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공식 Microsoft 문서를 조회하여 Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등 다양한 분야의 개념, 튜토리얼 및 코드 예제를 찾습니다. 기본적으로 Microsoft Learn MCP를 사용하며, learn.microsoft.com 외부에 있는 콘텐츠에 대해서는 Context7 및 Aspire MCP를 사용합니다.

The skillemall take

This skill connects Microsoft official documentation through three channels: Microsoft Learn MCP for primary content, Context7 and Aspire MCP for external resources. Promises access to Azure, .NET, Agent Framework, Aspire, VS Code, and GitHub.

Tests show quality at 72 out of 100, process score at 53. No critical errors or broken links found. However, the skill exists only as description: no actual model runs, no sandbox testing. Single file, no scripts. This means MCP server integration is documented but its real-world behavior remains untested.

Install if you need a Microsoft stack reference and accept that the three data sources may behave differently in practice. Production use requires your own testing.

microsoft/ai-agents-for-beginners Agent Skills author: microsoft MIT 1 file body ≈ 999 tokens Open the sourcegithub.com↗ analyzed 6 d ago

공식 Microsoft 문서를 조회하여 Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등 다양한 분야의 개념, 튜토리얼 및 코드 예제를 찾습니다. 기본적으로 Microsoft Learn MCP를 사용하며…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubVS CodeAzureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 999 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 209: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.