How do I install the Ddia Data Ethics skill?

Run npx skillmds@latest add ebarti/ddia-data-ethics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ddia Data Ethics safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ddia Data Ethics?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ddia Data Ethics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ddia Data Ethics?

ebarti (@ebarti) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ddia Data Ethics by ebarti — Agent Skill

Name: Ddia Data Ethics
Author: ebarti

Ddia Data Ethics

Ethical and societal frameworks for designing data-intensive systems, distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapter 14. Covers algorithmic accountability, bias, surveillance, consent, and the data-as-liability mindset — normative guidance, not pure engineering technique. Use this skill when: - Building or reviewing ML decision systems (credit, hiring, criminal justice) - Designing systems handling personal data - Implementing GDPR/CCPA right-to-erasure - Reviewing surveillance/tracking features - Auditing for algorithmic bias - Architecting consent flows - Making product decisions involving user data

ebarti c87057e 9 files · 53.5 KB Updated Sep 17, 2026

File contents

DDIA Data Ethics

This skill encodes the normative side of data engineering: what we should and should not build, separate from what we can build. It draws from chapter 14 of "Designing Data-Intensive Applications" (Kleppmann, 2nd ed). Where most of DDIA gives mechanical guidance ("use this index for that workload"), this material asks harder questions about the human consequences of automated decisions and large-scale data collection.

Treat the contents as a checklist of ethical concerns to surface during design, review, and product decisions — not a compliance script. Many recommendations here will create friction with growth, monetization, or simplicity goals; that friction is the point.

Quick Start

Read guidelines.md to find the right reference file for your situation
Load only the files relevant to your task — do not load the whole skill
Apply the framing as a review lens; surface tradeoffs explicitly, do not silently bypass them

References

Category	Files	Purpose
`references/predictive-analytics-bias/`	knowledge.md, rules.md, examples.md	Algorithmic decision-making, bias sources, feedback loops, accountability
`references/privacy-and-tracking/`	knowledge.md, rules.md, examples.md	Surveillance, consent, data minimization, right-to-erasure, data-as-liability

Workflows

Task	Workflow
Review an ML decision system or data-handling feature for ethics & privacy	`workflows/ethics-review-checklist.md`

Guidelines

See guidelines.md for:

Task-based file selection (ML review, privacy engineering, compliance)
Symptom/question lookup ("our model rejects more applicants from group X")
Topic-based browsing
Decision tree for the two main review paths

---name: ddia-data-ethicsdescription: Ethical and societal frameworks for designing data-intensive systems, distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapter 14. Covers algorithmic accountability, bias, surveillance, consent, and the data-as-liability mindset — normative guidance, not pure engineering technique. Use this skill when: - Building or reviewing ML decision systems (credit, hiring, criminal justice) - Designing systems handling personal data - Implementing GDPR/CCPA right-to-erasure - Reviewing surveillance/tracking features - Auditing for algorithmic bias - Architecting consent flows - Making product decisions involving user data---# DDIA Data EthicsThis skill encodes the *normative* side of data engineering: what we *should* and *should not* build, separate from what we *can* build. It draws from chapter 14 of "Designing Data-Intensive Applications" (Kleppmann, 2nd ed). Where most of DDIA gives mechanical guidance ("use this index for that workload"), this material asks harder questions about the human consequences of automated decisions and large-scale data collection.Treat the contents as a checklist of ethical concerns to surface during design, review, and product decisions — not a compliance script. Many recommendations here will create friction with growth, monetization, or simplicity goals; that friction is the point.## Quick Start1. Read `guidelines.md` to find the right reference file for your situation2. Load only the files relevant to your task — do not load the whole skill3. Apply the framing as a review lens; surface tradeoffs explicitly, do not silently bypass them## Contents### References| Category | Files | Purpose ||----------|-------|---------|| `references/predictive-analytics-bias/` | knowledge.md, rules.md, examples.md | Algorithmic decision-making, bias sources, feedback loops, accountability || `references/privacy-and-tracking/` | knowledge.md, rules.md, examples.md | Surveillance, consent, data minimization, right-to-erasure, data-as-liability |### Workflows| Task | Workflow ||------|----------|| Review an ML decision system or data-handling feature for ethics & privacy | `workflows/ethics-review-checklist.md` |## GuidelinesSee `guidelines.md` for:- Task-based file selection (ML review, privacy engineering, compliance)- Symptom/question lookup ("our model rejects more applicants from group X")- Topic-based browsing- Decision tree for the two main review paths

Frequently asked questions