skills.rest
bettercallfan
bettercallfanCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

pipeline-reasoner

Analyze business DAG pipelines for type inference, missing modules, and logical anomalies.

Updated Jun 2, 2026
Search Tags:#business intelligence#DAG analysis#pipeline-reasoner#CloudLLMReasoner#logical diagnosis
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill pipeline-reasoner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipeline-reasoner
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/program_snippet/pipeline-reasoner
Command: npx skills add https://github.com/bettercallfan/deerflow --skill pipeline-reasoner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, and includes scripts (resource) components.

What problem does it solve?

This Skill diagnoses the pipeline type, missing modules, and logical anomalies in business DAGs, providing a clear JSON-formatted diagnosis result.

Core Features & Use Cases

  • Pipeline Type Inference: Determines the business classification of a smart city based on the DAG.
  • Missing Module Detection: Identifies missing key business modules in the pipeline.
  • Logic Anomaly Detection: Finds logical breaks, reverse dependencies, data flow disconnections, or sequence anomalies.
  • Use Case: For a city planning department, use this Skill to analyze and diagnose the flow of a city's traffic management system, ensuring a smooth and efficient operation.

Quick Start

Diagnose the business DAG pipeline of the attached file 'traffic_management_system.dag'.

Frequently Asked Questions about pipeline-reasoner

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

FAQPage Schema
How do I detect missing modules in a business DAG pipeline?▼

To detect missing modules in a business DAG pipeline, this Skill analyzes your directed acyclic graph to identify absent key business modules and returns a structured JSON diagnosis result.

What is business DAG type inference for smart city scenarios?▼

Business DAG type inference for smart city scenarios is the process of analyzing a directed acyclic graph structure to determine the specific business classification of a smart city pipeline.

How do I diagnose logical anomalies and reverse dependencies in a DAG?▼

To diagnose logical anomalies and reverse dependencies in a DAG, this Skill analyzes the pipeline structure to identify logical breaks, data flow disconnections, and sequence anomalies locally.

Can I analyze pipeline logic anomalies locally without external API dependencies?▼

Yes, you can analyze pipeline logic anomalies locally without external API dependencies because this Skill applies local inference mechanisms to evaluate your business DAG and detect sequence anomalies.

Does DAG analysis for missing modules require networkx?▼

DAG analysis for missing modules requires networkx as a dependency to process the directed acyclic graph structures and accurately infer the business pipeline type.

What is the best way to identify data flow disconnections in a traffic management system DAG?▼

The best way to identify data flow disconnections in a traffic management system DAG is to use this Skill's logic anomaly detection to analyze the pipeline and output a structured JSON diagnosis.

Related Skills

Explore More Software Engineering Skills→
bettercallfanbettercallfan

pipeline-graph-builder

Construct business-level DAGs from code analysis results using Python and NetworkX.

Community
Advanced
lyderhansenlyderhansen
4

splunk-dashboard-studio

Generate Splunk Dashboard Studio JSON definitions for interactive dashboards.

Community
Advanced
jasrajtulsijasrajtulsi

networkx

Build, analyze, and visualize graph data structures in Python with NetworkX.

Community
Advanced
cq112233cq112233
21

workflow-runner

Execute multi-agent YAML workflows by parsing task dependencies and role-based instructions.

Community
Advanced