SkillProvider

The skill system allows Twinkle Auto’s agent to dynamically load specialized knowledge from external sources (Git repos, APIs, local files) and inject them into the LLM’s system prompt.

Architecture

Class	Role
Skill	Dataclass holding a single skill's name, content, and source
SkillProvider	Abstract base class for fetching skills from a source
SkillManager	Orchestrates multiple providers, aggregates skills for prompt injection

Skill Dataclass

@dataclasses.dataclass
class Skill:
    name: str       # Short identifier (typically filename without extension)
    content: str    # Full markdown content
    source: str     # Provider name + relative path for traceability

Creating a Custom Provider

Subclass SkillProvider and implement name and fetch():

from twinkle_client.skills.base import SkillProvider

class MySkillProvider(SkillProvider):

    @property
    def name(self) -> str:
        return 'my-skills'

    async def fetch(self) -> None:
        # Download/clone skill files to self.cache_dir
        # e.g., git clone, API download, file copy
        ...

The default load_skills() scans self.cache_dir for .md files (skipping README, LICENSE, etc.) and returns Skill objects.

SkillManager

from twinkle_client.skills.manager import SkillManager

manager = SkillManager()
manager.register(my_provider)
manager.register(another_provider)

# Fetch and load all skills
skills = await manager.load_all()

# Format for LLM system prompt injection
prompt_section = manager.format_for_prompt()

Key Methods

Method	Description
`register(provider)`	Add a skill provider
`load_all()`	Fetch + load from all providers
`format_for_prompt()`	Render skills as formatted text for system prompt
`get_skill_names()`	List names of loaded skills

Cache Directory

By default, skills are cached at ~/.cache/twinkle/auto/skills/<provider_name>/. Override by passing cache_dir to the provider constructor.