key.

2. Individual Skill Files

Each skill should have its own file, with the same name as the skill. Key points:

• Class Inheritance: The skill class inherit BaseClass created in base.py

• Name Attribute: The name attribute needs a same prefix as the category name, such as twitter_, for uniqueness in the system.

• Description Attribute: The description attribute is the description of the skill, which will be used in LLM to select the skill.

• Args Schema: The args_schema attribute is the pydantic model for the skill arguments.

• Main Logic (_arun method): The _arun method is the main logic of the skill.

  • There is special parameter config: RunnableConfig, which is used to pass the LangChain runnable config.
  • There is function context_from_config in IntentKitSkill, can be used to get the context from the runnable config.
  • In the _arun method, if there is any exception, just raise it, and the exception will be handled by the Agent.
  • If the return value is not a string, you can document it in the description attribute.

3. Initialization (__init__.py)

The __init__.py must have the function:

async def get_skills(
    config: "Config",
    is_private: bool,
    **_,
) -> list[OpenAIBaseTool]

• Config: Config is inherit from SkillConfig, and the states is a dict, key is the skill name, value is the skill state. If the skill category have any other config fields need agent creator to set, they can be added to Config.

• Caching: If the skill is stateless, you can add a global _cache for it, to avoid re-create the skill object every time.

• Availability Check: The __init__.py must also have the function:

def available() -> bool:
    """Check if this skill category is available based on system config."""

This function checks if all required system configuration variables exist. If the skill requires a platform-hosted API key (e.g., config.tavily_api_key), return whether that key is present. If the skill has no system config dependencies (e.g., only uses agent-owner provided keys), return True.

4. Visual Assets

A square image is needed in the category folder.

5. Configuration Schema (schema.json)

Add schema.json file for the config, since the Config inherit from SkillConfig, you can check examples in exists skill category to find out the pattern.

The x-tags in schema should be in this list: AI, Analytics, Audio, Communication, Crypto, DeFi, Developer Tools, Entertainment, Identity, Image, Infrastructure, Knowledge Base, NFT, Search, Social

Exception Handling

There is no need to catch exceptions in skills, because the agent has a dedicated module to catch skill exceptions. If you need to add more information to the exception, you can catch it and re-throw the appropriate exception.