SKILLEMALL.ai

AF azure-architecture-autopilot

Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

github/awesome-copilot Agent Skills author: github MIT 14 files body ≈ 1 712 tokens Open the sourcegithub.com analyzed 23 h ago

Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through…

As a process F 40/100 · Will not run — References files that are not bundled: references/*.md, assets/06-architecture-diagram.png, assets/07-azure-portal-resources.png

AnalyzerAzureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: references/*.md, assets/06-architecture-diagram.png, assets/07-azure-portal-resources.png
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/*.md
  • warning missing-ref reference to a missing file: assets/06-architecture-diagram.png
  • warning missing-ref reference to a missing file: assets/07-azure-portal-resources.png
  • warning missing-ref reference to a missing file: assets/08-deployment-succeeded.png

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: references/*.md, assets/06-architecture-diagram.png, assets/07-azure-portal-resources.png
  • 0Tools and files. 4 referenced file(s) missing: references/*.md, assets/06-architecture-diagram.png, assets/07-azure-portal-resources.png
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 8 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Steps. 8 steps, 4 vague phrases
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1712 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 636: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (9 of 10)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.