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 reskillgit 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/reskill)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/reskill"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/reskill/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/reskill"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/reskill.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 | $0.00012 | $0.00795 |
| Opus 5.5 | $0.00005 | $0.00318 |
| Sonnet 5.5 | $0.00002 | $0.00159 |
| Haiku 4.5 | $0.00001 | $0.00080 |
Grade A, and why
reskill 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 1mo 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 reskill — 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
When the coordinator hears "team, reskill" (or similar: "optimize context", "slim down charters"), trigger a team-wide optimization pass. The goal: reduce per-agent context consumption by extracting shared patterns from charters and histories into reusable skills.
This is a periodic maintenance activity. Run whenever charter/history bloat is suspected.
Process
Step 1: Audit
Read all agent charters and histories. Measure byte sizes. Identify:
- Boilerplate — sections repeated across ≥3 charters with <10% variation (collaboration, model, boundaries template)
- Shared knowledge — domain knowledge duplicated in 2+ charters (incident postmortems, technical patterns)
- Mature learnings — history entries appearing 3+ times across agents that should be promoted to skills
Step 2: Extract
For each identified pattern:
- Create or update a skill at
.squad/skills/{skill-name}/SKILL.md - Follow the skill template format (frontmatter + Context + Patterns + Examples + Anti-Patterns)
- Set confidence: low (first observation), medium (2+ agents), high (team-wide)
Step 3: Trim
Charters — target ≤1.5KB per agent:
- Remove Collaboration section entirely (spawn prompt + agent-collaboration skill covers it)
- Remove Voice section (tagline blockquote at top of charter already captures it)
- Trim Model section to single line:
Preferred: {model} - Remove "When I'm unsure" boilerplate from Boundaries
- Remove domain knowledge now covered by a skill — add skill reference comment if helpful
- Keep: Identity, What I Own, unique How I Work patterns, Boundaries (domain list only)
Histories — target ≤8KB per agent:
- Apply history-hygiene skill to any history >12KB
- Promote recurring patterns (3+ occurrences across agents) to skills
- Summarize old entries into
## Core Contextsection - Remove session-specific metadata (dates, branch names, requester names)
Step 4: Report
Output a savings table:
| Agent | Charter Before | Charter After | History Before | History After | Saved |
|---|
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.
- 1mo ago First seen · 93 lines · 12 tokens per session scan A 40df857c11f1
reskill is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,145 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 795 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to reskill, differing in 0 lines, and is treated as a copy.
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tidy-skill
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azure-ml-dataset-creator
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azure-ml-model-evaluation
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azure-ml-llm-trainer
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