Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
PowerShell 7+ scripting and Windows system administration -- cross-platform automation, secure credential handling, Pester testing, DSC, and JEA patterns.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
This skill covers PowerShell 7+ (Core) for cross-platform automation, system administration, and DevOps scripting. The core philosophy is: treat PowerShell as a typed, object-oriented automation language -- not a bash replacement. Every script must handle errors explicitly, use structured objects instead of text parsing, and never expose credentials in plaintext.
$ErrorActionPreference = 'Stop' at the top of scripts -- silent failures are the primary cause of automation bugs and data loss.Microsoft.PowerShell.SecretManagement module to pull secrets from vaults.[PSCustomObject] or -OutputType JSON for structured output -- text parsing with regex is fragile and breaks on locale/format changes.Invoke-Expression (IEX) on untrusted input -- it is the PowerShell equivalent of eval() and enables arbitrary code execution.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Parsing command output with regex instead of using objects | Breaks on locale changes, format updates, and different OS versions | Use cmdlet object output directly or convert to PSCustomObject |
Using Invoke-Expression to build dynamic commands | Enables code injection; any user input can execute arbitrary PowerShell | Use splatting (@params) for dynamic parameters; use Start-Process for external commands |
| Catching all exceptions with empty catch blocks | Silently swallows errors; automation appears to succeed when it failed | Use typed catch blocks; log and re-throw unexpected exceptions |
| Using Windows PowerShell 5.1 syntax without checking compatibility | Scripts fail on Linux/macOS where PS 7 is the only option | Use $PSVersionTable.PSVersion checks; prefer PS 7 cross-platform cmdlets |
| Storing credentials in script variables or config files | Plaintext secrets in source control; credential theft risk | Use Get-Secret from SecretManagement module; inject via environment variables in CI |
#Requires -Version 7.0
#Requires -Modules @{ ModuleName='Microsoft.PowerShell.SecretManagement'; ModuleVersion='1.1.0' }
$ErrorActionPreference = 'Stop'
Set-StrictMode -Version Latest
function Invoke-DataBackup {
[CmdletBinding()]
param(
[Parameter(Mandatory)]
[ValidateNotNullOrEmpty()]
[string]$TargetPath,
[Parameter()]
[switch]$Compress
)
begin {
Write-Verbose "Starting backup to $TargetPath"
}
process {
try {
# Business logic here
}
catch [System.IO.IOException] {
Write-Error "IO error during backup: $_"
throw
}
catch {
Write-Error "Unexpected error: $_"
throw
}
}
end {
Write-Verbose "Backup complete"
}
}
# Register a vault (one-time setup)
Register-SecretVault -Name 'AzureKeyVault' -ModuleName 'Az.KeyVault'
# Retrieve secret at runtime
$apiKey = Get-Secret -Name 'MyApiKey' -Vault 'AzureKeyVault' -AsPlainText
# Use in automation (never log the value)
$headers = @{ 'Authorization' = "Bearer $apiKey" }
Invoke-RestMethod -Uri $endpoint -Headers $headers
# Process structured data through the pipeline
Get-ChildItem -Path $target -Filter *.json |
ForEach-Object {
$data = Get-Content -Path $_.FullName | ConvertFrom-Json
[PSCustomObject]@{
FileName = $_.Name
ItemCount = $data.items.Count
LastModified = $_.LastWriteTime
}
} |
Sort-Object -Property ItemCount -Descending |
Export-Csv -Path 'report.csv' -NoTypeInformation
# Invoke-DataBackup.Tests.ps1
Describe 'Invoke-DataBackup' {
BeforeAll {
. $PSScriptRoot/Invoke-DataBackup.ps1
}
Context 'When target path exists' {
It 'Should create backup file' {
$result = Invoke-DataBackup -TargetPath $TestDrive
$result | Should -Not -BeNullOrEmpty
Test-Path "$TestDrive/backup.zip" | Should -BeTrue
}
}
Context 'When target path is invalid' {
It 'Should throw IO exception' {
{ Invoke-DataBackup -TargetPath '/nonexistent/path' } |
Should -Throw -ExceptionType ([System.IO.IOException])
}
}
}
# Use Join-Path for all path operations
$configPath = Join-Path -Path $HOME -ChildPath '.config' -AdditionalChildPath 'myapp', 'settings.json'
# Check platform before using platform-specific features
if ($IsWindows) {
# Windows-specific: registry, WMI, COM
$os = Get-CimInstance -ClassName Win32_OperatingSystem
} elseif ($IsLinux -or $IsMacOS) {
# Unix-specific: /proc, systemctl
$os = uname -a
}
| Skill | Relationship |
|---|---|
devops | CI/CD pipeline integration with PowerShell scripts |
docker-compose | Containerized PowerShell automation |
terraform-infra | Infrastructure provisioning alongside PS configuration |
tdd | Test-driven development methodology for Pester tests |
Before starting:
Read .claude/context/memory/learnings.md for prior PowerShell modules, Pester testing patterns, or OS-specific workarounds.
After completing: Record new PowerShell modules, Pester testing patterns, or OS-specific workarounds to .claude/context/memory/learnings.md.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
中文简介正在准备中。当前显示原文说明。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
中文简介正在准备中。当前显示原文说明。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
中文简介正在准备中。当前显示原文说明。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
中文简介正在准备中。当前显示原文说明。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
中文简介正在准备中。当前显示原文说明。