> Create or review Stratus Red Team attack techniques. Use when asked to add, create, or implement a new attack technique for AWS, Azure, GCP, Entra ID, EKS, or Kubernetes in the stratus-red-team project. Also use when reviewing PRs or code that implements attack techniques — the guidelines serve as a review checklist.
npx skills add https://github.com/DataDog/stratus-red-team --skill create-attack-technique
Each attack technique is composed of two files, which should be stored in v2/internal/attacktechniques/<platform>/<mitre-attack-tactic>/<name> (e.g., v2/internal/attacktechniques/aws/defense-evasion/cloudtrail-delete/):
main.go, containing the imperative attack logicmain.tf containing prerequisite infrastructure.The lifecycle of an attack technique in Stratus Red Team is:
When creating a new technique, follow the workflow below step by step. When reviewing a PR or existing technique, use the guidelines below as a checklist to verify correctness and consistency.
See references/provider-configs.md for the required Terraform provider blocks for each platform (AWS, Azure, Entra ID, GCP, Kubernetes).
When you're done, format your Terraform file using:
terraform fmt -write v2/internal/attacktechniques/<platform>/<tactic>/<name>/main.tf
detonate and revert functions, e.g. params["output_name"].display output is displayed back to the user on the CLI. Example:output "display" {
value = format("%s Secrets Manager secrets ready", local.num_secrets)
}
See assets/sample-attack-technique.go
cloudtrail.NewFromConfig(providers.AWS().GetConnection())client, err := armnetwork.NewClientFactory(providers.Azure().SubscriptionID, providers.Azure().GetCredentials(), providers.Azure().ClientOptions)service, err := iam.NewService(ctx, providers.GCP().Options())detonate and revert — never use log.Fatalf().fmt.Errorf("failed to <action>: %w", err) for error wrapping.log.Println.If the detonation is reversible, implement a revert function that undoes the changes made by detonate. This allows the technique to be cleaned up after use. The revert function has the same signature as detonate: func revert(params map[string]string, providers stratus.CloudProviders) error. See assets/sample-attack-technique.go for an example.
ID should always be of the form platform.mitre-attack-tactic.name, e.g. aws.defense-evasion.cloudtrail-deleteFriendlyName should always start with a verb, and be in the infinitive form.S3 ransomware, Creates S3 ransomwareSimulate S3 ransomwareDescription should contain at least an intro sentence and a Warm-up, Detonation, References section. "References" should ideally be examples of usage/sightings of this technique in the wild, or relevant cloud provider documentation. Example:Establishes persistence by creating a service account key on an existing service account.
Warm-up:
- Create a service account
Detonation:
- Create a new key for the service account
References:
- https://expel.com/blog/incident-report-spotting-an-attacker-in-gcp/
- https://rhinosecuritylabs.com/gcp/privilege-escalation-google-cloud-platform-part-1/
Detection should describe how to detect this technique, including relevant CloudTrail/audit log event names and any managed detection rules (e.g. GuardDuty finding types). Use HTML for formatting since it renders in the docs. Example:Identify when a CloudTrail trail is disabled, through CloudTrail's <code>StopLogging</code> event.
GuardDuty also provides a dedicated finding type, <a href="https://docs.aws.amazon.com/guardduty/latest/ug/guardduty_finding-types-iam.html#stealth-iam-cloudtrailloggingdisabled">Stealth:IAMUser/CloudTrailLoggingDisabled</a>.
IsIdempotent: set to true if the detonation can be called multiple times without side effects.Add a new import corresponding to your new Go file in v2/internal/attacktechniques/main.go.
Run:
cd v2 # if you're not already in there
go fmt ./...
make docs
Assess 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.
Take datadog/create-attack-technique from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.