Loading...
Loading...
Found 872 Skills
Hostinger VPS API for virtual machine management, Docker projects, firewalls, SSH keys, backups, snapshots, OS templates, post-install scripts, recovery mode, malware scanning, PTR records, and metrics. Use when creating, managing, or troubleshooting VPS instances, deploying Docker containers, configuring firewalls, or managing server infrastructure.
Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.
View Stripe revenue metrics — MRR, total charges, balance, refunds, and payouts. Use when the user asks about revenue, income, MRR, charges, refunds, or financial overview.
Configure single-project Google Cloud Logging: regional log buckets, log sinks, log views, restricting or hiding sensitive logs in the default view (_Default) filter, IAM permissions for views (Logs View Accessor, IAM conditions), logs-based metrics, log exclusions, and sampling. Don't use for cross-project logging or multi-project setups.
Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.
Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU debugging.
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.
Answer a merchant's **analytics and reporting** questions with **ShopifyQL** — Shopify's query language for aggregated store metrics that the Admin GraphQL API cannot compute. Choose this (not `admin`) whenever the ask is for **numbers, totals, trends, or breakdowns** rather than fetching or mutating individual records: including but not limited to total/gross/net sales and revenue, order counts, average order value, refunds, quantity sold, sessions, conversion rate, and traffic — sliced by product, channel, region, or customer, trended over time, or compared period-over-period. Examples: "total sales last 7 days", "orders by sales channel this month", "top products by revenue", "conversion rate this week", "sales this year vs last year". This topic covers writing the ShopifyQL query; if the merchant wants to run it against their store, execution is handed off to `use-shopify-cli`. Not for general Admin GraphQL record operations — fetching or mutating individual resources (use `admin`).
Audit how agent context (CLAUDE.md / AGENTS.md / rules / skills) lines up with the code across a set of repositories and generate a self-contained HTML report — a short list of specific "things to check" (context behind the code, thin coverage for the codebase, oversized files, no per-area context), plus per-repo raw metrics and a folder tree comparing folder LOC to context coverage. Use when the user wants to audit context coverage across repos, "which repos are missing CLAUDE.md", "where is our agent context thin or stale", "context coverage across my org / projects folder", or "/context-coverage". Works on a local folder of clones or a whole GitHub org via the gh CLI.
Fetch keyword metrics (volume, KD, intent) in bulk using DataForSEO API
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
Service metrics, RED metrics (Rate, Errors, Duration), and runtime-specific telemetry for .NET, Java, Node.js, Python, PHP, and Go applications.