rasa-building-skills

Building Skills for a Rasa Assistant

Terminology

In Rasa, a skill is a self-contained module inside a Rasa assistant that packages a specific capability or domain (e.g. booking, payments, order tracking).

The term "skill" can also refer to a IDE agent skill (SKILL.md). Use conversation context to determine which meaning the user intends. If ambiguous, ask.

A Rasa skill defines its interface (slots / memory) and logic (flows, prompts, actions, or tools). It is implemented as either:

1. Flow-based — a bundle of flows and slots for guided, deterministic behavior.
2. Sub-agent — a sub-agent that plans and acts autonomously using tools.
3. Hybrid — an orchestrator flow that delegates some steps to a sub-agent.

Workflow

1. Clarify what the user wants the Rasa assistant to do.
2. Choose the implementation approach (see "Choosing the approach").
3. Design the skill boundary — slots, flows, responses, actions, sub-agents (see "Designing the skill boundary").
4. Implement using the appropriate skill:
   • Flow-based → rasa-building-flows
   • ReAct sub-agent → rasa-setting-up-react-agents
   • A2A sub-agent → rasa-setting-up-a2a-agents
5. Ensure the new flows or sub-agents are reachable (via call/link steps or triggerable by their description).

Choosing the approach

The first design choice is how autonomous the skill should be:

if high-risk domain (payments, auth, KYC, PII, compliance):
    use flow-based
elif business logic must be tightly controlled (strict validation, fixed steps):
    use flow-based
elif most steps are deterministic but some need autonomy:
    use hybrid (flow orchestrates, sub-agent handles open-ended steps)
else:
    use sub-agent (start autonomous, add flows later for guardrails as needed)

If the approach is not obvious from context, ask the user whether they want more control over the conversation (flow-based) or a more autonomous experience (sub-agent). Frame it in terms of the trade-off: flows give predictability and auditability, sub-agents give flexibility and a more natural interaction. This is especially important when the user asks for multiple skills at once — each skill may warrant a different approach, so confirm the intent per skill before implementing.