Neurolink Guide
juspay/neurolink
Guide for using the NeuroLink SDK and CLI. An agent skill from juspay/neurolink.
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models.
Install Local AI Agents
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agents --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/local-ai-agents .claude/skills/local-ai-agents && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .claude/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agents --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/local-ai-agents .agents/skills/local-ai-agents && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .agents/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agents --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/local-ai-agents .cursor/skills/local-ai-agents && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .cursor/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/microsoft/ai-agents-for-beginners.git --path .agents/skills/local-ai-agents--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agents --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/local-ai-agents .gemini/skills/local-ai-agents && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .gemini/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/local-ai-agents .github/skills/local-ai-agents && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .github/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install microsoft/ai-agents-for-beginners local-ai-agents --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/local-ai-agents .opencode/skills/local-ai-agents && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "local-ai-agents" agent skill from https://github.com/microsoft/ai-agents-for-beginners/tree/main/.agents/skills/local-ai-agents into .opencode/skills/local-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-ai-agents", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
local-ai-agents- GitHub stars
- 77k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 525 words
- Files
- 1
- Skills in repo
- 123
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models.
- : run an agent locally
- SKILL.md covers Triggers, Core mental model, Why these specific pieces and Setup essentials, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- On-device agent
What it does
Local AI Agents is an agent skill from microsoft/ai-agents-for-beginners, published by the product's own GitHub organization. Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local…
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Structured output and tool calling, MCP servers and GPU and accelerator computing. It works with Model Context Protocol, Qwen, OpenAI and Chroma. The repository describes itself as: 18 Lessons to Get Started Building AI Agents. The licence is MIT.
When your agent uses it
- : run an agent locally
- On-device agent
- Qwen function calling
- Local tool calling
Example prompts
- “/local-ai-agents”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 25b7985. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Local AI Agents loads about 1.3k tokens when it runs. Until then it costs about 233 tokens; SKILL.md has 525 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from microsoft/ai-agents-for-beginners at commit 25b7985, republished under its MIT licence (© microsoft). 525 words, ~1,250 tokens.
Download SKILL.mdSave it as
.claude/skills/local-ai-agents/SKILL.md (or your agent's skills folder).- name
- local-ai-agents
- description
- Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
- license
- MIT
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 statusfrom 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~8 GB RAM is a realistic minimum; a GPU/NPU helps but is not required.
Show full SKILL.md (233 more words)Show less
Key patterns to reproduce
Point the learner at the notebook
17-local-agent-foundry-local.ipynb:
- Sandboxed tools: every file tool resolves paths and rejects anything outside a single project root — even locally, a tool runs with the user's permissions.
- Tool-calling loop: register tools with the OpenAI tools schema, execute requested tools locally, feed results back, repeat until a final answer.
- Local RAG: upsert docs into a Chroma collection;
search_docsreturns top-k chunks. - Local MCP: connect to a local server over
stdio; scope it to a project directory and validate its outputs.
Hybrid routing (local as one of the models)
| Situation | Where it runs |
|---|---|
| Sensitive data / offline | Local SLM |
| Simple, bounded task | Local SLM (cheap, fast) |
| Hard multi-hop reasoning on non-sensitive data | Cloud model |
| Cloud outage | Local SLM (graceful degradation) |
This mirrors the model-routing idea from Lesson 16, with the workstation as one of the routes. Prefer designs that fall back to local so the agent degrades in quality rather than failing outright.
Guardrails for the assistant
- Keep every file/tool operation scoped to a sandboxed project directory.
- Do not send code or data to the cloud when the learner's stated goal is privacy/offline — keep the whole pipeline local.
- Set realistic expectations for SLM quality; lean on tools and RAG rather than the model's memorised knowledge.
- Note that Lesson 17 has no Foundry Responses endpoint, so the cloud smoke-test action does not apply — validate by running the notebook locally.
© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .agents/skills/local-ai-agents of microsoft/ai-agents-for-beginners.
Open the folder on GitHubat commit 25b7985
Compare with similar skills
Local AI Agents next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Local AI Agents this skillmicrosoft/ai-agents-for-beginners | 77k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Neurolink Guidejuspay/neurolink | 143 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Tool Designagentailor/fullstack-langgraph-nextjs-agent | 132 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Qianwenai Wikichujianyun/skills | 740 | — | ~717 | Automated safety check: Pass | Custom licence | |
| Spring AI Integrationrrezartprebreza/spring-boot-skills | 298 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Spring AI Integrationrrezartprebreza/spring-boot-skills | 298 | 1 repos | ~2.5k | Automated safety check: Pass | MIT |
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Questions about Local AI Agents
What does Local AI Agents do?
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Local AI Agents is an agent skill from microsoft/ai-agents-for-beginners, published by the product's own GitHub organization. Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models.
When should I use Local AI Agents?
Local AI Agents fits situations like: : run an agent locally; on-device agent; qwen function calling; local tool calling.
How do I install Local AI Agents in Claude Code?
Run `npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a claude-code`. Or copy the skill folder (.agents/skills/local-ai-agents in microsoft/ai-agents-for-beginners) into .claude/skills/local-ai-agents in your project. Claude Code loads it when a task matches its description.
How do I install Local AI Agents in Codex?
Run `npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a codex`. Or copy the skill folder (.agents/skills/local-ai-agents in microsoft/ai-agents-for-beginners) into .agents/skills/local-ai-agents in your project. Codex loads it when a task matches its description.
Can I use Local AI Agents in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/ai-agents-for-beginners --skill local-ai-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/local-ai-agents, .gemini/skills/local-ai-agents, .github/skills/local-ai-agents and .opencode/skills/local-ai-agents in your project.
What does Local AI Agents need to run?
SKILL.md names no scripts, command-line tools or credentials: Local AI Agents is instructions for the agent only. Our summary lists: Python 3.
Does Local AI Agents access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Local AI Agents safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Local AI Agents use?
Local AI Agents is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Local AI Agents use?
About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Local AI Agents?
Skills that share tags, products or a category with Local AI Agents: Neurolink Guide (juspay/neurolink, 143 stars), Tool Design (agentailor/fullstack-langgraph-nextjs-agent, 132 stars), Qianwenai Wiki (chujianyun/skills, 740 stars) and Spring AI Integration (rrezartprebreza/spring-boot-skills, 298 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Local AI Agents?
microsoft (a GitHub organization, an official publisher) maintains it in microsoft/ai-agents-for-beginners, which has 76,612 GitHub stars. The repository holds 123 skills in this directory. The repository was last updated on September 19, 2026.
Source: microsoft/ai-agents-for-beginners on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.