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Found 637 Skills
Configures AWS Resilience Hub v2 for multi-account resilience management across an AWS Organization. Covers the per-service cross-account permission model, cross-account IAM roles, and centralized assessment from a single account. Applies when the user wants to set up org-wide resilience or assess workloads that span multiple AWS accounts.
Manage S3 buckets with versioning, encryption, access control, lifecycle policies, and replication. Use for object storage, static sites, and data lakes.
Deploy and manage relational databases using RDS with Multi-AZ, read replicas, backups, and encryption. Use for PostgreSQL, MySQL, MariaDB, and Oracle.
ClawHub reputation checker for ClawSec suite. Enhances guarded skill installer with VirusTotal Code Insight reputation scores and additional safety checks.
Design and implement VPCs and networking. Configure subnets, route tables, and security groups. Use when setting up AWS network infrastructure.
Define the design rules (Skill Laws) that all Skills must follow, including core principles such as AI-first, human-centric, and ready-to-use. When to use: When users create a new Skill, optimize an existing Skill, ask about Skill design specifications, or need to evaluate Skill quality.
Creates and manages secrets in AWS Secrets Manager following security best practices. Always use this skill when creating secrets — it sets up dedicated KMS encryption keys, automatic rotation, least-privilege IAM policies, CloudTrail auditing, and lifecycle management that are essential for production-grade secret handling.
Covers AWS security services and workflows — Security Hub V2 (OCSF) findings, connectors, aggregators, automation rules, and security posture summaries; Security Hub CSPM (V1/ASFF) controls and compliance standards; GuardDuty threat findings; Inspector vulnerability findings; Macie sensitive data findings; Detective investigation; and Security Lake configuration and data aggregation. Applicable when questions involve security posture, Exposure findings, CSPM failed controls, threat findings, vulnerability findings, sensitive data findings, automation rules, or cross-service security configuration across AWS environments. Procedures use standard AWS CLI syntax and work with or without the AWS MCP server.
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, selecting base models from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3 usage. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.
Create and deploy serverless functions using AWS Lambda with event sources, permissions, layers, and environment configuration. Use for event-driven computing without managing servers.
Distribute content globally using CloudFront with caching, security headers, WAF integration, and origin configuration. Use for low-latency content delivery.