Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.
npx skills add https://github.com/Dokhacgiakhoa/Agent-Skills-4-Vibe-Coding-CLI --skill bash-pro
set -Eeuo pipefail and proper error trappingfor f in $(ls)[[ ]] for Bash conditionals, fall back to [ ] for POSIX compliancegetopts and usage functionsmktemp and cleanup trapsprintf over echo for predictable output formatting$() instead of backticks for readabilityshopt -s inherit_errexit for better error propagation in Bash 4.4+IFS=$'\n\t' to prevent unwanted word splitting on spaces: "${VAR:?message}" for required environment variables-- and use rm -rf -- "$dir" for safe operations--trace mode with set -x opt-in for detailed debuggingxargs -0 with NUL boundaries for safe subprocess orchestrationreadarray/mapfile for safe array population from command outputSCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd -P)"find -print0 | while IFS= read -r -d '' file; do ...; done#!/usr/bin/env bash shebang for portability across systems(( BASH_VERSINFO[0] >= 4 && BASH_VERSINFO[1] >= 4 )) for Bash 4.4+ featurescommand -v jq &>/dev/null || exit 1case "$(uname -s)" in Linux*) ... ;; Darwin*) ... ;; esacsed -i vs sed -i '')--verbose instead of -vvalidate_input_file not check_filefunction_name() {readonly to prevent accidental modificationlocal keyword for all function variables to avoid polluting global scopetimeout for external commands: timeout 30s curl ... prevents hangs[[ -r "$file" ]] || exit 1<(command) instead of temporary files when possible[[ $num =~ ^[0-9]+$ ]]eval on user input; use arrays for dynamic command construction(umask 077; touch "$secure_file")-- to separate options from arguments: rm -rf -- "$user_input": "${REQUIRED_VAR:?not set}"trap to ensure cleanup happens even on abnormal exitwhile read instead of for i in $(cat file)[[ ]] instead of test, ${var//pattern/replacement} instead of sedsed with multiple expressions)mapfile/readarray for efficient array population from command output$(( )) instead of expr for calculationsprintf over echo for formatted output (faster and more reliable)xargs -P for parallel processing when operations are independent--help and -h flags showing usage, options, and examples--version flag displaying script version and copyright informationshdoc from special comment formatsshellman for system integration${var@U} uppercase conversion, ${var@L} lowercase${parameter@operator} transformations, compat shopt options for compatibilityvarredir_close option, improved exec error handling, EPOCHREALTIME microsecond precision[[ ${BASH_VERSINFO[0]} -ge 5 && ${BASH_VERSINFO[1]} -ge 2 ]]${parameter@Q} for shell-quoted output (Bash 4.4+)${parameter@E} for escape sequence expansion (Bash 4.4+)${parameter@P} for prompt expansion (Bash 4.4+)${parameter@A} for assignment format (Bash 4.4+)wait -n to wait for any background job (Bash 4.3+)mapfile -d delim for custom delimiters (Bash 4.4+)shellcheck-problem-matchers for inline annotations.pre-commit-config.yaml with shellcheck, shfmt, checkbashismsshellcheck *.sh && shfmt -d *.sh && bats test/gitleaks or trufflehog to prevent credential leakslogger command for system log integrationlog_info() { logger -t "$SCRIPT_NAME" -p user.info "$*"; echo "[INFO] $*" >&2; }--help and provide clear usage informationAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
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