GITHUB AGENT SKILLS / 技能收录
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我们从 GitHub 持续收录、审核并分类优质的 Agent Skills，按职业与使用场景整理，复制一行命令即可安装到 Claude Code、Cursor 等智能体。
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Garry Tan
@garrytan全部 Skills 196837
cloud-run-alert-configuration
Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
cloud-operator
Google Cloud (GCP) assistant and operator via the autocloud MCP server. Use for any GCP request: how-to, resources, Terraform/Kubernetes/Helm, rollouts, incidents, costs. Invoke it before answering a GCP question from your own knowledge or documentation search, and before proposing an architecture or backend, even when no cloud is named. Invoke it before reading or editing local *.tf or Kubernetes YAML. Don't use for non-GCP tasks or local machine setup like installing gcloud and setting up GCP.
cloud-monitoring-promql-query
Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
cloud-monitoring-metric-selection
Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
cloud-monitoring-list-time-series-request
Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
cloud-monitoring-chart-generation
Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.
cloud-logging-query-generation
Generates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.
cloud-logging-finops
Audits Cloud Logging costs for a Google Cloud project: the charges for log entries written to log buckets, the services, log streams and severities that drive them, and the log sinks that route them. Reconciles Cloud Billing export costs with Cloud Monitoring volume metrics, and generates a FinOps report with charts and copy-paste exclusion filters. Use when auditing Cloud Logging spend, finding logging waste, identifying top logging contributors, generating log cost reduction reports, or creating log sink exclusion filters. Don't use for querying log entries (use cloud-logging-query-generation), configuring Cloud Monitoring alerts, or querying metrics/traces.
cloud-logging-cross-project-configuration
Configure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.
cloud-databases-onboarding
Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.
cloud-build-basics
Teaches 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.
cloud-armor-crs-rule-upgrader
Upgrades Google Cloud Armor security policies from OWASP CRS 3.0 and 3.3 to OWASP CRS 4.22. Preserves signature opt-in and opt-out lists, translates evaluatePreconfiguredExpr to native evaluatePreconfiguredWaf dictionary parameters, remaps signature IDs to v042200, drops retired signature IDs, and updates structured preconfiguredWafConfig fine-tuning exclusions. Evaluates operational risk (High Breaking Change, Medium Attention Required, Low Clean Upgrade) including Method Enforcement breaking changes (PUT/PATCH/DELETE blocking) and sensitivity coverage expansions. Supports both in-place rule upgrades and smooth preview mode deployments with priority spacing and a 200-rule quota guardrail. Use when a user or agent needs to audit, upgrade, or transition Cloud Armor WAF rules to CRS 4.22. Don't use for non-WAF Cloud Armor rules (IP rate-limiting, geo-blocking, reCAPTCHA) or AWS/Azure WAF migrations.
bigtable-basics
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
bigquery-troubleshooting
Provides 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. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).
bigquery-optimization
Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine). Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).
bigquery-migration-sql-translator
Translates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery GoogleSQL using the BigQuery Migration Service (BQMS) MCP server. Use when translating legacy SQL queries, migrating schemas or metadata zip archives, or configuring BigQuery Migration Service (BQMS) YAML translation settings. Don't use for executing BigQuery queries or managing datasets and tables (use bigquery-basics), diagnosing runtime job errors (use bigquery-troubleshooting), or tuning slot reservations (use bigquery-optimization).
bigquery-bigframes
Generates 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.
bigquery-basics
Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.