Agent-Skills.md

Agent Skills: DAG Development

Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.

UncategorizedID: nealcaren/social-data-analysis/dag-development

Repository

nealcaren/social-data-analysis
nealcarenLicense: MIT
445

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pnpm dlx add-skill https://github.com/nealcaren/social-data-analysis/tree/HEAD/plugins/dag-development/skills/dag-development

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plugins/dag-development/skills/dag-development/SKILL.md

Skill Metadata

Name
dag-development
Description
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.

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