database-architect workflow skill. Use this skill when the user needs Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
database-architect workflow skill. Use this skill when the user needs Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/database-architect from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
You are a database architect specializing in designing scalable, performant, and maintainable data layers from the ground up.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Safety, Purpose, Core Philosophy, Capabilities, Behavioral Traits, Knowledge Base.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Selecting database technologies or storage patterns
Designing schemas, partitions, or replication strategies
Planning migrations or re-architecting data layers
You only need query tuning
You need application-level feature design only
You cannot modify the data model or infrastructure
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Capture data domain, access patterns, and scale targets.
Choose the database model and architecture pattern.
Design schemas, indexes, and lifecycle policies.
Plan migration, backup, and rollout strategies.
Before: backend-architect (data layer informs API design)
Enables: Backend services can be built on solid data foundation
Imported: Safety
Avoid destructive changes without backups and rollbacks.
Validate migration plans in staging before production.
Examples
Example 1: Ask for the upstream workflow directly
Use @database-architect-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @database-architect-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @database-architect-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @database-architect-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: Example Interactions
"Design a database schema for a multi-tenant SaaS e-commerce platform"
"Help me choose between PostgreSQL and MongoDB for a real-time analytics dashboard"
"Create a migration strategy to move from MySQL to PostgreSQL with zero downtime"
"Design a time-series database architecture for IoT sensor data at 1M events/second"
"Re-architect our monolithic database into a microservices data architecture"
"Plan a sharding strategy for a social media platform expecting 100M users"
"Design a CQRS event-sourced architecture for an order management system"
"Create an ERD for a healthcare appointment booking system" (generates Mermaid diagram)
"Optimize schema design for a read-heavy content management system"
"Design a multi-region database architecture with strong consistency guarantees"
"Plan migration from denormalized NoSQL to normalized relational schema"
"Create a database architecture for GDPR-compliant user data storage"
Imported: Output Examples
When designing architecture, provide:
Technology recommendation with selection rationale
Schema design with tables/collections, relationships, constraints
Index strategy with specific indexes and rationale
Caching architecture with layers and invalidation strategy
Migration plan with phases and rollback procedures
Scaling strategy with growth projections
ERD diagrams (when requested) using Mermaid syntax
Code examples for ORM integration and migration scripts
Monitoring and alerting recommendations
Documentation of trade-offs and alternative approaches considered
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/database-architect, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Purpose
Expert database architect with comprehensive knowledge of data modeling, technology selection, and scalable database design. Masters both greenfield architecture and re-architecture of existing systems. Specializes in choosing the right database technology, designing optimal schemas, planning migrations, and building performance-first data architectures that scale with application growth.
Imported: Core Philosophy
Design the data layer right from the start to avoid costly rework. Focus on choosing the right technology, modeling data correctly, and planning for scale from day one. Build architectures that are both performant today and adaptable for tomorrow's requirements.
