القائمة
Builds skills (capabilities) for a Rasa CALM assistant. Use when the user asks to create, add, or build a skill, capability, or feature for their Rasa assistant.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when a user wants to evaluate or test a Rasa assistant using the eval scenario framework — at any stage of the process: setting up the evaluation infrastructure for the first time, deciding which scenario types to cover for a flow, writing or generating scenario YAML files, learning about supported assertion types or how to write good success criteria, running goal-driven simulations where an LLM plays the user role against a live Rasa bot, or analyzing whether existing scenarios are comprehensive. Covers both the authoring side ("write scenarios", "generate evals", "help me write criteria") and the execution side ("simulate a conversation", "run my eval scenarios"). Not for scripted end-to-end tests, unit tests, NLU training, or general Rasa bot development.
Configures config.yml and endpoints.yml for Rasa CALM assistants. Covers pipeline (command generators, flow retrieval), policies (FlowPolicy), action endpoint, and language settings. Use when setting up a new project or modifying pipeline components.
Configures model_groups in endpoints.yml for LLM and embedding providers. Covers single and multi-deployment setups, routing strategies, failover, self-hosted models, and caching. Use when adding or changing LLM providers or setting up multi-LLM routing.
Enables and configures LLM-powered Contextual Response Rephraser in Rasa CALM. Covers endpoints.yml setup, per-response metadata, and prompt customization. Use when setting up or tuning the Contextual Response Rephraser.
Adds knowledge base search to a Rasa CALM assistant using EnterpriseSearchPolicy, connects vector stores (Faiss, Milvus, Qdrant), and configures generative or extractive search modes. Use when setting up RAG, connecting a vector store, overriding pattern_search, or implementing a custom information retriever.
Configures ReAct sub agents in a Rasa CALM assistant. Use when creating a sub-agent that dynamically selects MCP tools, choosing between general-purpose and task-specific agent types, customizing prompts, filtering tools, adding custom Python tools, or overriding input/output processing.
| name | rasa-building-skills |
| description | Builds skills (capabilities) for a Rasa CALM assistant. Use when the user asks to create, add, or build a skill, capability, or feature for their Rasa assistant. |
| license | Apache-2.0 |
| metadata | {"author":"rasa","version":"0.1.0","rasa_version":">=3.14.0"} |
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:
rasa-building-flowsrasa-setting-up-react-agentsrasa-setting-up-a2a-agentscall/link steps or
triggerable by their description).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.
Before implementing, outline:
rasa-managing-slots.rasa-building-flows.rasa-writing-responses.rasa-writing-custom-actions.rasa-setting-up-react-agents or
rasa-setting-up-a2a-agents.A single-flow skill can live in one file (e.g. data/track_order.yml). When a skill
contains multiple flows, group them in a subdirectory named after the skill:
data/
├── track_order.yml # single-flow skill
├── booking/ # multi-flow skill
│ ├── book_flight.yml
│ ├── select_destination.yml
│ └── choose_seats.yml
├── payments/
│ ├── process_payment.yml
│ └── verify_payment.yml
domain/
├── booking.yml
├── payments.yml
sub_agents/
├── policy_search/
│ └── config.yml
| Skill | Use for |
|---|---|
rasa-building-flows | Writing flows, branching logic, call/link steps |
rasa-managing-slots | Defining slots, types, mappings, persistence |
rasa-writing-responses | Bot responses and utter_ask_* templates |
rasa-writing-custom-actions | Custom actions and validation actions |
rasa-setting-up-react-agents | ReAct sub-agents with MCP tools |
rasa-setting-up-a2a-agents | External sub-agents via A2A protocol |
rasa-calling-mcp-tools-from-flows | Invoking MCP tools directly from flow steps |
rasa-configuring-mcp-server | MCP server setup in endpoints.yml |
rasa-configuring-assistant | Pipeline, policies, config.yml / endpoints.yml |
rasa-writing-e2e-tests | End-to-end conversation tests |
For worked examples (flight booking, stock research, payments, insurance claims, Jira), see references/examples.md.