Skills Overview

Skills are JavaScript functions that extend the AI agent's capabilities. Once deployed, the AI agent can call them as tools during conversations.

Based on the official Skills documentation: Skills Overview - Cloodot.

What skills do

Custom JavaScript or TypeScript functions let the AI agent:

• Fetch live data to answer with up-to-date information
• Run operations that change state in your systems

Skill kinds

Every skill is one of two kinds:

Search

Retrieve and return data for the AI to use in its answer.

Examples:

• Look up order status
• Check inventory
• Search an external catalog

Characteristics:

• Read-only lookups
• Return data for the AI to present
• No external state changes

Execute

Perform an operation, often with side effects.

Examples:

• Create a calendar event
• Send an email
• Update a CRM record
• Process an order

Characteristics:

• May change external systems
• Often require configuration/credentials
• Return a confirmation or result

Tool call flow

When a user requests something the AI agent can handle with a skill, this is what happens:

1. Tool discovery — the AI agent reads the parameters and descriptions of available skills.
2. Parameter extraction — it pulls relevant values from the conversation and maps them to skill parameters.
3. Skill invocation — the platform runs the skill in an isolated sandbox with those parameters.
4. Response processing — Cloodot validates and formats the output; errors are captured and returned.
5. Context integration — the result flows back into the conversation and the AI agent replies to the customer.

When skills are called

Skills run in these cases:

• User request — the customer explicitly asks ("book a meeting", "check inventory")
• AI agent decision — the AI agent decides invoking a skill would resolve the conversation
• UI interaction — the customer taps a skill-driven button or carousel
• Future — scheduled or event-driven runs (planned)

Skill context

When a skill executes, its context is a flat object with details about the conversation and organization. This is the complete set of keys — there are no aliases, so read exactly these names:

{
  "organizationId": "org_12345",
  "organizationName": "Acme Inc",
  "organizationTimezone": "America/New_York",
  "conversationId": "conv_12345",
  "messageId": "msg_12345",
  "channelId": "chan_12345",
  "channelType": "WHATSAPP",
  "channelName": "Acme Support",
  "channelReference": "+15551234567",
  "contactId": "contact_12345",
  "contactName": "Jane Doe",
  "contactEmail": "jane@example.com",
  "contactPhone": "+15557654321",
  "profileName": "Jane",
  "lastMessage": "Where is my order?",
  "messages": [
    { "from": "user", "text": "Where is my order?" },
    { "from": "assistant", "text": "Let me check that for you." }
  ],
  "collectionItems": [
    {
      "ref": "8123456789",
      "title": "Brass Kettle 2L",
      "subtitle": "From AED 129 · Acme",
      "url": "https://acme.example/products/brass-kettle"
    }
  ]
}

collectionItems — what the customer is looking at

When the AI agent shows products from a collection, the items it displayed are handed to your skill on the next turn. That is how a skill acts on "the blue one" or "the second one" without asking the customer to re-type a product name they can see on their screen.

{ "ref": "8123456789", "title": "Brass Kettle 2L", "subtitle": "From AED 129", "url": "https://…" }

ref is the item's ID in whatever system fed the collection — the Shopify product ID for a synced store, your own record ID for a custom feed, the internal item ID for items added by hand. Pass it straight back to that system.

Two rules make this reliable:

• Parameters win. When the agent passes a product reference as a parameter, use it. Fall back to collectionItems only when the parameter is missing — the agent may have searched again in the same turn, and that search is not in collectionItems until the turn is saved.
• Never pick for the customer. One item in the list means "that one". Two or more, with nothing to tell them apart, means the honest answer is a question.

Use context to personalize responses. Fields populate when available — some may be empty depending on the channel.

Skill definition fields

A skill definition includes:

• slug — unique identifier (alphanumeric + underscores)
• name — display name
• description — what the skill does
• prompt — how to describe it to the AI agent
• definition — JavaScript or TypeScript code with a handler function
• parameters — input schema the AI agent passes in
• response — output schema returned to the AI agent
• buttons — optional quick-action buttons

Handler function

All skills require a handler function:

async function handler(input) {
  const { config, secrets, parameters, context } = input
  
  // Your skill logic here
  
  return {
    message: "Response to AI",
    // ... other fields
  }
}

Input structure

{
  config: Record<string, any>           // Non-sensitive configuration values
  secrets: Record<string, any>          // Config fields marked sensitive (API keys, tokens)
  parameters: Record<string, any>       // Input from AI tool call
  context: {
    conversationId?: string
    organizationId?: string
    // ... see "Skill context" above
  }
}

Further reading

Build and deploy

• Creating Skills — build your first skill step by step
• Skill Schema — definition format and validation rules
• Skill Sets — group skills into reusable bundles
• AI Agent Configuration — wire custom skills into the AI agent
• MCP Servers — connect skills to external services via MCP

Related features

• Knowledge Base — ground skill responses in your knowledge base
• Custom Fields — use custom fields in skill parameters and responses
• Integrations — connect skills to external APIs and services

Next steps