12,427 skills found · Page 76 of 415
google / gke-compute-classesConfigures, optimizes, and troubleshoots GKE ComputeClasses
google / google-cloud-solution-agentic-ai-bidirectional-streamingGuides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring.
google / google-cloud-solution-agentic-ai-borderless-data-lakehouseDiscovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents
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-solution-multi-agent-securityDesigns, deploys, and secures Google Cloud Agent Gateway solutions
google / google-cloud-solution-n-tier-serverless-web-appAssists in designing and implementing secure n-tier serverless web applications and microservices on Google Cloud
google / agent-platform-tuningAgent Platform Model Tuning
google / developer-device-platform-basicsProvides guidance and instructions on managing remote devices on Developer Device Platform (DDP)
google / gemini-live-apiGenerates a Gemini LiveAPI client service class in the user's chosen programming language
google / gke-ai-troubleshooting-jobset-interruptionDiagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously
google / gke-ai-troubleshooting-tpu-dynamic-slices-monitoringMonitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources
google / gke-ai-troubleshooting-tpu-metrics-monitoringMonitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL
google / gke-workload-scalingManages scaling for GKE workloads using HPA and VPA
google / google-cloud-networking-observabilityInvestigates Google Cloud networking issues by analyzing GCP logs, metrics, and diagnostics
google / agent-platform-skill-registryInteract with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
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 — 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 / gke-golden-pathProvides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns
google / google-agents-cli-onboardingOnboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to "create a new agent", "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "evaluate an agent", "deploy an agent", "publish an agent", "monitor an…
google / bigtable-basicsAssists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable
google / data-manager-api-setupGuides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API.
google / cloud-sql-basicsThis file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL.
wanshuiyin / Auto-claude-code-research-in-sleepARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
wanshuiyin / kill-argumentTwo-thread adversarial review: a fresh reviewer constructs the strongest 200-word rejection memo, then a second fresh reviewer defends the paper point-by-point and surfaces still-unresolved critical issues
wanshuiyin / citation-auditZero-context verification that every bibliographic entry in the paper is real, correctly attributed, and used in a context the cited paper actually supports — catching hallucinated authors, wrong years, fabricated venues, version mismatches, and wrong-context citations
wanshuiyin / experiment-auditAudit experiment integrity before claiming results. Uses cross-model review (external reviewer backend) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope
wanshuiyin / experiment-bridgeWorkflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results
wanshuiyin / experiment-queueSSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention
wanshuiyin / grant-proposalDraft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats
wanshuiyin / idea-discovery-robotWorkflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas
