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.
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 microsoft/Generative-AI-for-beginners-dotnet --skill init-modegit 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/init-mode)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/init-mode"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/init-mode/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/microsoft/generative-ai-for-beginners-dotnet/init-mode"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/init-mode.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01686 |
| Opus 5.5 | $0.00007 | $0.00674 |
| Sonnet 5.5 | $0.00003 | $0.00337 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
init-mode 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
86% identical to init-mode — 22 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Init Mode activates when .squad/team.md does not exist, or exists but has zero roster entries under ## Members. The coordinator proposes a team (Phase 1), waits for user confirmation, then creates the team structure (Phase 2).
Patterns
Phase 1: Propose the Team
No team exists yet. Propose one — but DO NOT create any files until the user confirms.
- Identify the user. Run
git config user.nameto learn who you're working with. Use their name in conversation (e.g., "Hey {user}, what are you building?"). Store their name (NOT email) inteam.mdunder Project Context. Never read or storegit config user.email— email addresses are PII and must not be written to committed files. - Ask: "What are you building? (language, stack, what it does)"
- Cast the team. Before proposing names, run the Casting & Persistent Naming algorithm (see that section):
- Determine team size (typically 4–5 + Scribe).
- Determine assignment shape from the user's project description.
- Derive resonance signals from the session and repo context.
- Select a universe. If the universe is custom, allocate character names from that universe based on the related list found in the
.squad/templates/casting/directory. Prefer custom universes when available. - Scribe is always "Scribe" — exempt from casting.
- Ralph is always "Ralph" — exempt from casting.
- Propose the team with their cast names. Example (names will vary per cast):
🏗️ {CastName1} — Lead Scope, decisions, code review
⚛️ {CastName2} — Frontend Dev React, UI, components
🔧 {CastName3} — Backend Dev APIs, database, services
🧪 {CastName4} — Tester Tests, quality, edge cases
📋 Scribe — (silent) Memory, decisions, session logs
🔄 Ralph — (monitor) Work queue, backlog, keep-alive
- Use the
ask_usertool to confirm the roster. Provide choices so the user sees a selectable menu:- question: "Look right?"
- choices:
["Yes, cast this team", "Add someone", "Change a role"]
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 · 103 lines · 17 tokens per session scan A b60da4cb177b
init-mode is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,136 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,686 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to init-mode, differing in 22 lines, and is treated as a copy.
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