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/reflectnpx skills add microsoft/Generative-AI-for-beginners-dotnet --skill reflectgit 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/reflect)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/reflect"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/reflect.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 | $0.00066 | $0.01711 |
| Opus 5 | $0.00033 | $0.00856 |
| Sonnet 5 | $0.00013 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
reflect 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 6d 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 reflect — 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect Skill
Critical learning capture system for Squad. Prevents repeating mistakes and preserves successful patterns across sessions.
Analyze conversations and propose improvements to squad knowledge based on what worked, what didn't, and edge cases discovered. Every correction is a learning opportunity.
Integration with Squad Architecture
Reflect complements existing Squad knowledge systems:
.squad/agents/{agent}/history.md— Permanent learnings from completed work (append-only; each agent updates their own file; Scribe propagates cross-agent updates).squad/decisions.md— Team-wide decisions that all agents respectreflectskill — Captures in-flight learnings from conversations that may graduate to history.md or decisions.md
Workflow:
- Use
reflectduring work to capture learnings - At session end, review captured learnings
- Promote HIGH confidence patterns → lead agent for decision.md review
- Promote agent-specific patterns →
{agent}/history.mdupdates
Triggers
🔴 HIGH Priority (Invoke Immediately)
| Trigger | Example | Why Critical |
|---|---|---|
| User correction | "no", "wrong", "not like that", "never do" | Captures mistakes to prevent repetition |
| Architectural insight | "you removed that without understanding why" | Documents design decisions (Chesterton's Fence) |
| Immediate fixes | "debug", "root cause", "fix all" | Learns from errors in real-time |
🟡 MEDIUM Priority (Invoke After Multiple)
| Trigger | Example | Why Important |
|---|---|---|
| User praise | "perfect", "exactly", "great" | Reinforces successful patterns |
| Tool preferences | "use X instead of Y", "prefer" | Builds workflow preferences |
| Edge cases | "what if X happens?", "don't forget", "ensure" | Captures scenarios to handle |
🟢 LOW Priority (Invoke at Session End)
| Trigger | Example | Why Useful |
|---|---|---|
| Repeated patterns | Frequent use of specific commands/tools | Identifies workflow preferences |
| Session end | After complex work | Consolidates all session learnings |
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.
- 6d ago First seen · 230 lines · 66 tokens per session scan A a844dcf82122
reflect is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,047 stars, last pushed 5d ago), licensed MIT. It adds 66 tokens to every session and 1,711 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to reflect, differing in 0 lines, and is treated as a copy.
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