Generative AI for Beginners .NET is a hands-on course that teaches .NET developers to build applications using generative AI models and related tools. Its lessons use practical samples covering scenarios such as chat, audio transcription, agents, and local AI. The catalogue entries are add-ons associated with the course repository.
Borrowing it
Nothing to install: this file belongs to microsoft/Generative-AI-for-beginners-dotnet. 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/microsoft/Generative-AI-for-beginners-dotnet/main/.github/skills/squad-conventions/SKILL.mdgit clone --depth 1 https://github.com/microsoft/Generative-AI-for-beginners-dotnetWrote 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/generative-ai-for-beginners-dotnet/squad-conventions)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/squad-conventions"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/squad-conventions.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 5.1 | $0.00015 | $0.00824 |
| Opus 5 | $0.00008 | $0.00412 |
| Sonnet 5 | $0.00003 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
squad-conventions scanned grade A with 1 finding 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 8d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
Squad has zero runtime dependencies. Everything uses Node.js built-ins (`fs`, `path`, `os`, `child_process`). Do not add packages to `dependencies` in `package.json`. This is a hard constraint, not a preference. This is a copy
100% identical to squad-conventions — 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
These conventions apply to all work on the Squad CLI tool (create-squad). Squad is a zero-dependency Node.js package that adds AI agent teams to any project. Understanding these patterns is essential before modifying any Squad source code.
Patterns
Zero Dependencies
Squad has zero runtime dependencies. Everything uses Node.js built-ins (fs, path, os, child_process). Do not add packages to dependencies in package.json. This is a hard constraint, not a preference.
Node.js Built-in Test Runner
Tests use node:test and node:assert/strict — no test frameworks. Run with npm test. Test files live in test/. The test command is node --test test/.
Error Handling — fatal() Pattern
All user-facing errors use the fatal(msg) function which prints a red ✗ prefix and exits with code 1. Never throw unhandled exceptions or print raw stack traces. The global uncaughtException handler calls fatal() as a safety net.
ANSI Color Constants
Colors are defined as constants at the top of index.js: GREEN, RED, DIM, BOLD, RESET. Use these constants — do not inline ANSI escape codes.
File Structure
.squad/— Team state (user-owned, never overwritten by upgrades).squad/templates/— Template files copied fromtemplates/(Squad-owned, overwritten on upgrade).github/agents/squad.agent.md— Coordinator prompt (Squad-owned, overwritten on upgrade)templates/— Source templates shipped with the npm package.squad/skills/— Team skills in SKILL.md format (user-owned).squad/decisions/inbox/— Drop-box for parallel decision writes
Windows Compatibility
Always use path.join() for file paths — never hardcode / or \ separators. Squad must work on Windows, macOS, and Linux. All tests must pass on all platforms.
Init Idempotency
The init flow uses a skip-if-exists pattern: if a file or directory already exists, skip it and report "already exists." Never overwrite user state during init. The upgrade flow overwrites only Squad-owned files.
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.
- 8d ago First seen · 70 lines · 15 tokens per session scan A 7e1a53f10cee
squad-conventions is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,051 stars, last pushed 7d ago), licensed MIT. It adds 15 tokens to every session and 824 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to squad-conventions, differing in 0 lines, and is treated as a copy.
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tidy-skill
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azure-ml-dataset-creator
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azure-ml-model-evaluation
Evaluate generative AI applications and models locally or in the cloud using Azure AI Evaluation SDK. Measure quality, safety, and performance with built-in and custom evaluators.
azure-ml-llm-trainer
Train or fine-tune LLMs on Azure ML managed compute with TRL trainers. Uses direct trainer loops (SFT, DPO, RL) without relying on serverless APIs or Hugging Face infrastructure.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.