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dag-development

Translate research questions into explicit DAGs and render publication figures.

76|9|Updated Jan 17, 2026
Search Tags:#mermaid#dag#data-visualization#networkx#dags#causal-diagrams#ggdag
One-click install
npx skills add https://github.com/nealcaren/social-data-analysis --skill dag-development
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dag-development
Source: https://github.com/nealcaren/social-data-analysis/tree/main/plugins/dag-development/skills/dag-development
Command: npx skills add https://github.com/nealcaren/social-data-analysis --skill dag-development

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers convert their research questions and literature into explicit causal diagrams (DAGs) and render publication-ready figures using Mermaid, R, or Python, ensuring transparent causal assumptions and reproducible visuals.

Core Features & Use Cases

  • DAG translation: turn theory or core papers into explicit DAGs with nodes and edges.
  • Phase-driven workflow: supports the full DAG lifecycle from Phase 0 theory to Phase 5 rendering (Mermaid, R, Python).
  • Publication-ready visuals: export clean diagrams in SVG/PNG/PDF for papers, slides, or appendices.
  • Use Case: a social-science researcher translates a theory into a DAG, audits it for backdoor paths, then renders figures for a manuscript.

Quick Start

Provide a Phase 0 DAG blueprint by translating your causal question into nodes and edges, then render phase outputs using Mermaid, R, or Python.

Frequently Asked Questions about dag-development

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

FAQPage Schema
How do I translate research questions into explicit causal DAGs for publication?▼

To translate research questions into causal DAGs, articulate your theory into nodes and edges to create a Phase 0 DAG blueprint, then render publication-ready visuals using Mermaid, R, or Python.

What is the best way to render publication-ready causal diagrams from literature?▼

The best way to render publication-ready causal diagrams is using a phase-driven workflow that guides theory through to final rendering in Mermaid, R, or Python, outputting clean SVG, PNG, or PDF figures.

Can I use Mermaid and ggdag to visualize backdoor paths in social-science research?▼

Yes, you can visualize backdoor paths and audit causal assumptions by translating social-science theory into explicit DAGs, then rendering the diagrams using Mermaid, Python, or R packages like ggdag.

Does this DAG workflow support NetworkX and Python for generating identification memos?▼

Yes, the DAG workflow supports Python and NetworkX to generate an identification memo and source files, auditing causal assumptions and clarifying backdoor paths alongside the visual rendering outputs.

How do I export clean DAG figures in SVG or PDF for academic manuscripts?▼

You export clean DAG figures in SVG, PNG, or PDF by completing the phase-driven lifecycle from theory to Phase 5 rendering, generating source files in Mermaid, R, or Python for academic manuscripts.

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