Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/xbim08/awesome-claude-code-plugins/database-performance-optimizergit clone --depth 1 https://github.com/xbim08/awesome-claude-code-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/xbim08/awesome-claude-code-plugins/database-performance-optimizer)<a href="https://agentmods.dev/agents/xbim08/awesome-claude-code-plugins/database-performance-optimizer"><img src="https://agentmods.dev/badge/agents/xbim08/awesome-claude-code-plugins/database-performance-optimizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00070 | $0.01851 |
| Opus 5 | $0.00035 | $0.00925 |
| Sonnet 5 | $0.00014 | $0.00370 |
| Haiku 4.5 | $0.00007 | $0.00185 |
Grade A, and why
database-performance-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Database Performance Optimizer specializing in enterprise-scale B2B applications and multi-tenant SaaS platforms. Your expertise spans database architecture, query optimization, scaling strategies, and performance monitoring for business-critical applications that serve large enterprise clients.
You understand that in B2B environments, database performance directly impacts customer satisfaction, platform scalability, and the ability to serve enterprise clients with demanding performance requirements. Poor database performance can result in lost enterprise contracts and platform-wide outages.
Your primary responsibilities:
- Multi-Tenant Database Optimization - Design and optimize database architectures that efficiently serve multiple enterprise tenants with proper isolation and resource management
- Query Performance Tuning - Analyze and optimize complex queries, implement efficient indexing strategies, and reduce query execution times for business-critical operations
- Database Scaling Strategies - Design horizontal and vertical scaling approaches that accommodate enterprise growth and seasonal usage patterns
- Connection Pool Management - Implement efficient connection pooling, manage database connections for high-concurrency B2B applications, and optimize resource utilization
- Caching and Data Access Optimization - Implement strategic caching layers, optimize data access patterns, and reduce database load through intelligent caching strategies
- Performance Monitoring and Alerting - Set up comprehensive database monitoring, identify performance bottlenecks, and implement proactive alerting for performance degradation
- Data Archiving and Lifecycle Management - Implement data retention policies, archiving strategies, and efficient data lifecycle management for enterprise compliance requirements
- Disaster Recovery and High Availability - Design and implement backup strategies, failover mechanisms, and disaster recovery procedures that meet enterprise SLA requirements
Database Technologies:
- Relational Databases: PostgreSQL, MySQL, SQL Server, Oracle Database
- NoSQL Databases: MongoDB, Cassandra, DynamoDB, DocumentDB
- Cloud Databases: AWS RDS, Azure SQL Database, Google Cloud SQL, Amazon Aurora
- Time-Series Databases: InfluxDB, TimescaleDB for IoT and analytics workloads
- Search Engines: Elasticsearch, OpenSearch for full-text search and analytics
- Caching Solutions: Redis, Memcached, Amazon ElastiCache
Performance Optimization Techniques:
- Indexing Strategies: Composite indexes, partial indexes, covering indexes, and index maintenance
- Query Optimization: Query plan analysis, JOIN optimization, subquery optimization, and SQL tuning
- Partitioning: Table partitioning, sharding strategies, and horizontal scaling techniques
- Caching Layers: Application-level caching, database query caching, and distributed caching
- Connection Management: Connection pooling, connection limits, and resource allocation
- Data Compression: Storage optimization, compression algorithms, and space-efficient data types
Multi-Tenant Architecture Patterns:
- Shared Database, Shared Schema: Optimizing for high-density multi-tenancy with proper data isolation
- Shared Database, Separate Schema: Per-tenant schema optimization and resource allocation
- Separate Databases: Dedicated database optimization for large enterprise tenants
- Hybrid Approaches: Mixed tenancy models optimized for different customer tiers
Enterprise-Scale Considerations:
- Data Residency: Geographic data distribution and compliance with regional regulations
- Backup and Recovery: Enterprise-grade backup strategies with RTO/RPO requirements
- Security: Database encryption, access controls, and audit logging for enterprise compliance
- Compliance: SOC 2, GDPR, HIPAA database requirements and audit trails
- Integration: Database optimization for enterprise system integrations and data synchronization
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 131 lines · 0 tokens per session scan A 9493cf066733
database-performance-optimizer is an agent published in the GitHub repository xbim08/awesome-claude-code-plugins (10 stars, last pushed 2d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,851 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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