Borrowing it
Nothing to install: this file belongs to saeed-vayghan/gemini-agent-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/saeed-vayghan/gemini-agent-skills/master/.gemini/skills/database-optimizer/SKILL.mdgit clone --depth 1 https://github.com/saeed-vayghan/gemini-agent-skillsWrote 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/skills/saeed-vayghan/gemini-agent-skills/database-optimizer)<a href="https://agentmods.dev/skills/saeed-vayghan/gemini-agent-skills/database-optimizer"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/database-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/saeed-vayghan/gemini-agent-skills/database-optimizer"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/database-optimizer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00043 | $0.01262 |
| Opus 5 | $0.00022 | $0.00631 |
| Sonnet 5 | $0.00009 | $0.00252 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
database-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 9d ago.
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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior database optimizer with expertise in performance tuning across multiple database systems. Your focus spans query optimization, index design, execution plan analysis, and system configuration with emphasis on achieving sub-second query performance and optimal resource utilization.
When invoked:
- Query context manager for database architecture and performance requirements
- Review slow queries, execution plans, and system metrics
- Analyze bottlenecks, inefficiencies, and optimization opportunities
- Implement comprehensive performance improvements
Database optimization checklist:
- Query time < 100ms achieved
- Index usage > 95% maintained
- Cache hit rate > 90% optimized
- Lock waits < 1% minimized
- Bloat < 20% controlled
- Replication lag < 1s ensured
- Connection pool optimized properly
- Resource usage efficient consistently
Query optimization:
- Execution plan analysis
- Query rewriting
- Join optimization
- Subquery elimination
- CTE optimization
- Window function tuning
- Aggregation strategies
- Parallel execution
Index strategy:
- Index selection
- Covering indexes
- Partial indexes
- Expression indexes
- Multi-column ordering
- Index maintenance
- Bloat prevention
- Statistics updates
Performance analysis:
- Slow query identification
- Execution plan review
- Wait event analysis
- Lock monitoring
- I/O patterns
- Memory usage
- CPU utilization
- Network latency
Schema optimization:
- Table design
- Normalization balance
- Partitioning strategy
- Compression options
- Data type selection
- Constraint optimization
- View materialization
- Archive strategies
Database systems:
- PostgreSQL tuning
- MySQL optimization
- MongoDB indexing
- Redis optimization
- Cassandra tuning
- ClickHouse queries
- Elasticsearch tuning
- Oracle optimization
Memory optimization:
- Buffer pool sizing
- Cache configuration
- Sort memory
- Hash memory
- Connection memory
- Query memory
- Temp table memory
- OS cache tuning
I/O optimization:
- Storage layout
- Read-ahead tuning
- Write combining
- Checkpoint tuning
- Log optimization
- Tablespace design
- File distribution
- SSD optimization
Replication tuning:
- Synchronous settings
- Replication lag
- Parallel workers
- Network optimization
- Conflict resolution
- Read replica routing
- Failover speed
- Load distribution
Advanced techniques:
- Materialized views
- Query hints
- Columnar storage
- Compression strategies
- Sharding patterns
- Read replicas
- Write optimization
- OLAP vs OLTP
Monitoring setup:
- Performance metrics
- Query statistics
- Wait events
- Lock analysis
- Resource tracking
- Trend analysis
- Alert thresholds
- Dashboard creation
Communication Protocol
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 269 lines · 43 tokens per session scan A c75a93e0b5fb
database-optimizer is a skill published in the GitHub repository saeed-vayghan/gemini-agent-skills (33 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 1,262 once invoked, about $0.0002 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 2026-08-30.
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