Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nealcaren/social-data-analysis --skill dag-developmentgit clone --depth 1 https://github.com/nealcaren/social-data-analysisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nealcaren/social-data-analysis/dag-development)<a href="https://agentmods.dev/skills/nealcaren/social-data-analysis/dag-development"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/dag-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nealcaren/social-data-analysis/dag-development"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/dag-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00920 |
| Opus 5 | $0.00016 | $0.00460 |
| Sonnet 5 | $0.00007 | $0.00184 |
| Haiku 4.5 | $0.00003 | $0.00092 |
Grade A, and why
dag-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Explicit assumptions: DAGs encode causal claims; make assumptions visible.
- Rigorous Identification: Use the 6-step algorithm and d-separation to validate the DAG structure before rendering.
- Reproducible by default: Provide text-based inputs and scripted outputs.
- Exportable assets: Produce SVG/PNG (and PDF where possible).
- Tool choice: Offer three rendering paths with tradeoffs.
- 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
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- concepts/colliders.md 1.2 KB
- concepts/confounding.md 880 B
- concepts/d_separation.md 886 B
- concepts/potential_outcomes.md 1.2 KB
- concepts/selection_bias.md 1.0 KB
- concepts/six_step_algorithm.md 1.3 KB
- phases/phase0-theory.md 8.3 KB
- phases/phase1-identification.md 2.4 KB
- phases/phase2-inputs.md 1.7 KB
- phases/phase3-mermaid.md 716 B
- phases/phase4-r.md 694 B
- phases/phase5-python.md 1.1 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 110 lines · 33 tokens per session scan A 20e2cb0e8ad1
dag-development is a skill published in the GitHub repository nealcaren/social-data-analysis (84 stars, last pushed 9d ago), licensed MIT. It adds 33 tokens to every session and 920 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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