Claude Skills by hamzabellouch

Architect, index, and optimize spatial databases using PostgreSQL and PostGIS. Master geometry vs geography data types, spatial reference systems (SRID 4326 vs 3857), GiST and SP-GiST indexing, spatial joins, K-Nearest Neighbor (KNN) distance queries, and spatial clustering with ST_ClusterDBSCAN. Trigger when designing GIS schemas, optimizing geo-queries, or processing spatial datasets.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

Generates Logging Query Language (LQL) queries for Google Cloud Logging 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 Spanner.

Retrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP 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.

Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).

This 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. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for re...

Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery qu...

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.

Interacts with Google Cloud services using the gcloud CLI safely and efficiently. Covers command validation, data reduction, safety guardrails with a denylist, and workflows for discovery and investigation. You MUST read this skill before invoking any gcloud command. Use when managing cloud resources, querying configurations, or troubleshooting issues via gcloud. Don't use when writing or debugging Google Cloud client library code or raw REST/gRPC API interactions.

Configures GKE Backup Plans and restore workflows. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.

Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).

Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up node autoscaling specifically (use gke-scaling instead).

Plans, configures, and manages GKE networking. Covers private clusters, VPC- native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning. Use when designing GKE networking layouts, configuring private clusters, setting up Gateway API, planning GKE IP ranges, or configuring GKE ingress/egress. Don't use for basic application routing that does not require dedicated network configuration.

Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.

Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.

Guides agents through a structured 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to opinionated best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage buckets, Comp...

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.

Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.

Deploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-rec...

Guides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.

Guides 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. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple te...

Guides agents to discover requirements and design a governed, secure borderless open data lakehouse with agentic AI integration. Use when designing a multi-product architecture that connects data silos to AI agents, joining data across clouds, or running federated queries across Google Cloud and external data sources, including on-premises or other cloud providers. Don't use for simple single-cloud data warehouses or non-AI workloads.

Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is accessed through federation mechanisms such as Apache Iceberg, other "zero-copy ETL" methods, or remote query push-down. Use this skill when designing an architecture for efficient a...

Interactively discovers requirements for a specific cloud workload and generates design recommendations and architectural guidance to build a multi-product solution in Google Cloud. Use when users need holistic, end-to-end design recommendations and architectural guidance for complex workloads on Google Cloud for specific use cases. Don't use this skill when other specialized skills exist that directly address the user-specified workload or use case. If the user's request is narrowly focused ...

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector databa...

Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.

Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.

Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.

Generates reliability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use this skill to evaluate a workload, identify reliability requirements, and provide actionable recommendations for build, deploy, and manage the workload reliably in Google Cloud.

Generates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.

Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud.

Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server...

Design, route, and verify printed circuit boards (PCBs) using KiCad 8 EDA. Master schematic capture, Electrical Rules Check (ERC), footprint assignments, multi-layer stackups, impedance-controlled trace routing, ground plane stitching vias, Design Rules Check (DRC), and manufacturing fabrication exports (Gerber RS-274X, Excellon drill files, IPC-D-356 netlists). Trigger when designing electronics, routing PCBs, or preparing hardware for fabrication.

Design, synthesize, and verify digital hardware circuits using Verilog and SystemVerilog for FPGAs (Xilinx Artix/Zynq, Intel Cyclone, Lattice iCE40). Implement synthesizable finite state machines (FSM), synchronous reset logic, clock domain crossing (CDC) synchronizers, and testbench simulation using Icarus Verilog and GTKWave. Trigger when writing Verilog HDL, designing FPGA logic, or debugging hardware timing.

Harden JWT-based authentication and session lifecycles using asymmetric signing (RS256/EdDSA), JWKS rotation, strict claims validation, Redis-backed sliding window sessions, revocation lists, and secure HttpOnly cookie storage. Trigger when implementing token validation, session revocation, or hardening API authentication.

Implement enterprise OAuth 2.1 and OpenID Connect (OIDC) authorization code flow with Proof Key for Code Exchange (PKCE), state and nonce verification, refresh token rotation, token revocation, and centralized IdP federation (Keycloak, Auth0, Okta). Trigger when building secure authentication, single sign-on (SSO), or third-party delegated authorization.

Master enterprise Ansible automation, idempotent playbook execution, reusable role design, Ansible Vault security, and Molecule automated testing.

Architect, build, and deploy production-grade Cloudflare Workers, Durable Objects, D1, KV, and edge API architectures using Wrangler, TypeScript, and Hono.

Master production-grade Terraform and OpenTofu module architecture, state management, dependency injection, testing, and multi-environment orchestration.

Implement production-grade i18next localization workflows, dynamic locale switching, ICU message syntax formatting, fallback strategies, namespaces, and automated translation extraction. Trigger when configuring internationalization (i18n) in React/Next.js/Vue applications or setting up localization workflows.

Master right-to-left (RTL) and bidirectional (BiDi) UI design, CSS logical properties (`margin-inline`, `padding-block`), layout direction switching (Arabic, Hebrew, Persian), typography scaling, icon mirroring rules, and automated RTL testing. Trigger when designing or implementing RTL UI support, multi-directional web/mobile layouts, or fixing BiDi rendering bugs.

Develop production-grade Linux Kernel Modules (LKM), character device drivers (`cdev`), and hardware control abstractions in C. Implement file operations (`open`, `read`, `write`, `unlocked_ioctl`), kernel memory allocators (`kmalloc`, `vmalloc`), spinlocks, mutexes, interrupt request handlers (bottom halves / tasklets), and sysfs attributes. Trigger when writing kernel drivers, low-level hardware interfaces, or debugging kernel crashes.

Design, implement, and verify high-performance custom memory allocators in C and C++. Build Arena / Bump allocators, Pool / Fixed-size block allocators, and Free-List allocators. Ensure strict hardware memory alignment (alignof, power-of-two boundaries), zero-fragmentation lifecycles, and cache line locality. Trigger when building game engines, high-frequency trading (HFT) engines, or embedded runtime systems.

Build and manage end-to-end MLOps pipelines using MLflow for experiment tracking, model registry, artifact logging, autologging, model evaluation, and deployment serving. Triggers when integrating MLflow tracking servers, configuring S3/GCS backend stores, writing custom PyFunc models, automating hyperparameter tuning (Optuna), registering models, or building FastAPI/Docker inference services.

Implement automated machine learning model performance monitoring, statistical data drift (KS-test, PSI, Wasserstein distance), concept drift detection, and prediction quality evaluation in production ML systems using Evidently AI, NannyML, SciPy, and Prometheus. Triggers when establishing model observability, real-time feature drift middleware, automated HTML drift reporting, or retraining trigger alerts.

Instructions to make or update an app's UI so that it adapts to different

Upgrades, or migrates, an Android project to use Android Gradle Plugin