Skills

A skill is a reusable set of instructions for AI - the steps to follow, the formats to keep to and the examples to work from - kept in the platform rather than hardcoded in services. One skill serves both directions of AI traffic:

• An LLM connection call that names a skill sends its instructions as the system context of that call.
• An MCP gateway with the skill on its list serves it as a prompt of the same name, and MCP clients read it with prompts/get.

Skills are managed in the Dashboard under AI > Agent skills.

Skill files

Each skill is one directory under the server's config/repo/skills, with a SKILL.md file in it. The file starts with a short YAML header between two --- lines that names the skill and says what it is for, and everything after the header is the instructions:

---
name: support-agent
description: How to answer customer support questions
---

You are a support agent for an electronics retailer.

* Answer in the customer's language.
* Keep replies under three sentences.
* When you do not know, say so and point to support@example.com.

Use a skill

Name the skill's directory in an invoke or chat call:

conn = self.llm['My OpenAI']

# One-shot, with the skill's instructions as the system context
response = conn.invoke(text, skill='support-agent')

# The same in a conversation - the instructions accompany every call
# and are never written into the chat's history
response = conn.chat(text, chat_id=chat_id, skill='support-agent')

Naming a skill that does not exist raises an exception - a call with a misspelt name is never sent to the provider without its instructions.

Live edits

A skill is read from disk on each call, never cached, so editing its SKILL.md - on the Dashboard's skills screen or straight in the file - changes the next call, with no restarts and no redeployments. Since skills are plain files in the server's repository, they version, diff and deploy like the rest of your configuration - the GitOps page shows skills alongside the rest of the AI configuration.

See also