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.
Borrowing it
Nothing to install: this file belongs to microsoft/Generative-AI-for-beginners-dotnet. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/Generative-AI-for-beginners-dotnet/main/.github/skills/secret-handling/SKILL.mdgit 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/secret-handling)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/secret-handling"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/secret-handling/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/secret-handling"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/secret-handling.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 | $0.00019 | $0.01998 |
| Opus 5 | $0.00010 | $0.00999 |
| Sonnet 5 | $0.00004 | $0.00400 |
| Haiku 4.5 | $0.00002 | $0.00200 |
Grade A, and why
secret-handling 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 10d 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 secret-handling — 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Spawned agents have read access to the entire repository, including .env files containing live credentials. If an agent reads secrets and writes them to .squad/ files (decisions, logs, history), Scribe auto-commits them to git, exposing them in remote history. This skill codifies absolute prohibitions and safe alternatives.
Patterns
Prohibited File Reads
NEVER read these files:
.env(production secrets).env.local(local dev secrets).env.production(production environment).env.development(development environment).env.staging(staging environment).env.test(test environment with real credentials)- Any file matching
.env.*UNLESS explicitly allowed (see below)
Allowed alternatives:
.env.example(safe — contains placeholder values, no real secrets).env.sample(safe — documentation template).env.template(safe — schema/structure reference)
If you need config info:
- Ask the user directly — "What's the database connection string?"
- Read
.env.example— shows structure without exposing secrets - Read documentation — check
README.md,docs/, config guides
NEVER assume you can "just peek at .env to understand the schema." Use .env.example or ask.
Prohibited Output Patterns
NEVER write these to .squad/ files:
| Pattern Type | Examples | Regex Pattern (for scanning) |
|---|---|---|
| API Keys | OPENAI_API_KEY=sk-proj-..., GITHUB_TOKEN=ghp_... |
`[A-Z_]+(?:KEY |
| Passwords | DB_PASSWORD=super_secret_123, password: "..." |
`(?:PASSWORD |
| Connection Strings | postgres://user:pass@host:5432/db, Server=...;Password=... |
`(?:postgres |
| JWT Tokens | eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... |
eyJ[A-Za-z0-9_-]+\.eyJ[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+ |
| Private Keys | -----BEGIN PRIVATE KEY-----, -----BEGIN RSA PRIVATE KEY----- |
-----BEGIN [A-Z ]+PRIVATE KEY----- |
| AWS Credentials | AKIA..., aws_secret_access_key=... |
`AKIA[0-9A-Z]{16} |
| Email Addresses | [email protected] (PII violation per team decision) |
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} |
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.
- 10d ago First seen · 201 lines · 19 tokens per session scan A b9222aa13274
secret-handling is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,057 stars, last pushed 9d ago), licensed MIT. It adds 19 tokens to every session and 1,998 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 secret-handling, 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-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-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-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.