SKILLEMALL.ai

BC azure-container-registry-cli

Manage Azure Container Registry via the az acr CLI including registries, images, cloud builds, ACR Tasks, authentication, tokens, geo-replication, and networking. Use when working with ACR, az acr commands, pushing/importing/purging container images in Azure, or when the user mentions Azure Container Registry.

Not recommendedcritical or high security findings
github/awesome-copilot Agent Skills author: github MIT 5 files body ≈ 1 156 tokens Open the sourcegithub.com analyzed 8 h ago

Manage Azure Container Registry via the az acr CLI including registries, images, cloud builds, ACR Tasks, authentication, tokens, geo-replication, and…

As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

IntegrationAzureDockerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
77
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 2

  • high Dangerous commands cmd-pipe-to-shell SKILL.md:17
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash  # Linux
Medium and low: 1
  • medium Dangerous commands cmd-privilege SKILL.md:17
    Privilege escalation / world-writable permissions
    curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash  # Linux

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

✓ No remarks against the Agent Skills spec

Process rating: all ten parameters 64/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1156 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

  • +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 311: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 4 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.