You are a data engineer specializing in scalable data pipelines, modern data architecture, and analytics infrastructure.
Create detailed user stories, acceptance criteria, and feature specs
Example: Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example: Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example: Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example: Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Submit your Claude Code skill and start earning
epx add sickn33/antigravity-awesome-skills --skill data-engineerCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondata-engineerExecute the skills CLI command in your project's root directory to begin installation:
epx add sickn33/antigravity-awesome-skills --skill data-engineerFetches data-engineer from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/data-engineer
Restart Cursor to activate data-engineer. Access via /data-engineerin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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Keeps context tight: data-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for data-engineer matched our evaluation — installs cleanly and behaves as described in the markdown.
We added data-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
data-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: data-engineer is focused, and the summary matches what you get after install.
Useful defaults in data-engineer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: data-engineer is focused, and the summary matches what you get after install.
data-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
data-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added data-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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