2,822 skills found Β· Page 20 of 95
modelscope / FunASROpen-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
google / gke-workload-scaling-troubleshootingDiagnoses GKE HorizontalPodAutoscaler (HPA) failures β metrics showing as <unknown>, FailedGetResourceMetric / FailedGetScale / FailedComputeMetricsReplicas events, missing Pod resource requests, custom/external metrics-pipeline breakage (FailedGetExternalMetric / FailedGetCustomMetric, unavailableβ¦
google / google-cloud-filestore-autoscaleInspects Filestore capacity and utilization on Google Cloud, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization)
google / cloud-build-basicsTeaches the fundamentals of Cloud Build on Google Cloud. Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.
google / dbt-sf-to-bq-translatorTranslates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication
google / gke-backup-drConfigures Backup for GKE: the BackupRestore cluster addon, BackupPlan and RestorePlan resources, restore workflows, and CMEK-encrypted backups. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.
google / gke-cost-optimizationOptimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas
google / gke-app-onboardingManages GKE application onboarding, covering containerization, deployment manifests, and migration
google / google-cloud-solution-agentic-analytics-spark-knowledge-catalogDiscovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine)
google / bigquery-troubleshootingProvides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads
google / google-cloud-solution-build-deploy-agentsDesigns, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions
google / alloydb-basicsManages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations
google / application-design-center-design-deployProcesses GCP infrastructure design and deployment workflows within Application Design Center (ADC)
google / google-cloud-solution-agentic-ai-data-science-workflowDesigns a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices
google / google-cloud-waf-operational-excellenceGenerates operational guidance for CI/CD deployment pipelines, observability, monitoring, logging, and incident management on Google Cloud based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF)
google / gke-workload-scalingManages scaling for GKE workloads using HPA and VPA
google / bigquery-bigframesGenerates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library β use bigquery-basics.
google / bigquery-optimizationProvides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries
google / bigtable-basicsAssists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable
datawhalechina / easy-vibeπ» Vibe Coding 101ο½Your First Course For AI Product Builders.
AgriciDaniel / claude-seoUniversal SEO skill for Claude Code. 26 sub-skills + 19 sub-agents covering technical SEO, E-E-A-T, schema, GEO/AEO, agent readiness (Lighthouse Agentic Browsing, WebMCP, llms.txt), backlinks, local SEO, e-commerce, international SEO, Google APIs, and PDF/Excel reporting.
AgriciDaniel / seoComprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity.
AgriciDaniel / seo-programmaticProgrammatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention
citrolabs / ego-liteThe fastest browser for AI agents to run browser automation, built for sharing your logged-in browser state with your AI agents, like Codex or Claude Code, without disturbing you. Zero cost, zero config.
browser-use / browser-harnessLet Claude Code, Codex or Cursor use your real Chrome, logged in as you. One prompt to set up. Open source, MIT.
rowboatlabs / rowboatAI coworker with memory and collaboration
muratcankoylan / advanced-evaluationThis skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
muratcankoylan / evaluationThis skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.
muratcankoylan / project-developmentThis skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured oβ¦
muratcankoylan / self-improvement-loopsThis skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agentβ¦
