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Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Audit and incrementally retrofit an existing codebase so its intent and behavior are equally legible to people and AI agents. Applies Context Architecture's eight principles: place AGENTS.md at boundaries, bind every context claim to a mechanism (lint, types, tests, review), name boundaries, and find context that has rotted. Use when an agent reimplements code that already exists, invents structure, follows stale or deleted docs, propagates a deprecated pattern, or resolves ambiguity at random, or when asked to make a repository "agent-ready", "AI-legible", or to add or fix AGENTS.md / CLAUDE.md files.
Use when the user wants to integrate with the X (Twitter) API via Xquik to search tweets, look up user profiles, extract followers, run giveaway draws, monitor accounts, or access trending topics. Also use when the user mentions 'Xquik,' 'Twitter API,' 'X API,' 'tweet scraper,' 'follower extraction,' or 'Twitter monitoring.' Covers REST API, webhooks, and MCP server setup.
Fill any PDF locally and place each value precisely in a visual editor. Use when the user wants to fill out a PDF form, enter data into a PDF, complete a tax/insurance/bank form, or position text on a flat/scanned PDF. Handles flat (field-less) PDFs, per-character (comb) fields, and native AcroForm fields; leaves the signature blank for the user to sign.
Automated Android CI/CD pipeline to Google Play — supports TWA, React Native, Flutter, and native Android. Run npx android-cicd to set up keystore generation, GitHub Secrets, and a multi-stage workflow (internal/alpha/beta/production) with auto-bump versionCode.
Use this skill whenever a developer wants to turn an idea into a complete, production-ready, end-to-end system plan BEFORE writing any code. Trigger on 'plan this system', 'design the architecture for', 'help me blueprint', 'deep plan for X', 'break this idea into components', 'expand into action points', 'full implementation plan', or when the user pastes a project idea wanting architecture, components, pipelines, and file-level execution mapped out. Casual phrasing also triggers: 'help me think this through end-to-end', 'plan before coding'. Also covers living-workspace patterns: self-improving knowledge bases, reflection loops with auditor agents, four-agent teams, schema-as-code, wiki health scoring. **Resume triggers**: 'where did we leave off', 'continue this plan', 'resume my blueprint' - rehydrates state from disk via SESSION.md/NEXT.md/decisions/. Web research is mandatory every invocation.
Use when adding a blog to a Next.js + Sanity site, building a blog section from scratch, integrating Sanity CMS for editorial content, or setting up an SEO-optimized article surface. Triggers on phrases like 'add a blog', 'build the blog', 'create blog section', 'set up blog with Sanity', 'integrate Sanity CMS', 'blog architecture', or any new-blog scoping conversation on a Next.js project.
| name | bigquery-basics |
| description | Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery. |
| source | google/skills (Apache 2.0) |
BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities.
Enable the BigQuery API:
gcloud services enable bigquery.googleapis.com --quiet
Create a Dataset:
bq mk --dataset --location=US my_dataset
Create a Table:
Create a file named schema.json with your table schema:
[
{
"name": "name",
"type": "STRING",
"mode": "REQUIRED"
},
{
"name": "post_abbr",
"type": "STRING",
"mode": "NULLABLE"
}
]
Then create the table with the bq tool:
bq mk --table my_dataset.mytable schema.json
Run a Query:
bq query --use_legacy_sql=false \
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \
WHERE state = "TX" LIMIT 10'
Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
CLI Usage: Essential bq command-line tool
operations for managing data and jobs.
Client Libraries: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
MCP Usage: Using the BigQuery remote MCP server and Gemini CLI extension.
Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
IAM & Security: Roles, permissions, and data governance best practices.
If you need product information not found in these references, use the
Developer Knowledge MCP server search_documents tool.