GoogleCloudPlatform

Enforces data governance by verifying Knowledge Catalog Aspects before recommending BigQuery tables.

Ensures the game has a fun easter egg that turns the screen black and white, sepia or gameboy monotone colours. Use when creating or modifying a game.

Ensures platformer games have polished movement, responsive controls, and balanced jump mechanics. Use ONLY when the game type is "platformer".

Guides the creation of a new game and is the high-level orchestrator skill that MUST be invoked first. Use when tasked with creating a new game.

Arcade game must have a retro asthetic. Use when creating or modifying a game to ensure that the game looks retro and maintains a consistent look and feel with all games.

Game will always use the same control scheme/buttons and mapping of the gamepad and keyboard. Use when creating or modifying a game.

Helps the user make or modify a game. Use when the user wants to create a new game or modify an existing one.

Ensures games created have a good quality user experience and follow best practices. Use when creating or modifying a game.

Automate the initial scaffolding of a KCC "direct" resource, including CRD types and generation scripts. Use this when starting a new "direct" implementation for a GCP resource.

Creates or updates the _identity.go and _reference.go files for a Config Connector resource, ensuring they follow the canonical gcpurls.Template pattern. Use this when you need to make sure the identity and reference is up to date for a KCC resource or when implementing IdentityV2 and refs.Ref for a resource.

Standards and workflows for creating direct KRM Go types for an existing CRD while maintaining strict schema compatibility.

Comprehensive review guide and criteria for PRs adding or modifying features in legacy resources (TF patches, DCL patches, CRD changes, MockGCP alignment, and Direct Controller alignment).

Implement the controller, mappers, and fuzzer for a direct KCC resource, ensuring package isolation and CI compliance. Use this when implementing the main reconciliation logic for a "direct" resource.

Guides modifying, patching, and updating resources and fields managed by the Terraform/legacy controller, including patching the local/vendored Terraform Google Beta provider.

Guides the implementation of export support for a direct controller, including implementing AdapterForURL and verifying it through the unified test runner.

Base skill containing shared standards for all KCC direct resource types (both greenfield and brownfield).

Skill for reviewing Magic Modules schemas. Currently covers the Enum vs. String decision via the knowledge base.

Opt resource into MMv1 list-resource generation by setting `generate_list_resource: true`, validate it locally, and open a one-resource PR against GoogleCloudPlatform/magic-modules. Invoke when the user asks to add list-resource support for a specific MMv1 resource or to enable `generate_list_resource` for an eligible resource.

Fallback workflow for general implementation and debugging tasks that do not involve creating a new resource.

Workflow specifically for creating a new resource, supporting both autogen and manual generation.

Create a Pull Request (PR) against GoogleCloudPlatform/magic-modules following repository standards, including branch management, commit formatting, mandatory release notes, pre-PR verification checks, and gh CLI commands.

Validate that changes to the generated providers don't introduce breaking changes or fields with missing tests or missing documentation. Use this skill when the user wants to check for breaking changes, missing tests, or missing documentation, or other problems in the downstream providers.

Guide for Phase 6 of TGC Main Loop (Fix). Use to understand how to fix failures.

Add a new field to an existing handwritten resource in TGC. Use when you need to extend a handwritten resource.

Systematically diagnose GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads. Identifies preemption events, maintenance interruptions, bad host VMs, unhealthy pods, and coordinator worker failures.

Diagnose and predict node disruption during Compute Engine host maintenance for GPU and TPU workloads.

Diagnose and prevent `vbar_control_agent` segfaults and OOMs caused by race conditions during TPU device resets and frequent metrics collection (e.g. every 3s). Use when TPU slice initialization fails or `vbar_control_agent` crashes on TPU v6e nodes.

Verifies if a GKE or Kubernetes cluster is unused (no active compute, external exposure, or persistent data) before allowing deletion. Evaluates external exposure (LoadBalancer Service, Ingress, Gateway, MultiClusterIngress), persistent data (Bound PVC), and active compute (Running/Pending Pods in user namespaces) with low-overhead queries and fail-close timeouts.

Monitor and manage GKE TPU Dynamic Slices custom resources. Use when checking slice lifecycle states, troubleshooting failed slice creations (e.g. SliceCreationFailed, FAILED), running single or multi-slice workloads, or safely deleting/disabling slices.

Monitor and troubleshoot GKE TPU workloads using GKE system metrics and PromQL.

Dynamically generates specialized GKE skills for complex troubleshooting, operational workflows, architectural setup, or performance/cost optimization. Trigger this skill whenever the user faces a novel or non-obvious GKE challenge, needs custom cluster management workflows, or standard agent capabilities fall short, even if they don't explicitly ask to create a skill.

Expert instructions for building high-quality GKE troubleshooting skills. Codifies Step 0 context rules, zero-hallucination signatures, and explicit LQL/PromQL query requirements.

Use when the marathon plan needs non-spatial event parameters defined: theme, hydration-point logistics, crowd zones, start times, or expected participant counts. Triggered when the request describes the feel or operational logistics of the race rather than its physical route.

Use when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing landmarks, or the zone-sweep algorithm. Triggered by route-planning or traffic-analysis language.

Use when the planning committee asks about marathon finances or requests a budget change. Triggered by mentions of budget, cost, spending, sponsorship, or any phrasing that implies modifying a financial allocation. Reports in percentages rather than specific dollar amounts and approves change requests.

Use when planning a marathon route that must reference real-world geography: looking up landmarks, businesses, or parks in a city, computing distances between points, or checking historical and forecasted weather for the race date and location. Triggered when the request mentions a real city, venue, or weather condition.

Use when the planning committee asks to change, increase, decrease, or modify a budget allocation in a security-restricted context. Triggered by any budget-modification phrasing. Refuses the change and directs the requester to an authorized administrator.

Use when a runner agent receives a distance update and must deplete water proportionally, or when the runner enters a hydration station and must decide whether to stop and rehydrate. Triggered by distance events and station events.

Use when a runner agent needs to manage athletic performance during a race tick: accelerating, braking, or reading current vitals (speed, energy, hydration, exhaustion). Triggered every tick the runner is active.

Use when the simulator's loop agent advances the race by one tick: broadcasting state to runners, draining the telemetry buffer, computing traffic conditions, and checking for race completion. Triggered every iteration of the LoopAgent.

End-to-end GECX/CXAS/CES conversational agent lifecycle -- build agents from requirements (PRD-to-agent), create and run evals (goldens, simulations, tool tests, callback tests), debug failures, and iterate to production quality. Use this skill whenever the user mentions GECX, CXAS, CES, SCRAPI, conversational agents, voice agents, audio agents, agent evals, pushing/pulling/linting agents, or agent instructions/callbacks/tools on the Google Customer Engagement Suite platform.

Automates the ingestion of customer requirement documents such as diagrams, BRDs, code etc., synthesizes high-fidelity natural transcripts, and compiles them into highly interactive, responsive Critical User Journey (CUJ) reports.

Migrate Dialogflow CX (DFCX) agents to CXAS (Customer Experience Agent Studio) agents. Use this skill when the user mentions DFCX migration, migrating agents, converting DFCX to CXAS, porting agents, agent migration, or post-migration optimization/consolidation. Four independently runnable scripts: migrate.py (1:1), stage_1.py (variable dedup + consolidation), stage_2.py (instruction state machines + tool mocks + lint + report), stage_3.py (rewires consolidated topology from source dep graph; only needed when stage_1 ran consolidation). State persists between scripts via <target>_ir.json so each can run / re-run / resume independently.

Parsing and ingestion directives for the Python-based Agent Development Kit (ADK) conversational agent framework.

Parsing and Ingestion directives for the Dialogflow CX (DFCX) agent structure.

Converts CXAS golden evaluations to SCRAPI SimulationEvals test cases. Use when generating high-level, goal-oriented test cases from turn-by-turn evaluation JSONs, and when enriching test expectations with inferred tool calls.

Extracts conversational transcripts from .drawio XML files.

Extracts conversational transcripts from Cyara XML test case files.

Expert in AI image generation and editing. Use when the user needs high-quality textures, character-consistent visuals, or image-to-image editing using mcp-nanobanana-go.

Installs Google's GenMedia MCP servers (Lyria, NanoBanana, Veo, Chirp, AVTool) via curl from pre-compiled release binaries and registers them in mcp_config.json. Use when the required media synthesis tools are missing or inactive.

Expert in generating full multi-scene multimedia storybooks (image, video, voice, and music) with dynamic duration probing, conversational tempo guardrails, a dedicated self-correcting Editor's QC Room, and pipeline flowcharts embedded in interactive reports.

Builds the mcp-genmedia Go MCP servers (nanobanana, veo, lyria, gemini-multimodal, chirp3-hd, avtool) from source and wires them into settings.json. Use this skill whenever the MCP tools are missing or broken — typically at the start of a new session, after a container restart, or when /tmp has been wiped. The prebuilt binaries in /workspace/.local/bin/ have no exec bit and live on a noexec mount; this skill compiles fresh executables into /tmp/bin/ where execution is allowed.

Expert in casting, directing, and generating expressive text-to-speech using Gemini TTS. Use this when the user needs virtual voice actor personas, expressive speech generation, or multiple variations of a voiceover (like "take 3 on the bounce").

Expert media production assistant. Use when requested to help with storyboarding, podcast creation, audio assembly, or complex multi-step media workflows using the GenMedia MCP servers (Veo, Lyria, Gemini TTS, NanoBanana).

Expert in video composition, editing, and format conversion. Use when the user wants to generate high-quality video, overlay images on video, concatenate clips, create GIFs, or sync audio to video using mcp-avtool-go and mcp-veo-go.

Expert in audio synthesis, music generation, and mixing. Use when creating podcasts, background scores, or multi-track audio layering using mcp-chirp3-go, mcp-lyria-go, mcp-gemini-go, mcp-nanobanana-go, and mcp-avtool-go.

Author rule notes, parameters, dependencies, tuning, and testing sections for a YARA-L rule.

Extract unstructured legacy rules from a raw text file and structure them into valid JSON.

Take a source SIEM rule and format its content for readability, then insert it into a YARA-L rule template.

Generate a raw sample log entry in the original source format that will trigger the migrated YARA-L rule.

Migrate a source rule section in a YARA-L file into YARA-L 2.0, adhering to precise Google SecOps schemas.

Initialize a new YARA-L rule directory structure and YARA-L rule file from the template.

Full orchestration workflow to migrate a custom SIEM rule to YARA-L, running all sub-steps sequentially.

Run the recommender script to generate recommendations for SecOps curated/community rules based on custom rules.

Specialized in working with Google Maps Grounding lite and MCP tools. Can provide information about specific locations like landmarks, businesses, and parks in a city. Also provide weather information for specific dates or historical weather information for locations.

Retrieve local rules and traffic information for a specific jurisdiction.

TODO - One line description of what this skill does.

Evaluates the plan against financial, community, and logistical goals.

Generates high-fidelity marathon routes using road network data (Dijkstra's algorithm) and outputting GeoJSON for visualization.

Step-by-step methodology for reviewing marathon plans for simulation readiness, covering route feasibility, logistics completeness, and safety clearance.

Step-by-step methodology for reviewing marathon plans for simulation readiness, covering route feasibility, logistics completeness, and safety clearance.

Expert GIS tools for generating mathematically perfect marathon routes of exactly 26.2 miles using road network data and zone-sweep decomposition.

Adopts the Agent Manager persona. Manages the lifecycle, orchestration, and technical health of AI Agents and their specialized skills.

Adopts the Site-Reliability Engineer (SRE) persona. Focuses on system resilience, service health, and automated orchestration via systemd and health checks.

Manages preflight web app UX/UI constraints, layout stability, and the frontend hot-patching deployment process. Use when modifying HTML, CSS, or JS in the apps/workstations/preflight-web/ directory.

Adopts the Legal Expert persona. Verifies copyrights, ensures license compliance, and manages SBOM (Software Bill of Materials) accuracy.

Adopts the Open-Source Expert persona. Ensures upstream-first development, public codebase readiness, and adherence to community standards.

Adopts the Privacy Expert persona. Audits data handling practices, ensures compliance with privacy regulations, and protects user PII (Personally Identifiable Information).

Adopts the Security Expert (SEC) persona. Focuses on system hardening, vulnerability auditing, and the protection of sensitive credentials and data.

Adopts the UX Specialist persona. Focuses on the visual impact, layout stability, and accessibility of the workstation's desktop environment and dashboards.

Google Ads copywriting expertise. Covers Responsive Search Ad (RSA) best practices, headline formulas, description frameworks, and character limits. Load this skill when generating text ads, headlines, or ad copy.

Brand strategy and campaign positioning expertise. Covers audience segmentation, competitive differentiation, value proposition frameworks, and campaign brief creation. Load this skill when setting up campaigns, selecting audience segments, or defining creative direction.

Ad platform specifications and best practices. Covers Google Ads, YouTube, Instagram, TikTok, and Meta ad formats, dimensions, character limits, and optimization tips. Load this skill when creating platform-specific content or recommending campaign settings.

Market trend analysis and product-trend mapping expertise. Covers how to interpret trend data, align products to cultural moments, and create trend-informed campaign angles. Load this skill during the Creative Research Phase when running trend_spotter and product_trend_mapper.

Cinematic video ad storytelling expertise. Covers 3-act narrative structure, emotional arcs, camera motion, scene transitions, and voiceover pacing for 24-second commercial video ads. Load this skill when generating video ads or storylines.

Visual art direction expertise for marketing imagery. Covers photography styles, composition rules, color theory, lighting techniques, and product staging. Load this skill when generating image ads, asset sheets, moodboards, or keyframe images.

Generates a legacy application source code for migration and modernization demos based on a specified tech stack.

Guidelines and rules for provisioning Google Cloud Spanner instances, including selecting the correct edition (Enterprise vs Standard) and compute capacity based on schema features (e.g., graphs and vector search) and environment purpose.

Guides the agent to bootstrap an initial ContextSet (templates, facets, and value searches) by deducing key information from the database schema and generating a ContextSet file.

Generate or expand a golden evaluation dataset of SQL/Question (NLQ+SQL) pairs for evaluating NL-to-SQL translation accuracy on a target database.

Guides the agent to execute an evaluation of a ContextSet against a golden NLQ+SQL dataset using the Evalbench framework.

Guides the agent to perform hill-climbing iterations to improve a ContextSet based on Evalbench evaluation results.

Orchestrates the initialization workflow for context engineering, and provides helper workflow for setting up database connections by creating or updating tools.yaml configurations.

Context engineering for Gemini Data Analytics API's data agent developer platform tools. Generates, evaluates, and iteratively optimizes a ContextSet (Templates, Facets, Value Searches) to maximize Natural-Language-to-SQL translation accuracy. Use this skill to run the automated setup, NL-SQL pair evaluation dataset generation and expansion, bootstrapping, scoring, and optimization pipeline. For manual authoring standards and schema syntax rules, see the context-generation-guide skill.

Guidelines and best practices for generating context items (Templates, Facets, Value Searches). Use this skill whenever the user asks to create, author, or generate context for database enrichment, or asks for examples and instructions on how to write templates, facets, or value searches. It helps bridge the gap between LLMs and structured databases. For running the automated generation, evaluation, and tuning lifecycle, see the context-engineering-workflow skill.