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/agent-collaboration/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/agent-collaboration)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/agent-collaboration"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/agent-collaboration.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 | $0.00022 | $0.00440 |
| Opus 5 | $0.00011 | $0.00220 |
| Sonnet 5 | $0.00004 | $0.00088 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
agent-collaboration 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 7d 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 agent-collaboration — 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
Every agent on the team follows identical collaboration patterns for worktree awareness, decision recording, and cross-agent communication. These were previously duplicated in every charter's Collaboration section (~300 bytes × 18 agents = ~5.4KB of redundant context). Now centralized here.
The coordinator's spawn prompt already instructs agents to read decisions.md and their history.md. This skill adds the patterns for WRITING decisions and requesting help.
Patterns
Worktree Awareness
Use the TEAM ROOT path provided in your spawn prompt. All .squad/ paths are relative to this root. If TEAM ROOT is not provided (rare), run git rev-parse --show-toplevel as fallback. Never assume CWD is the repo root.
Decision Recording
After making a decision that affects other team members, write it to:
.squad/decisions/inbox/{your-name}-{brief-slug}.md
Format:
### {date}: {decision title}
**By:** {Your Name}
**What:** {the decision}
**Why:** {rationale}
Cross-Agent Communication
If you need another team member's input, say so in your response. The coordinator will bring them in. Don't try to do work outside your domain.
Reviewer Protocol
If you have reviewer authority and reject work: the original author is locked out from revising that artifact. A different agent must own the revision. State who should revise in your rejection response.
Anti-Patterns
- Don't read all agent charters — you only need your own context + decisions.md
- Don't write directly to
.squad/decisions.md— always use the inbox drop-box - Don't modify other agents' history.md files — that's Scribe's job
- Don't assume CWD is the repo root — always use TEAM ROOT
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.
- 7d ago First seen · 43 lines · 22 tokens per session scan A e1943766f6aa
agent-collaboration is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,048 stars, last pushed 6d ago), licensed MIT. It adds 22 tokens to every session and 440 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 agent-collaboration, 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.