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/model-selectionnpx skills add microsoft/Generative-AI-for-beginners-dotnet --skill model-selectiongit 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/model-selection)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/model-selection"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/model-selection.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 | $0.00015 | $0.01342 |
| Opus 5 | $0.00008 | $0.00671 |
| Sonnet 5 | $0.00003 | $0.00268 |
| Haiku 4.5 | $0.00002 | $0.00134 |
Grade A, and why
model-selection 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 4d 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 model-selection — 8 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Selection
Determines which LLM model to use for each agent spawn.
SCOPE
✅ THIS SKILL PRODUCES:
- A resolved
modelparameter for everytasktool call - Persistent model preferences in
.squad/config.json - Spawn acknowledgments that include the resolved model
❌ THIS SKILL DOES NOT PRODUCE:
- Code, tests, or documentation
- Model performance benchmarks
- Cost reports or billing artifacts
Context
Squad supports 18+ models across three tiers (premium, standard, fast). The coordinator must select the right model for each agent spawn. Users can set persistent preferences that survive across sessions.
5-Layer Model Resolution Hierarchy
Resolution is first-match-wins — the highest layer with a value wins.
| Layer | Name | Source | Persistence |
|---|---|---|---|
| 0a | Per-Agent Config | .squad/config.json → agentModelOverrides.{name} |
Persistent (survives sessions) |
| 0b | Global Config | .squad/config.json → defaultModel |
Persistent (survives sessions) |
| 1 | Session Directive | User said "use X" in current session | Session-only |
| 2 | Charter Preference | Agent's charter.md → ## Model section |
Persistent (in charter) |
| 3 | Task-Aware Auto | Code → sonnet, docs → haiku, visual → opus | Computed per-spawn |
| 4 | Default | claude-haiku-4.5 |
Hardcoded fallback |
Key principle: Layer 0 (persistent config) beats everything. If the user said "always use opus" and it was saved to config.json, every agent gets opus regardless of role or task type. This is intentional — the user explicitly chose quality over cost.
AGENT WORKFLOW
On Session Start
- READ
.squad/config.json - CHECK for
defaultModelfield — if present, this is the Layer 0 override for all spawns - CHECK for
agentModelOverridesfield — if present, these are per-agent Layer 0a overrides - STORE both values in session context for the duration
On Every Agent Spawn
- CHECK Layer 0a: Is there an
agentModelOverrides.{agentName}in config.json? → Use it. - CHECK Layer 0b: Is there a
defaultModelin config.json? → Use it. - CHECK Layer 1: Did the user give a session directive? → Use it.
- CHECK Layer 2: Does the agent's charter have a
## Modelsection? → Use it. - CHECK Layer 3: Determine task type:
- Code (implementation, tests, refactoring, bug fixes) →
claude-sonnet-4.6 - Prompts, agent designs →
claude-sonnet-4.6 - Visual/design with image analysis →
claude-opus-4.6 - Non-code (docs, planning, triage, changelogs) →
claude-haiku-4.5
- Code (implementation, tests, refactoring, bug fixes) →
- FALLBACK Layer 4:
claude-haiku-4.5 - INCLUDE model in spawn acknowledgment:
🔧 {Name} ({resolved_model}) — {task}
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.
- 4d ago First seen · 126 lines · 15 tokens per session scan A b98edce1040a
model-selection is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,046 stars, last pushed 3d ago), licensed MIT. It adds 15 tokens to every session and 1,342 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 model-selection, differing in 8 lines, and is treated as a copy.
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