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 skills add ComeOnOliver/skillshub --skill database-performancegit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote 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/comeonoliver/skillshub/database-performance)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/database-performance"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/database-performance/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/comeonoliver/skillshub/database-performance"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/database-performance.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 | $0.00044 | $0.03274 |
| Opus 5.5 | $0.00018 | $0.01310 |
| Sonnet 5.5 | $0.00009 | $0.00655 |
| Haiku 4.5 | $0.00004 | $0.00327 |
Grade A, and why
database-performance 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 1mo 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.
This is a copy
100% identical to database-performance — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Performance Patterns
When to Use This Skill
Use this skill when:
- Designing data access layers
- Optimizing slow database queries
- Choosing between EF Core and Dapper
- Avoiding common performance pitfalls
Core Principles
- Separate read and write models - Don't use the same types for both
- Think in batches - Avoid N+1 queries
- Only retrieve what you need - No SELECT *
- Apply row limits - Always have a configurable Take/Limit
- Do joins in SQL - Never in application code
- AsNoTracking for reads - EF Core change tracking is expensive
Read/Write Model Separation (CQRS Pattern)
Read and write models are fundamentally different - they have different shapes, columns, and purposes. Don't create a single "User" entity and reuse it everywhere.
- Read models are denormalized, optimized for query efficiency, and return multiple projection types (UserProfile, UserSummary, UserDetailForAdmin)
- Write models are normalized, validation-focused, and accept strongly-typed commands (CreateUserCommand, UpdateUserCommand)
Architecture
src/
MyApp.Data/
Users/
# Read side - multiple optimized projections
IUserReadStore.cs
PostgresUserReadStore.cs
# Write side - command handlers
IUserWriteStore.cs
PostgresUserWriteStore.cs
# Read DTOs - lightweight, denormalized
UserProfile.cs
UserSummary.cs
# Write commands - validation-focused
CreateUserCommand.cs
UpdateUserCommand.cs
Orders/
IOrderReadStore.cs
IOrderWriteStore.cs
(similar structure...)
Read Store Interface
// Read models: Multiple specialized projections optimized for different use cases
public interface IUserReadStore
{
// Returns detailed profile for single-user view
Task<UserProfile?> GetByIdAsync(UserId id, CancellationToken ct = default);
// Returns lightweight info for lookups
Task<UserProfile?> GetByEmailAsync(EmailAddress email, CancellationToken ct = default);
// Returns paginated summaries - only what the list view needs
Task<IReadOnlyList<UserSummary>> GetAllAsync(int limit, UserId? cursor = null, CancellationToken ct = default);
// Boolean query - no entity needed
Task<bool> EmailExistsAsync(EmailAddress email, CancellationToken ct = default);
}
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.
- 1mo ago First seen · 507 lines · 44 tokens per session scan A a2d5397783b9
database-performance is a skill published in the GitHub repository ComeOnOliver/skillshub (64 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 3,274 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to database-performance, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
database-performance
Database access patterns for performance. Separate read/write models, avoid N+1 queries, use AsNoTracking, apply row limits, and never do application-side joins. Works with EF Core and Dapper.
database-performance
Database access patterns for performance. Separate read/write models, avoid N+1 queries, use AsNoTracking, apply row limits, and never do application-side joins. Works with EF Core and Dapper.
doctrine-fetch-modes
Optimize Doctrine fetching with DTO hydration (SELECT NEW), fetch joins, lazy loading, query hints, and DBAL 4 access.
doctrine-batch-processing
Process large datasets with Doctrine (ORM 3 toIterable, flush+clear, bulk DQL) and memory management.
mongodb-expert
Expert-level MongoDB database design, aggregation pipelines, indexing, replication, and production operations. Use when the user mentions NoSQL, database, aggregation, or performance, or when the task involves CRUD Operations, Query Operators, Aggregation Pipeline, or Indexing.
postgresql-expert
Expert-level PostgreSQL database administration, advanced queries, performance tuning, and production operations. Use when the user mentions database, SQL, or performance, or when the task involves Advanced Data Types, Full-Text Search, Advanced Indexes, or Advanced Queries.