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BF set-app-registration-native

Use when the user wants to wire a Power Apps Wrap mobile app to an Entra ID app registration by opening the Power Apps Wrap app-registration page, pasting the resulting client ID, and updating auth.config.json.

microsoft/power-platform-skills Agent Skills author: microsoft 1 file body ≈ 1 299 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Use when the user wants to wire a Power Apps Wrap mobile app to an Entra ID app registration by opening the Power Apps Wrap app-registration page, pasting the…

As a process F 50/100 · Will not run — References files that are not bundled: ${PLUGIN_ROOT}/shared/shared-instructions.md

ProcedureWriting and documentstype 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
B
88/100
safety, quality, tests
Safety 60%
95
Quality 40%
78
Run on models
none yet
Process rating
F
50/100
Will not run
References files that are not bundled: ${PLUGIN_ROOT}/shared/shared-instructions.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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Edit Write Grep Glob Bash AskUserQuestion

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

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ${PLUGIN_ROOT}/shared/shared-instructions.md

Process rating: all ten parameters 50/100

Will not run. References files that are not bundled: ${PLUGIN_ROOT}/shared/shared-instructions.md
  • 0Tools and files. 1 referenced file(s) missing: ${PLUGIN_ROOT}/shared/shared-instructions.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 3 mutating operations with no state check
  • 60Failures and branches. 2 branches
  • 100Steps. 10 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1299 tokens
  • 100Progress reporting. Reports progress

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 210: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (10 code blocks)

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