majiayu000/claude-skill-registry-data/dag-development

DAG Development

You help users develop causal diagrams (DAGs) from their research questions, theory, or core paper, and then render them as clean, publication-ready figures using Mermaid, R (ggdag), or Python (networkx). This skill spans conceptual translation and technical rendering.

When to Use This Skill

Use this skill when users want to:

• Translate a research question or paper into a DAG
• Clarify mechanisms, confounders, and selection/measurement structures
• Turn a DAG into a figure for papers or slides
• Choose a rendering stack (Mermaid vs R vs Python)
• Export SVG/PNG/PDF consistently

Core Principles

1. Explicit assumptions: DAGs encode causal claims; make assumptions visible.
2. Rigorous Identification: Use the 6-step algorithm and d-separation to validate the DAG structure before rendering.
3. Reproducible by default: Provide text-based inputs and scripted outputs.
4. Exportable assets: Produce SVG/PNG (and PDF where possible).
5. Tool choice: Offer three rendering paths with tradeoffs.
6. Minimal styling: Keep figures simple and journal‑friendly.

Workflow Phases

Phase 0: Theory → DAG Translation

Goal: Help users turn their current thinking or a core paper into a DAG Blueprint.

• Clarify the causal question and unit of analysis
• Translate narratives/mechanisms into nodes and edges
• Record assumptions and uncertain edges

Guide: phases/phase0-theory.md Concepts: confounding.md, potential_outcomes.md

Pause: Confirm the DAG blueprint before auditing.

Phase 1: Critique & Identification

Goal: Validate the DAG blueprint using formal rules (Shrier & Platt, Greenland).

• Run the 6-step algorithm (Check descendants, non-ancestors).
• Check for Collider-Stratification Bias.
• Identify the Sufficient Adjustment Set.
• Detect threats from unobserved variables.

Guide: phases/phase1-identification.md Concepts: six_step_algorithm.md, d_separation.md, colliders.md, selection_bias.md

Pause: Confirm the "Validated DAG" (nodes + edges + adjustment strategy) before formatting.

Phase 2: Inputs & Format

Goal: Turn the Validated DAG into render‑ready inputs.

• Finalize node list, edge list, and node types (Exposure, Outcome, Latent, Selection).
• Choose output formats (SVG/PNG/PDF) and layout.

Guide: phases/phase2-inputs.md

Pause: Confirm the DAG inputs and output target before rendering.

Phase 3: Mermaid Rendering

Goal: Render a DAG quickly from Markdown using Mermaid CLI.

Guide: phases/phase3-mermaid.md

Pause: Confirm Mermaid output or move to R/Python.

Phase 4: R Rendering (ggdag)

Goal: Render a DAG using R with ggdag for publication‑quality plots.

Guide: phases/phase4-r.md

Pause: Confirm R output or move to Python.

Phase 5: Python Rendering (networkx)

Goal: Render a DAG using Python with uv inline dependencies.

Guide: phases/phase5-python.md

Output Expectations

Provide:

• A DAG Blueprint (Phase 0)
• An Identification Memo (Phase 1)
• A DAG source file (Mermaid .mmd, R .R, or Python .py)
• Rendered figure(s) in SVG/PNG (and PDF when available)

Invoking Phase Agents

Use the Task tool for each phase:

Task: Phase 3 Mermaid
subagent_type: general-purpose
model: sonnet
prompt: Read phases/phase3-mermaid.md and render the user’s DAG