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CF azure-cloud-adoption-framework

Expert guidance for planning and executing cloud adoption using Azure Cloud Adoption Framework. Covers strategy, planning, readiness & landing zones, adoption patterns, governance, security, operations & management, organization & teams, and adoption scenarios. Use when planning Azure landing zones, AVD, AVS, SAP, Oracle, AKS, Arc, or Citrix workloads, and other Azure Cloud Adoption Framework related development tasks.

MicrosoftDocs/Agent-Skills Agent Skills author: MicrosoftDocs CC-BY-4.0 1 file body ≈ 16 437 tokens Open the sourcegithub.com↗ analyzed 12 d ago

Expert guidance for planning and executing cloud adoption using Azure Cloud Adoption Framework.

As a process F 25/100 · No process to follow — References files that are not bundled: security.md

ReferenceAzureInfrastructuretype 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
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
F
25/100
No process to follow
References files that are not bundled: security.md
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 SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. 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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 16437 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: security.md

Process rating: all ten parameters 25/100

Will not run. References files that are not bundled: security.md
  • 0Tools and files. 1 referenced file(s) missing: security.md
  • 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
  • 10Execution cost. Instruction body is 16437 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 35 mutating operations with no state check
  • 50Steps. 2 steps
  • 50Failures and branches. 0 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 422: enough signal without eating the budget
  • +4Structure: 12 headings

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.