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Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).

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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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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 relational database workloads.

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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.

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Configures best-practice, high-signal alerting policies for Google Cloud Run resources (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.

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Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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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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Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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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.

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Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

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Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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Teaches the fundamentals of Google Cloud Build (GCB). 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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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

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Design, mock up, build and review terminal UIs — full-screen TUIs, interactive CLIs and pickers, and plugin panels or popups inside a host (herdr, tmux, Neovim, k9s, lazygit). Use when designing, reviewing (from code, a live capture or a screenshot) or redesigning a TUI's layout, keybindings, states or colors, including requests that name a known TUI (lazygit, k9s, btop, yazi, helix, fzf); writing terminal mockups; building or fixing one in Ink, Textual, Bubble Tea, Ratatui or gum; mapping a color scheme (Catppuccin, Dracula, Nord, Gruvbox, Tokyo Night, Rosé Pine…) onto a TUI's theme; fixing misaligned glyphs, flicker, NO_COLOR, exit codes, signals, pipes or a broken terminal after exit; suspend or $EDITOR handoff; or profiling a slow TUI. Not for web or native GUIs, terminal-emulator, editor or shell-prompt themes.

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Build a candidate sourcing strategy and outreach pipeline. Use when the user says "where do I find candidates for this role", "build a sourcing plan", "write a cold outreach message", "we're not getting enough applicants", "our pipeline is dry", "help me find passive candidates", "write a LinkedIn message", or wants to increase the volume or quality of candidates in the funnel - even if they don't explicitly say "sourcing strategy". Also use when a recruiter needs to go beyond job postings to find talent.

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Construct a compelling job offer. Use when the user says "write an offer letter", "help me make an offer", "how should I structure this offer", "what should we include in the offer", "how do I explain the equity", "the candidate is negotiating", or wants to convert a candidate from interested to accepted - even if they don't explicitly say "offer letter". Also use when a recruiter needs to explain comp philosophy or total compensation to a candidate.

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Write SEO-optimized long-form articles targeting specific keywords using the They Ask You Answer Big 5 framework (Marcus Sheridan). Articles are categorized by Big 5 type (Cost, Problems, Versus, Best/Reviews, How-To) and structured accordingly. The "answer first" rule applies to every article. Use when the user asks for an SEO article, blog post for ranking, "rank for keyword X", organic content, search-optimized post, pillar page, or content for organic traffic. Includes keyword targeting, search intent matching, internal linking suggestions, and meta tags. For improving an existing article, see copy-review. For deciding what to write about, see social-calendar pillars and icp-research. For repurposing it, see content-repurposer.

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Identify, score, and mitigate delivery risks for a program or release. Use when the user says "risk review", "what could go wrong", "risk register", "delivery risks for this release", "program risk assessment", "identify blockers before they hit", or wants to pressure-test a plan before committing - even if they don't say "risk review". Based on "The Phoenix Project" by Kim, Behr & Spafford (unplanned work, fragile systems, single points of failure) and "The Art of Project Management" by Scott Berkun (scoping risk, milestone pressure, hidden assumptions).

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Write an empowered team brief. Use when the user says "team brief", "product brief", "give the team a problem", "empowered team", "how do I brief engineering", "delegate to the team", "product mission", or wants to give a team clear goals without dictating the solution - even if they don't say "team brief".

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Rate any product, feature, or experience on the 11-star scale (Brian Chesky's Airbnb thought experiment). Use when user says "rate this experience", "11-star", "star rating", "experience audit", "how good is this", "experience rating", "product audit", "quality assessment", or wants to evaluate product quality and identify improvement paths. Also trigger when user wants to benchmark a feature, assess where a product stands, or map out what "great" looks like - even if they don't explicitly say "11-star".

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Allocate testing effort based on risk and failure likelihood using Google's SET/SWE/TE model. Use when user says "risk-based testing", "where should I focus testing", "test coverage strategy", "prioritize what to test", "high risk areas", or needs to decide how to distribute QA effort across a codebase - even if they don't explicitly say "risk-based".

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Assess whether your leadership behaviors amplify or diminish your team using Liz Wiseman's Multipliers framework. Use when a leader asks "am I a good manager", "why isn't my team performing at their potential", "I feel like I'm doing all the thinking for my team", "my team seems dependent on me", "I want to be a better leader", "how do I empower my people", or "my team is smart but underperforming". Also trigger when someone describes always having the best ideas in the room, team members not taking initiative, reports waiting for direction before acting, or a leader who feels exhausted from carrying the team. Diminisher patterns are often invisible to the leader exhibiting them.

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