llms

llm-friendly entry points for ai coding assistants working with yaebal projects.

machine-readable docs

the site exposes two plain-text files for agents and search tools:

https://yaebal.pages.dev/llms.txt
https://yaebal.pages.dev/llms-full.txt

agent prompt

if you are using an assistant that accepts project instructions, give it this baseline before it writes yaebal code:

use yaebal as an esm typescript telegram bot api framework.
prefer createBot() from "yaebal" for app code.
context type flows through .derive(), .decorate(), .install(), and .extend().
use on("message:text") for typed text-message handlers.
use @yaebal/test for tests instead of hitting telegram in ci.

where an assistant should look first

// canonical docs entry points for agents
/docs/getting-started/
/docs/core/
/docs/context/
/docs/plugins/
/docs/api/
/docs/troubleshooting/
/docs/production/

rules that prevent hallucinations

• use createBot() from yaebal for application examples unless the docs specifically show @yaebal/core.
• use explicit .js extensions in local esm imports in generated projects.
• use bot.install(plugin()) for yaebal plugins, not raw use().
• do not invent callback-data formats; use callbackData().
• do not hit real telegram in tests; use @yaebal/test.
• check the generated bot api reference for method params and return types.