code-business-dag-analysis-pipeline

Analyze Python code into a business-level DAG and generate Markdown reports with Mermaid visualizations.

Updated Jun 2, 2026

Search Tags:#code analysis #python #business intelligence #dag #pipeline diagnosis

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill code-business-dag-analysis-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: code-business-dag-analysis-pipeline
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/program_snippet/code-business-dag-analysis-pipeline
Command: npx skills add https://github.com/bettercallfan/deerflow --skill code-business-dag-analysis-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill analyzes Python code to generate a business-level Directed Acyclic Graph (DAG), diagnoses the pipeline logic, and generates comprehensive reports, enabling efficient code analysis and optimization.

Core Features & Use Cases

Code to DAG Analysis: Transforms Python code into a business-level DAG, identifying the relationship between code components and business processes.
Pipeline Logic Diagnosis: Diagnoses potential issues in the code's business pipeline, such as missing modules or logical anomalies.
Comprehensive Reporting: Generates detailed Markdown reports with Mermaid visualizations, providing insights into the code's structure and performance.

Quick Start

Analyze the business logic of a Python code snippet using the code-business-dag-analysis-pipeline skill.

Frequently Asked Questions about code-business-dag-analysis-pipeline

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

FAQPage Schema

How do I generate a business-level DAG from Python code?▼

To generate a business-level DAG from Python code, this Skill parses source files using ast and networkx to identify component relationships and map business processes into a Directed Acyclic Graph structure.

What is the best way to diagnose business pipeline logic in Python?▼

Diagnosing business pipeline logic in Python is achieved by analyzing the generated DAG to identify potential issues like missing modules or logical anomalies, providing insights into code structure and performance.

Can I visualize Python code analysis results using Mermaid?▼

Yes, you can visualize Python code analysis results using Mermaid. The Skill generates comprehensive Markdown reports that include Mermaid visualizations to represent the diagnosed business pipeline and DAG structure.

Do I need langchain to analyze Python code into a DAG?▼

Yes, you need langchain along with ast and networkx installed in your Python environment, as these libraries are required dependencies for automating the code analysis and DAG generation process.

Does this pipeline diagnosis approach work for automated code review?▼

Yes, this pipeline diagnosis approach works for automated code review. It transforms Python code into a DAG to diagnose logical anomalies and generate detailed reports, making it suitable for optimization and testing.