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 skills/microsoft/agentic-journeys/data-access-abstractionnpx skills add microsoft/agentic-journeys --skill data-access-abstractiongit clone --depth 1 https://github.com/microsoft/agentic-journeysWrote 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/microsoft/agentic-journeys/data-access-abstraction)<a href="https://agentmods.dev/skills/microsoft/agentic-journeys/data-access-abstraction"><img src="https://agentmods.dev/badge/skills/microsoft/agentic-journeys/data-access-abstraction.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 | $0.00073 | $0.06368 |
| Opus 5 | $0.00036 | $0.03184 |
| Sonnet 5 | $0.00015 | $0.01274 |
| Haiku 4.5 | $0.00007 | $0.00637 |
Grade A, and why
data-access-abstraction 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 3d 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 data-access-abstraction — 0 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 — 799 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Access Abstraction
Build APIs with swappable data layers using the repository pattern. Develop locally with SQLite, deploy to Azure with Cosmos DB or PostgreSQL — same route code, different backend. Works in any language.
When to Use This Skill
- Building an API that needs to run locally (SQLite) and in Azure (Cosmos DB or PostgreSQL)
- Adding a new data provider to an existing app without changing routes or business logic
- Migrating from one database to another incrementally
- Any project following the AIMarket journey pattern (local dev → cloud deploy)
Pattern Overview
The pattern is the same regardless of language:
Routes/Controllers → Repository Interfaces → Factory → Implementations
│
├── SQLite (local dev)
├── Cosmos DB (Azure deploy)
├── PostgreSQL (Azure deploy)
└── In-memory (testing)
Three rules:
- Define repository interfaces (or abstract classes / protocols) per entity
- Routes depend only on the interfaces — never import a database client directly
- A factory reads a config value (
DATA_PROVIDER) and returns the right implementation
Adding a new database means writing a new implementation file. Zero changes to routes.
Environment Variable
All languages use the same convention:
DATA_PROVIDER=sqlite # Local development (default)
DATA_PROVIDER=cosmos # Azure Cosmos DB
DATA_PROVIDER=postgres # Azure PostgreSQL
Node.js / TypeScript
Repository Interfaces
// data/interfaces.ts
export interface IProductRepository {
getAll(params: {
page: number; pageSize: number;
category?: string; minPrice?: number; maxPrice?: number;
}): Promise<{ data: Product[]; totalCount: number }>;
getById(id: string): Promise<Product | null>;
create(input: CreateProductInput): Promise<Product>;
update(id: string, fields: Partial<Product>): Promise<Product | null>;
}
export interface IOrderRepository {
create(input: CreateOrderInput): Promise<Order>;
getById(id: string): Promise<Order | null>;
getByUserId(userId: string, page: number, pageSize: number): Promise<{ data: Order[]; totalCount: number }>;
}
export interface IUserRepository {
create(input: CreateUserInput): Promise<User>;
getById(id: string): Promise<User | null>;
getByEmail(email: string): Promise<User | null>;
}
export interface DataStore {
products: IProductRepository;
orders: IOrderRepository;
users: IUserRepository;
close?(): void;
}
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.
- 3d ago First seen · 799 lines · 73 tokens per session scan A 416a4fa1299c
data-access-abstraction is a skill published in the GitHub repository microsoft/agentic-journeys (5 stars, last pushed 25d ago), licensed MIT. It adds 73 tokens to every session and 6,368 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-access-abstraction, differing in 0 lines, and is treated as a copy.
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