Skills database-optimizer
📦

database-optimizer

Content revision r2 Safe ⚙️ External commands

Optimize Database Performance and Scalability

Slow queries and weak database designs can raise latency, cost, and operational risk. This skill provides evidence-based guidance for queries, indexes, caching, scaling, migrations, and monitoring.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "database-optimizer" from https://skillstore.io/skills/sickn33-database-optimizer.md and its manifest at https://skillstore.io/api/skills/sickn33-database-optimizer/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-database-optimizer.md Signed manifest GET /api/skills/sickn33-database-optimizer/manifest Signed lockfile GET /api/skills/sickn33-database-optimizer/lockfile

Test it

Using "database-optimizer". A PostgreSQL order lookup is slow despite an index on customer_id.

Expected outcome:

  • Finding: filters on status and created_at may force the single-column index to scan many rows.
  • Recommendation: test a composite index ordered by equality and range predicates with EXPLAIN ANALYZE.
  • Validation: compare latency, rows scanned, index size, and write overhead before deployment.

Using "database-optimizer". A GraphQL products page issues one inventory query per product.

Expected outcome:

  • Finding: field-level inventory loading creates an N+1 query pattern.
  • Recommendation: batch inventory requests by product identifier and cache results within each request.
  • Validation: assert a bounded query count and compare response latency under representative load.

Using "database-optimizer". A write-heavy events table is growing beyond current maintenance windows.

Expected outcome:

  • Plan: baseline ingestion, retention, query patterns, storage growth, and maintenance duration.
  • Options: compare time partitioning, archival, compression, and read replicas against operational requirements.
  • Rollout: test one partition interval, monitor write amplification, and retain a documented rollback path.

Security Audit

Safe
v5 • 7/23/2026 Open versioned report

The static finding is a false positive because SKILL.md line 24 uses backticks only to format a Markdown path. No executable command, prompt injection, or semantic security threat was found.

1
Files scanned
169
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:24
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). database-optimizer security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-database-optimizer/audits/5

BibTeX citation

@techreport{sickn33-sickn33-database-optimizer-2026, author = {sickn33}, title = {database-optimizer security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-database-optimizer/audits/5}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "database-optimizer security audit report (audit version 5)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-23" url: "https://skillstore.io/skills/sickn33-database-optimizer/audits/5" identifiers: - type: other value: "skillstore:sickn33-database-optimizer:audit:5" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance

What You Can Build

Resolve Production Query Latency

Review slow queries, execution plans, locks, and metrics to identify prioritized improvements with validation steps.

Remove Application N+1 Queries

Analyze ORM or GraphQL query patterns and design batching, eager-loading, or caching changes.

Plan Database Growth

Compare partitioning, replicas, sharding, caching, and cloud scaling options against workload and cost requirements.

Try These Prompts

Review a Slow Query 
Review this slow query for [database]. Explain likely bottlenecks and suggest safe improvements using [query, schema, and execution plan].
Design an Index Strategy 
Design indexes for [table schema] and [frequent queries]. Explain column order, expected benefits, write costs, and validation steps.
Diagnose N+1 Behavior 
Diagnose N+1 behavior in [framework] using [query log]. Recommend batching or eager-loading changes and a regression test.
Create a Scaling Roadmap 
Create a phased scaling plan for [workload metrics], covering partitioning, replicas, caching, migration safety, observability, cost, and rollback criteria.

Best Practices

  • Capture latency, throughput, resource use, and execution plans before recommending changes.
  • Test optimizations with representative data and load before production deployment.
  • Document expected benefits, operational costs, monitoring signals, and rollback criteria.

Avoid

  • Do not add indexes without checking workload frequency, selectivity, storage, and write overhead.
  • Do not rewrite queries without comparing execution plans and measured results.
  • Do not introduce caching, sharding, or denormalization before defining consistency and invalidation requirements.

Frequently Asked Questions

Which databases does this skill cover?
It covers major relational, NoSQL, cloud, time-series, graph, search, and columnar database platforms described in the skill.
Can it connect to my database?
No. It analyzes the schemas, plans, logs, metrics, and constraints that you provide.
What inputs produce useful recommendations?
Provide the database version, schema, query, execution plan, data volume, workload pattern, current metrics, and operational constraints.
Does it replace performance testing?
No. Every recommendation requires testing with representative data, concurrency, and production-like resource limits.
Can it help plan safe migrations?
Yes. It can propose phased migration, monitoring, validation, rollback, and zero-downtime strategies.
How are recommendations prioritized?
Recommendations prioritize measured bottlenecks, expected impact, implementation risk, maintainability, scalability, and cost.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/database-optimizer

Ref

f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526

Maintenance freshness

7/26/2026

Usage

10 downloads · 102 views

File structure

📄 SKILL.md