AI Agents for Beginners is a course that teaches the fundamentals of building AI agents through a sequence of lessons. People learning generative AI and agent development use it to study topics and frameworks including AutoGen and Semantic Kernel. The catalogue entries provide skills, instructions, and an agent related to the course.
Borrowing it
Nothing to install: this file belongs to microsoft/ai-agents-for-beginners. 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/ai-agents-for-beginners/main/.agents/skills/local-ai-agents/SKILL.mdgit clone --depth 1 https://github.com/microsoft/ai-agents-for-beginnersWrote 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/ai-agents-for-beginners/local-ai-agents)<a href="https://agentmods.dev/skills/microsoft/ai-agents-for-beginners/local-ai-agents"><img src="https://agentmods.dev/badge/skills/microsoft/ai-agents-for-beginners/local-ai-agents/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/ai-agents-for-beginners/local-ai-agents"><img src="https://agentmods.dev/badge/skills/microsoft/ai-agents-for-beginners/local-ai-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk fail
- NVIDIA SkillSpector pass
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 | $0.00200 | $0.01160 |
| Opus 5.5 | $0.00080 | $0.00464 |
| Sonnet 5.5 | $0.00040 | $0.00232 |
| Haiku 4.5 | $0.00020 | $0.00116 |
Grade A, and why
local-ai-agents 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.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating Local AI Agents with Foundry Local and Qwen
Companion skill for Lesson 17 – Creating Local AI Agents. Use it to help a learner build an agent that reasons, calls tools, and searches documentation entirely on their own machine — no cloud inference. Ground every recommendation in the lesson content and the runnable notebook.
Triggers
Activate this skill when a learner wants to:
- Run an agent fully on-device for privacy, cost, or offline reasons.
- Serve a model locally with Foundry Local and connect via the OpenAI-compatible endpoint.
- Use a Qwen function-calling model to drive reliable local tool calls.
- Add local RAG (Chroma) or a local MCP server.
- Design a hybrid local/cloud routing strategy.
Core mental model
An SLM trades breadth for privacy, cost, and offline operation. The winning
strategy: let the SLM orchestrate and let tools do the heavy lifting. The
model does not need to know the codebase — it needs to know when to call
read_file and search_docs. That plays to an SLM's strength (bounded decisions
like tool selection) and away from its weakness (broad knowledge, long multi-hop
reasoning).
Why these specific pieces
- Foundry Local exposes an OpenAI-compatible HTTP endpoint, so cloud agent code transfers by changing only
base_url(and using a local placeholder API key). It also auto-selects the best build (CPU/GPU/NPU) for the machine. - Qwen models are trained for function calling and emit well-formed tool calls consistently — this is what turns a local chat model into a local agent.
- Chroma runs in-process and stores vectors on disk, so the whole RAG pipeline (embed → store → retrieve → reason) stays local.
- MCP is a transport, not a cloud service: an MCP server can run locally over
stdio.
Setup essentials
foundry model run qwen2.5-7b-instruct
foundry service status
from foundry_local import FoundryLocalManager
from openai import OpenAI
manager = FoundryLocalManager("qwen2.5-7b-instruct")
client = OpenAI(base_url=manager.endpoint, api_key=manager.api_key) # local placeholder
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 · 97 lines · 200 tokens per session scan A b06b343808df
local-ai-agents is a skill published in the GitHub repository microsoft/ai-agents-for-beginners (77,935 stars, last pushed 3d ago), licensed MIT. It adds 200 tokens to every session and 1,160 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
agent-data-ml-model
Agent skill for data-ml-model - invoke with $agent-data-ml-model.
agent-neural-network
Agent skill for neural-network - invoke with $agent-neural-network.
agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural.
agent-sona-learning-optimizer
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer.