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missing_operator_stop

Inserts a placeholder node into a DAG when no matching operator exists.

539|171|Updated May 3, 2018
Search Tags:#pipeline#Data Flow#workflow planning#placeholder#DAG#Operator
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill missing-operator-stop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: missing_operator_stop
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/dag_system_node/missing_operator_stop
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill missing-operator-stop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

When planning a data-processing workflow, the required operator capability may not exist in the current Skill library, which would break the DAG structure. This placeholder operator keeps the DAG complete by bridging upstream outputs to downstream nodes until a real Skill is generated to replace it.

Core Features & Use Cases

  • DAG Placeholder Node: Declares a stand-in operator that accepts upstream output as input and exposes an output slot for downstream references.
  • Capability Declaration: Records the expected skill name, required capability description, and the reason the existing operators cannot satisfy the need.
  • Use Case: During workflow planning, a user requests a data-cleaning step that no existing Skill supports. The planner inserts this placeholder node so the pipeline structure stays valid while a real Skill is developed and swapped in later.

Quick Start

Insert a placeholder node into the DAG that takes the upstream output as input and describes the missing operator capability needed to complete the workflow.

Frequently Asked Questions about missing_operator_stop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I keep a DAG valid when a required operator is missing?▼

Insert a placeholder node that consumes the upstream output and exposes an output slot for downstream nodes. This preserves the DAG structure until a real Skill implementing the missing capability is generated and substituted.

What is a placeholder operator in workflow planning?▼

A placeholder operator is a stand-in DAG node used during workflow planning when the Skill library lacks the required capability. It records the expected skill name, capability description, and reason so a real operator can replace it later.

What inputs does the DAG placeholder node require?▼

The node requires an input referencing the upstream node output and a capability description of the missing operator. Optional parameters include the expected skill name and the reason existing operators cannot meet the requirement.

When should I not use a placeholder operator in a DAG?▼

Avoid it when a matching Skill already exists in the library, since a real operator should be used directly. The placeholder only makes sense during planning when the needed capability is genuinely absent and will be implemented later.

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