Nodes

A Node represents a single step in a Workflow. Each node contains a natural language instruction that an AI model executes, with access to tools from declared Skills.

Fields

Field	Type	Required	Default	Description
name	string	REQUIRED	—	Display name for this node. MUST be non-empty.
instruction	string	REQUIRED	—	Natural language instruction for the AI model. MUST be non-empty.
skills	string[]	OPTIONAL	[]	Skill IDs this node has access to.
output	JSON Schema object	OPTIONAL	—	Structured output schema for this node’s result.

Instruction Semantics

The instruction field is the primary directive for the AI model at this step.

A conforming executor:

• MUST pass the instruction to the AI model as the primary directive.
• MUST NOT alter, summarize, or truncate the instruction.
• MAY augment the instruction with context from prior nodes (see Execution Model).

Input Augmentation

If the workflow input contains a rules field (string), a conforming executor MUST prepend it to the instruction with the heading:

## Rules — You MUST Follow These

{rules}

If the workflow input contains a context field (string), a conforming executor MUST prepend it with the heading:

## Background Context

{context}

When both are present, rules appear first, then context, then the node’s base instruction, separated by ---.

Skills Semantics

The skills array declares which Skills the node can access during execution.

A conforming executor:

• MUST resolve tools from the listed skill IDs and make them available to the AI model during node execution.
• MUST NOT make tools available from skills not listed in the node’s skills array.
• SHOULD silently skip skills that are not configured (missing required config values) rather than failing the workflow. The node executes with whatever tools are available from the remaining skills.

Output Schema Semantics

The output field declares a JSON Schema that the node’s result data MUST conform to.

A conforming executor:

• MUST request structured output from the AI model conforming to this schema when output is present.
• The structured output becomes the node’s result data, available to downstream nodes via context accumulation.
• When output is absent, the node’s result data is implementation-defined.

Examples

Minimal Node

gather:
  name: Gather Context
  instruction: Pull error details, logs, and recent commits related to the alert.
  skills:
    - github
    - sentry

Node with Structured Output

investigate:
  name: Root Cause Analysis
  instruction: >-
    Classify each issue as novel or duplicate. Assess severity and
    fix complexity for each finding.
  skills:
    - github
    - linear
  output:
    type: object
    properties:
      findings:
        type: array
        items:
          type: object
          properties:
            title: { type: string }
            severity: { type: string, enum: [critical, high, medium, low] }
            is_duplicate: { type: boolean }
            fix_complexity: { type: string, enum: [simple, moderate, complex] }
          required: [title, severity, is_duplicate]
      novel_count: { type: number }
      highest_severity: { type: string }
    required: [findings, novel_count, highest_severity]

Node with No Skills

A node with no skills has no tools. The AI model executes the instruction using only its training and the accumulated context.

summarize:
  name: Summarize Findings
  instruction: >-
    Produce a concise summary of all findings for the notification.
    Include severity, root cause, and links to created issues.