database-designer
by alirezarezvanidatabase-designer is a Database Engineering skill for schema analysis, index recommendations, SQL/NoSQL selection, and safe migration planning with Python helpers and references.
This skill scores 84/100, which means it is a solid listing candidate for directory users who want a database-design workflow with executable helper tools and examples. It is meaningfully more actionable than a generic prompt because it defines trigger cases, provides scripts for schema analysis/index optimization/migrations, and includes sample inputs and expected outputs, though users should still treat generated SQL recommendations as requiring review.
- Clear trigger scope: the frontmatter says to use it for schema design, migrations, query optimization, SQL vs NoSQL choices, and data relationship modeling.
- Substantial operational content: SKILL.md is large and structured, and the repository includes schema analysis, index optimization, and migration-generation Python tools with usage examples.
- Good adoption evidence: sample schemas, query-pattern assets, expected output files, and reference guides give agents concrete formats and examples to follow.
- SKILL.md lacks an explicit install command, so directory users may need to infer setup from the repository/README rather than from the skill file itself.
- The toolkit appears to rely on lightweight Python parsers and JSON/DDL inputs, so users should validate outputs against their target database engine before applying migrations or indexes.
Overview of database-designer skill
What database-designer is for
database-designer is a Database Engineering skill for turning database design questions into structured schema analysis, index recommendations, migration plans, and database selection guidance. It is most useful when you need more than a generic “design a schema” answer: you want the agent to inspect DDL or JSON schema, reason about relationships, constraints, normalization, query patterns, and safe schema evolution.
Best-fit users and jobs
Install this skill if you regularly ask an AI assistant to review SQL schemas, model entities, choose SQL vs NoSQL, plan migrations, or optimize indexes from real query patterns. It fits backend engineers, database engineers, technical founders, and architects who can provide schema details and workload context. Beginners can use it too, but the best results come when you can describe tables, access patterns, growth expectations, and operational constraints.
What makes the database-designer skill different
The repository includes practical helpers beyond the prompt workflow: schema_analyzer.py, index_optimizer.py, and migration_generator.py. These scripts support analysis of schema structure, query-driven index design, and migration planning with rollback and validation thinking. The references also add useful decision support, including database selection, normalization, and index strategy patterns.
Where it is strongest and weakest
The skill is strongest for relational schema review, SQL DDL analysis, missing constraint detection, index planning, Mermaid ERD generation, and expand-contract migration thinking. It is less suitable as a replacement for live database profiling, production query-plan inspection, vendor-specific tuning, or legal/compliance review. Treat its output as an engineering draft to validate against your actual database engine, data volume, and deployment process.
How to Use database-designer skill
database-designer install and first files to read
Install the database-designer skill with:
npx skills add alirezarezvani/claude-skills --skill database-designer
After installing, read SKILL.md first for invocation scope, then README.md for tool-oriented workflow. Preview the sample inputs in assets/sample_schema.sql, assets/sample_schema.json, and assets/sample_query_patterns.json before using your own files. For deeper judgment, read references/database-design-reference.md, references/normalization_guide.md, references/index_strategy_patterns.md, and references/database_selection_decision_tree.md.
Inputs that produce useful results
The skill works best when you provide concrete database artifacts, not just a product idea. Useful inputs include:
- Current schema as SQL DDL or JSON
- Target schema or proposed model
- Main entities and relationships
- Query patterns, joins, filters, sorting, and frequency
- Expected row counts, cardinality, and growth rate
- Database engine and version, such as PostgreSQL, MySQL, SQLite, MongoDB, or DynamoDB
- Migration limits, downtime tolerance, rollback requirements, and data backfill needs
A weak prompt is: “Design a database for ecommerce.”
A stronger prompt is: “Use database-designer to review this PostgreSQL ecommerce schema. Optimize for product search, order history, and inventory updates. Expected scale is 2M products, 20M orders, and 5k login queries/minute. Identify normalization issues, missing constraints, index strategy, and migration risks.”
Practical database-designer usage workflow
For schema review, start with schema_analyzer.py:
python schema_analyzer.py --input schema.sql --output-format text
Use --generate-erd when you need a Mermaid relationship diagram for documentation or review. For index work, prepare query-pattern JSON similar to assets/sample_query_patterns.json, then run:
python index_optimizer.py --schema schema.json --queries queries.json --format text
For migration planning, compare current and target schema files:
python migration_generator.py --current current.json --target target.json --zero-downtime
In the assistant, ask for both the automated findings and the engineering rationale. The best database-designer usage combines scripts for repeatable checks with an AI prompt that explains tradeoffs, operational risks, and next actions.
Prompt pattern for better output
Use this structure when invoking the skill:
- State the task: schema review, index optimization, migration plan, or database selection.
- Provide the schema or file path.
- Describe workload and constraints.
- Ask for a prioritized output format.
Example:
“Use the database-designer skill for Database Engineering. Analyze schema.sql and these query patterns. Prioritize critical correctness issues, missing constraints, high-impact indexes, and migration-safe changes. Separate recommendations into quick fixes, risky changes, and items requiring production metrics.”
database-designer skill FAQ
Is database-designer better than an ordinary database prompt?
Yes, when you have real schema or workload inputs. A generic prompt may produce plausible tables or indexes, but database-designer gives the agent a more specific operating mode: analyze normalization, constraints, index gaps, redundant indexes, migration safety, rollback, and database selection. It also includes example assets and Python tools that make the workflow more concrete.
Can I use it without running the Python scripts?
Yes. The skill can guide an AI conversation even if you only paste DDL, schema JSON, or a design brief. However, the scripts are valuable when you want repeatable analysis, formatted output, ERD generation, or migration comparison. Python 3.7+ is enough, and the included tools appear designed without external dependencies.
What database systems does it fit?
The skill is broadly useful for relational database design and can help compare SQL and NoSQL options. Its strongest examples and tooling center on SQL-style schemas, constraints, indexes, and migrations. For engine-specific tuning, supplement its recommendations with PostgreSQL EXPLAIN ANALYZE, MySQL execution plans, cloud database metrics, or vendor documentation.
When should I not install database-designer?
Skip it if you only need one-off naming suggestions, a toy schema, or a high-level product brainstorm. Also avoid treating it as authoritative for production tuning without real query plans, data distribution, lock behavior, and deployment constraints. It is a design and analysis accelerator, not a substitute for database observability or DBA review.
How to Improve database-designer skill
Improve database-designer results with workload detail
The most common failure mode is under-specified workload. Index and schema advice changes dramatically based on read/write ratio, selectivity, join paths, ordering, tenant isolation, and archival rules. Add query frequency, slow query examples, expected cardinality, and latency targets. If you do not know exact numbers, provide ranges and mark assumptions explicitly.
Validate recommendations against production constraints
Before applying output, ask the skill to classify each recommendation by risk: safe DDL, blocking migration, data rewrite, backfill required, application change required, or needs production measurement. For migrations, require rollback SQL, validation queries, deployment order, and an expand-contract plan when downtime is not acceptable.
Iterate after the first analysis
Do not stop at the first report. Feed the initial recommendations back with constraints such as “avoid table rewrites,” “PostgreSQL only,” “writes are more important than reads,” or “cannot add more than three indexes.” This turns broad database-designer guidance into an implementable engineering plan.
Keep local examples and references updated
To improve the database-designer skill for your team, add representative schemas, query-pattern files, expected output examples, and engine-specific notes. The most valuable additions are real migration incidents, accepted indexing conventions, naming rules, and database selection criteria. This makes future results more consistent with your architecture instead of generic best practice.