dag-development
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.
npx skills add majiayu000/claude-skill-registry --skill dag-development --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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**:
- When to Use This Skill
- Core Principles
- Workflow Phases
- Phase 0: Theory → DAG Translation
- Phase 1: Critique & Identification
- Phase 2: Inputs & Format
- Phase 3: Mermaid Rendering
- Phase 4: R Rendering (ggdag)
- Phase 5: Python Rendering (networkx)
- Output Expectations
- Invoking Phase Agents
What does the dag-development skill do?
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill dag-development --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.