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SQL-AI-samples copilot-instructions.md

Instructions for building SQL database applications with SQL Database Projects, where database structure is stored as version-controlled SQL files. They cover organizing tables, views, and stored procedures and creating a local SQL Server instance.

In plain words
What is it for?
Use them to scaffold or modify a T-SQL application, organize database objects, keep scripts repeatable, and deploy a local SQL Server database.
Why use it?
They provide a consistent way to manage database changes and set up a local environment for development and testing.

Instructions file for GitHub Copilot

331 3mo ago A scan 3,630 tokens original MIT
View the source file Azure-Samples/SQL-AI-samples
Install

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.

agentmods
npx agentmods add instructions/azure-samples/sql-ai-samples/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/Azure-Samples/SQL-AI-samples

Made for: GitHub Copilot.

Per session 3,630 This file is loaded in full into every session.
When invoked 3,630 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.03630 $0.03630
Opus 5 $0.01815 $0.01815
Sonnet 5 $0.00726 $0.00726
Haiku 4.5 $0.00363 $0.00363

Measured 2d ago against content hash a973d950bf2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

SQL-AI-samples copilot-instructions.md 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 2d 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.

AgentMode/.github/copilot-instructions.md · 278 lines

How it starts

The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Guidelines for AI Assistants: Scaffolding SQL Database Projects for T/SQL Applications

This notebook guides AI assistants through a step-by-step chain-of-thought to generate a full SQL Database Application using the SQL project SDK, Microsoft.Build.Sql

Each database object must be placed in its own .sql file, organized into a coherent folder structure. (this makes it easier for VSCode agent-mode to work with files)

The application should expose Stored Procedures and Views for all operations (all tables should be hidden as internal to the application).

NOTES:

  • You (the AI Agent) must not edit this file.

Summary

  • Use SQL Database Projects (.sqlproj) to manage schema as code. (Target SQL Server 2019, as this is the latest version that tSQLt supports via a .dacpac)
  • You can find the full documentation for Sql Database Projects here:
  • https://raw.githubusercontent.com/MicrosoftDocs/sql-docs/refs/heads/live/docs/tools/sql-database-projects/sql-database-projects.md
  • Keep T/SQL scripts idempotent and version-controlled.
  • For local deployment, use sqlcmd create mssql to create a local SQL Server instance.
  • use the helper script create-mssql-instance-using-sqlcmd.ps1 in the ./.github/tsql/install folder
  • To get the connection string, run sqlcmd config connection-strings (use the helper script get-sql-connection-string.ps1 in the ./.github/tsql/install folder).

Considerations

  • Folder structure maps directly to SQL object types.
  • Idempotency: scripts must handle repeated executions.
  • Naming conventions align with best practices.
  • After each step, you MUST:
    • build (using Build.ps1, which takes -ProjectName as the single parameter)
      • Build.ps1 is in the ./.github/tsql/inner-loop folder.
    • publish (using Publish.ps1, which takes -ProjectName as the single parameter)
      • Publish.ps1 is in the ./.github/tsql/inner-loop folder.
      • Publish.ps1 uses SqlPackage to publish the project to the SQL Server.

Read the full file on GitHub · 278 lines

Changes

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.

  1. 2d ago First seen · 278 lines · 3,630 tokens per session scan A a973d950bf2b

Subscribe to this mod's changes

SQL-AI-samples copilot-instructions.md is an instructions file published in the GitHub repository Azure-Samples/SQL-AI-samples (331 stars, last pushed 3mo ago), licensed MIT. It adds 3,630 tokens to every session, about $0.0181 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.

Data and AIDatabasesDevOps and cloudLLMaiazureazure-sql-databaseazure-sql-db
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