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local-ai-agents

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.

71

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-organized, mostly lean, and actionable, with executable setup code, a routing decision table, and explicit guardrails including a noted exception (no Foundry Responses endpoint). It falls just short of top marks on conciseness and actionability because some patterns are explained in prose rather than full runnable code, and progressive disclosure is limited by the absence of in-bundle reference files.

Suggestions

Tighten the 'Why these specific pieces' bullets so each states only the decision-driving fact (e.g. drop 'MCP is a transport, not a cloud service') to push conciseness toward 5.

Add a short executable snippet for the tool-calling loop (register tools schema → execute → feed results → repeat) or for Chroma upsert/top-k so the core agent pattern is copy-paste ready, not just described.

If fuller RAG/MCP/tool-loop detail exists, surface it as a one-level-deep reference file under references/ so progressive disclosure can score 5 against a real bundle structure.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — the mental model ('let the SLM orchestrate and let tools do the heavy lifting') and the 'Why these specific pieces' bullets add decision rationale rather than padding; a couple of sentences (e.g. 'MCP is a transport, not a cloud service') lightly restate known facts, keeping it just below a 5.

4 / 5

Actionability

Concrete executable bash and Python setup code is copy-paste ready ('foundry model run qwen2.5-7b-instruct', the FoundryLocalManager + OpenAI client wiring), and key patterns (sandboxed path resolution, tool-calling loop, Chroma top-k, MCP over stdio) are described concretely; the loop/RAG patterns are prose rather than full executable code, which is the minor gap below 5.

4 / 5

Workflow Clarity

The tool-calling loop is sequenced (register → execute → feed back → repeat) and the hybrid-routing table plus guardrails give explicit decision checkpoints and validation guidance ('validate by running the notebook locally'); no explicit validate→fix→retry feedback loop is present, which is the gap below 5, and the primary workflow is non-destructive so the destructive cap does not apply.

4 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent), but the body is well-sectioned under ~80 lines and its single external pointer (the companion lesson notebook) is clearly signaled and one level deep; the reference target sits outside the skill bundle rather than being a curated in-bundle file, which is the minor organization gap below 5.

4 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, well-triggered, complete, and distinctive. It states concrete capabilities, supplies natural trigger phrases, answers both what and when, and uses explicit use/don't-use boundaries to avoid collisions with sibling skills. Voice is third person throughout, incurring no penalty.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — 'Foundry Local', 'OpenAI-compatible local endpoint', 'sandboxed local tools', 'local RAG with Chroma', 'local MCP servers', 'hybrid cloud/local routing' — giving comprehensive coverage of the domain rather than a single vague action.

5 / 5

Completeness

It explicitly answers 'what' (covers SLMs, endpoint, tools, RAG, MCP, routing, privacy/cost/offline trade-offs) and 'when' via the USE FOR trigger list plus a DO NOT USE FOR boundary, matching the score-5 anchor.

5 / 5

Trigger Term Quality

The USE FOR clause supplies many natural phrases a user would actually say — 'run an agent locally', 'offline agent', 'on-device agent', 'privacy-preserving agent', 'engineering assistant on my machine' — with synonym coverage; no file extensions apply to this domain.

5 / 5

Distinctiveness Conflict Risk

Clear niche (local/on-device agents via Foundry Local + Qwen) and the DO NOT USE FOR clause disambiguates from cloud-deployment (Lesson 16) and first-agent (Lesson 01) skills, leaving minimal conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 2 suspicious

Warning

Total

15

/

16

Passed

Repository
microsoft/ai-agents-for-beginners
Reviewed

Table of Contents

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