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 agentmods add skills/microsoft/generative-ai-for-beginners-dotnet/client-compatibilitynpx skills add microsoft/Generative-AI-for-beginners-dotnet --skill client-compatibilitygit 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/client-compatibility)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/client-compatibility"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/client-compatibility.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 | $0.00018 | $0.01238 |
| Opus 5 | $0.00009 | $0.00619 |
| Sonnet 5 | $0.00004 | $0.00248 |
| Haiku 4.5 | $0.00002 | $0.00124 |
Grade A, and why
client-compatibility 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 5d 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 client-compatibility — 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Squad runs on multiple Copilot surfaces (CLI, VS Code, JetBrains, GitHub.com). The coordinator must detect its platform and adapt spawning behavior accordingly. Different tools are available on different platforms, requiring conditional logic for agent spawning, SQL usage, and response timing.
Patterns
Platform Detection
Before spawning agents, determine the platform by checking available tools:
-
CLI mode —
tasktool is available → full spawning control. Usetaskwithagent_type,mode,model,description,promptparameters. Collect results viaread_agent. -
VS Code mode —
runSubagentoragenttool is available → conditional behavior. UserunSubagentwith the task prompt. Dropagent_type,mode, andmodelparameters. Multiple subagents in one turn run concurrently (equivalent to background mode). Results return automatically — noread_agentneeded. -
Fallback mode — neither
tasknorrunSubagent/agentavailable → work inline. Do not apologize or explain the limitation. Execute the task directly.
If both task and runSubagent are available, prefer task (richer parameter surface).
VS Code Spawn Adaptations
When in VS Code mode, the coordinator changes behavior in these ways:
- Spawning tool: Use
runSubagentinstead oftask. The prompt is the only required parameter — pass the full agent prompt (charter, identity, task, hygiene, response order) exactly as you would on CLI. - Parallelism: Spawn ALL concurrent agents in a SINGLE turn. They run in parallel automatically. This replaces
mode: "background"+read_agentpolling. - Model selection: Accept the session model. Do NOT attempt per-spawn model selection or fallback chains — they only work on CLI. In Phase 1, all subagents use whatever model the user selected in VS Code's model picker.
- Scribe: Cannot fire-and-forget. Batch Scribe as the LAST subagent in any parallel group. Scribe is light work (file ops only), so the blocking is tolerable.
- Launch table: Skip it. Results arrive with the response, not separately. By the time the coordinator speaks, the work is already done.
read_agent: Skip entirely. Results return automatically when subagents complete.agent_type: Drop it. All VS Code subagents have full tool access by default. Subagents inherit the parent's tools.description: Drop it. The agent name is already in the prompt.- Prompt content: Keep ALL prompt structure — charter, identity, task, hygiene, response order blocks are surface-independent.
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.
- 5d ago First seen · 90 lines · 18 tokens per session scan A d8da5e1e9918
client-compatibility is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,047 stars, last pushed 4d ago), licensed MIT. It adds 18 tokens to every session and 1,238 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to client-compatibility, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
tidy-skill
Keep local AI agent environments clean, explainable, and recoverable. Use for repo artifact governance, workspace cache audits, WSL2/Docker hygiene, package and model cache mapping, C-drive growth diagnosis, and safe cleanup boundaries. Prevent throwaway Markdown files, audit local development environment sprawl, and…
terminal-management
Teaches AI agents to properly manage VS Code terminal lifecycle — always use background terminals and kill them after commands complete. Prevents zombie terminal accumulation in GitHub Codespaces and VS Code.
ai-generated-readme-banners
Create professional ultra-wide cinematic banners for GitHub READMEs using modern AI image models with typography.
appropriate-reliance
Calibrated human-AI collaboration with creative latitude — trust calibrated to reliability, creativity preserved with validation.
brain-qa
Brain QA is a mental exercise, not just a muscle exercise. The script validates structure, but the skill's true value is teaching Alex what to look for beyond what scripts can automate.
cloud-solution-architect
Design well-architected Azure cloud systems — 10 design principles, 6 architecture styles, 44 design patterns, technology choices, WAF pillars.