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Found 1,444 Skills
Use when monitoring account analytics, tracking growth metrics, or measuring content performance
Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.
User-facing NemoClaw guidance for installing, configuring, operating, securing, monitoring, and troubleshooting NemoClaw sandboxes. Use when users ask about NemoClaw quickstarts, OpenClaw and OpenShell relationships, local inference, remote GPU deployment, sandbox lifecycle, network policy, security posture, agent skills, command reference, or issue triage instructions.
Manage daily operations for Israeli freelancers (osek murshe, osek patur) - invoice aging with collection reminders, utility bill collection via browser automation, tax deadline alerts (VAT, Bituach Leumi, mkdamot, annual report), osek patur threshold monitoring, and organized accountant packages (havila). Use when a freelancer needs help tracking invoices, preparing documents for their accountant, monitoring their osek patur revenue ceiling, or staying on top of Israeli tax filing deadlines. Prevents missed VAT filings (which trigger automatic penalties), forgotten invoice follow-ups, and disorganized handoffs to accountants. Do NOT use for VAT return preparation (use israeli-vat-reporting), e-invoice generation (use israeli-e-invoice), or payroll/employee management.
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.
Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2.
Optimize web application performance using code splitting, lazy loading, caching, compression, and monitoring. Use when improving Core Web Vitals and user experience.
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
AWS RDS (Relational Database Service) management using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.