Explains Google Cloud Application Integration concepts, region provisioning, IAM roles, run-as service accounts, interface choices (REST, Python discovery client, Terraform, ADK), and platform limits. Use when setting up Application Integration in a Google Cloud project, enabling required APIs, provisioning a region, configuring IAM or run-as service accounts, choosing between Application Integration and custom code, or checking quotas and timeouts. Do not use for building, publishing, or invoking integrations (use application-integration-authoring) or diagnosing failed or stuck runs and logs (use application-integration-troubleshooting).
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Diagnoses failed, stuck, or silent Google Cloud Application Integration runs and Integration Connectors tasks using the REST API, Cloud Logging, and Cloud Monitoring. Use when an integration fails, returns HTTP 200 without doing the expected work, hangs in a suspended or retry state, or hits IAM, run-as service account, connector health, or quota errors. Do not use for initial region provisioning (use application-integration-basics) or building and deploying versions (use application-integration-authoring).
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Provisions, inspects, updates, and deletes Google Cloud Assured Workloads V1 compliant folders, CMEK settings, Sovereign Controls by Partners, and service usage restrictions. Use when creating, listing, describing, updating, or deleting an Assured Workloads workload for any compliance regime or data boundary (FedRAMP, DoD IL2/IL4/IL5, ITAR, CJIS, Healthcare, Sovereign Controls by Partners, or Regional Data Boundaries). Don't use for brownfield assessments or custom frameworks (use assuredworkloads-environment-assessor or assuredworkloads-frameworks-*), or non-compliant folders.
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Discover Google Cloud resource telemetry and effective Org Policies, evaluate brownfield workloads against sovereign data boundary and compliance standards (EU Data Boundary with Support, FedRAMP Moderate/High, DoD IL4/IL5, ITAR, CJIS, US/EU Regional Data Boundaries), and recommend the optimal Assured Workloads Framework. Use when asked to: "assess my environment for compliance", "which Assured Workloads framework should I use?", "check if my folder is ready for EU sovereignty", "evaluate compliance posture for folders/123 or projects/xyz", or "discover compliance gaps before deployment". Do NOT use for live mutating policy deployments, generating deep audit reports via Cloud Audit Manager, generating remediation scripts, or authoring custom Assured Workloads frameworks.
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Authors custom cloud controls and customized frameworks for Assured Workloads when built-in resources cannot satisfy core requirements. Use when creating or synthesizing custom data boundary resources. Don't use for browsing or mapping existing built-in frameworks (use assuredworkloads-frameworks-navigator instead).
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Acts as a Data Boundary Compliance Analyst that maps user compliance queries against Data Boundary Frameworks and cloud controls, validates matched resources, and recommends appropriate Frameworks or cloud controls. Use when mapping direct natural language workload requirements to Assured Workloads / Data Boundary Frameworks and cloud controls, browsing built-in Data Boundary frameworks, or identifying major requirement gaps. Don't use for non-Data-Boundary Compliance Manager posture assessments or direct Terraform deployment execution without framework selection.
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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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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.
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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.
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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).
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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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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).
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Creates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints. Use when writing custom scalar or aggregate functions in BigQuery, installing PyPI packages or Cloud Storage libraries in a Python or JavaScript UDF, or calling Google Cloud and external APIs from a Python UDF (WITH CONNECTION) or Remote Function. Don't use for standard BigQuery table/dataset CRUD (use bigquery-basics), DataFrames client workflows (use bigquery-bigframes), or BigQuery ML models (use bigquery-ai-ml).
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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.
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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.
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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.
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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.
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Configure single-project Cloud Logging on Google Cloud: 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.
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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.
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A folder with a SKILL.md file: a name, a description of when to use it, and instructions. Claude loads a skill only when the task matches its description.
How do I use one I find here?
Copy the folder into your project's .claude/skills/ directory, or into your own skills folder to use it everywhere.
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