SKILLEMALL.ai
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Fanya uchunguzi kwenye nyaraka rasmi za Microsoft ili upate dhana, mafunzo, na mifano ya msimbo katika Azure, .NET, Agent Framework, Aspire, VS Code, GitHub, na zaidi. Inatumia Microsoft Learn MCP kama chaguo-msingi, na Context7 na Aspire MCP kwa maudhui yanayopatikana nje ya learn.microsoft.com.

The skillemall take

Promises to search Microsoft's official docs—Azure, .NET, Agent Framework, and more. Uses Microsoft Learn MCP as the primary source, pulls from Context7 and Aspire MCP for content outside learn.microsoft.com.

Quality score of 72 looks decent, but process score dropped to 53—reproducibility is shaky. No critical errors found, which is suspicious for a one-file skill with 1356 tokens. Never ran on sandbox or models. Platform support is broad: Claude to DeepSeek.

Install if you need Microsoft ecosystem references and can tolerate incomplete visibility into how it actually works.

microsoft/ai-agents-for-beginners Agent Skills author: microsoft MIT 1 file body ≈ 1 356 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Fanya uchunguzi kwenye nyaraka rasmi za Microsoft ili upate dhana, mafunzo, na mifano ya msimbo katika Azure, .NET, Agent Framework, Aspire, VS Code, GitHub…

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 1356 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 297: 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.