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 history-hygienegit 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/history-hygiene)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/history-hygiene"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/history-hygiene/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/history-hygiene"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/history-hygiene.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00018 | $0.00366 |
| Opus 5 | $0.00009 | $0.00183 |
| Sonnet 5 | $0.00004 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
history-hygiene 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 11d 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 history-hygiene — 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.
What it actually says
Context
History files (.md files tracking decisions, spawns, outcomes) are read cold by future agents. Stale or incorrect entries poison decision-making downstream. The Kobayashi incident proved this: history said "Brady decided v0.6.0" when Brady had reversed that to v0.8.17. Future spawns read the wrong truth and repeated the mistake.
Patterns
- Record the final outcome, not the initial request.
- Wait for confirmation before writing to history — don't log intermediate states.
- If a decision reverses, update the entry immediately — don't leave stale data.
- One read = one truth. A future agent should never need to cross-reference other files to understand what actually happened.
Examples
✓ Correct:
- "Migration target: v0.8.17 (initially discussed as v0.6.0, corrected by Brady)"
- "Reverted to Node 18 per Brady's explicit request on 2024-01-15"
✗ Incorrect:
- "Brady directed v0.6.0" (when later reversed)
- Recording what was requested instead of what actually happened
- Logging entries before outcome is confirmed
Anti-Patterns
- Writing intermediate or "for now" states to disk
- Attributing decisions without confirming final direction
- Treating history like a draft — history is the source of truth
- Assuming readers will cross-reference or verify; they won't
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.
- 11d ago First seen · 37 lines · 18 tokens per session scan A 1087baea8153
history-hygiene is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,058 stars, last pushed 10d ago), licensed MIT. It adds 18 tokens to every session and 366 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 history-hygiene, 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.
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-dataset-creator
Generate synthetic and simulated datasets for evaluation and fine-tuning using Azure AI Foundry simulators. Create non-adversarial task data, adversarial safety data, and conversation datasets without manual data collection.
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.