plugins/terraform-master/skills/terraform-tasks/SKILL.md
Terraform task patterns — common workflows and pipeline steps. PROACTIVELY activate for: (1) terraform init/plan/apply/destroy workflows, (2) terraform fmt and validate in CI, (3) workspace management (terraform workspace), (4) remote state backends (Azure Storage, S3, Terraform Cloud), (5) state locking and DynamoDB locks, (6) terraform import for existing resources, (7) terraform refresh and drift detection, (8) -target and -replace use cases, (9) version pinning (required_version, required_providers), (10) tfvars file conventions for environments. Provides: workflow templates, backend configuration examples, state-locking setup, import recipes, and CI pipeline patterns.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace terraform-tasksInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
MANDATORY: Always Use Backslashes on Windows for File Paths
When using Edit or Write tools on Windows, you MUST use backslashes (\) in file paths, NOT forward slashes (/).
Examples:
D:/repos/project/file.tsxD:\repos\project\file.tsxThis applies to:
NEVER create new documentation files unless explicitly requested by the user.
This skill enables autonomous execution of complex Terraform tasks with comprehensive provider knowledge and platform awareness.
Generate complete, production-ready Terraform code for any cloud provider:
Process:
Example Tasks:
Handle Terraform and provider version upgrades:
Process:
Example Tasks:
Diagnose and fix Terraform issues:
Process:
Example Tasks:
Scan and fix security issues:
Process:
Example Tasks:
Review and improve Terraform architecture:
Process:
Example Tasks:
Create complete CI/CD pipelines:
Process:
Example Tasks:
Create reusable Terraform modules:
Process:
Example Tasks:
Migrate infrastructure to Terraform:
Process:
Example Tasks:
This skill operates autonomously with minimal user intervention:
When encountering issues:
All generated Terraform code must:
User Request: "Create an Azure Storage Account with all security best practices"
Skill Actions:
User Request: "My terraform plan is failing with authentication error on Windows"
Skill Actions:
User Request: "Review my Terraform structure, I have 1000+ resources in one state file"
Skill Actions:
This skill works in tandem with the terraform-expert agent:
Use this skill when you need to autonomously execute Terraform tasks with comprehensive provider knowledge and platform awareness.
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.