Skill UI

Helps understand and write EAS workflow YAML files for Expo projects. Use for CI/CD or workflows in an Expo/EAS context, .eas/workflows/, EAS build pipelines, or deployment automation.

Automate software development workflows including build, test, and deployment directly within GitHub repositories. Configure CI/CD pipelines, manage secrets, cache dependencies, and utilize reusable workflows for efficient DevOps practices. Requires understanding of YAML and platform CLIs.

This skill acts as an expert CI/CD diagnostician for GitHub Actions. It analyzes failed workflow logs and pipeline definitions to identify root causes like missing secrets, environment mismatches, or syntax errors. It provides precise YAML or code changes to fix the pipeline, optimize performance, and update deprecated actions while ensuring security best practices.

This skill operates HyperExecute end-to-end for TestMu AI and LambdaTest cloud test execution. It helps analyze projects, create and validate YAML configurations, run CLI jobs, debug failures, and integrate CI/CD pipelines. It ensures secure credential handling and provides troubleshooting support for test automation workflows.

Implement Kubernetes security contexts, Pod Security Standards, network policies, and RBAC to harden clusters and workloads. Includes YAML examples for restricted pods, default-deny networking, and least-privilege access.

Deploy, scale, and manage Kubernetes workloads with YAML examples for Deployments, Services, Ingress, ConfigMaps, Secrets, HPA, storage, and kubectl debugging. Suitable for DevOps engineers and developers operating containerized applications.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Analyzes data coverage gaps and creates monitors for warehouse tables and AI agents. Covers use-case analysis, data monitor creation, monitors-as-code YAML, and agent observability.

Nika is an open-source Rust engine that captures repeatable AI tasks as checkable YAML workflow files. It audits workflows before token spending, enforces strict cost budgets, and executes against local or cloud providers. Ideal for daily digests, ETL, and multi-step pipelines requiring audit trails and deterministic execution without autonomous coding capabilities.

Train and fine-tune transformer language models with Hugging Face TRL via CLI. Covers SFT, DPO, GRPO, RLOO, KTO and reward model training, plus LoRA adapters, YAML configs and distributed multi-GPU runs.

This skill enables splitting extracted text into optimized chunks for LLM context windows or RAG ingestion pipelines. It supports configurable chunk sizes, overlap settings, and various chunker types including text, markdown, YAML, and semantic splitting. Users can utilize token-based sizing for specific models or standalone commands for pre-existing text. Ideal for preparing documents for vector stores or summarization tasks.