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Bigquery Auth

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minicoohei
bigquery-auth

GCPプロジェクト単位でBigQuery認証を設定するスキル。 gcloud設定プロファイルで複数プロジェクトを安全に分離管理。 「BigQueryに繋ぎたい」「BQ認証」「gcloud認証」「データ分析の認証設定」等のリクエストで発動。

Overview

Publisherminicoohei
Repositoryai-agent-camp
Skill namebigquery-auth
Stars
346
Forks
13
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by minicoohei on GitHub. Read the source before you install it.

Installation

Install the Bigquery Auth AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/bigquery-auth
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/minicoohei/ai-agent-camp.git /tmp/ai-agent-camp
mkdir -p .claude/skills
cp -r /tmp/ai-agent-camp/.claude/skills/bigquery-auth .claude/skills/bigquery-auth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bigquery Auth in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bigquery Auth on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bigquery Auth is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

トリガーワード

「BigQuery認証」「BQ繋ぎたい」「データ分析の認証」「gcloud認証」

BigQuery Authentication (Project-based)

GCPプロジェクト単位でgcloud設定プロファイルを作成し、BigQuery認証を行うスキルです。

Workflow

  1. ユーザーが「BigQuery使いたい」「{プロジェクト}のデータを見たい」等と言う
  2. GCPプロジェクトIDを確認(必須)
  3. 既存の設定プロファイルを確認
  4. 必要に応じて新規プロファイルを作成
  5. ブラウザ認証をガイド
  6. 接続テストを実行

認証手順

Step 1: 設定プロファイル確認

bash
gcloud config configurations list

既存プロファイルを表示し、目的のプロジェクト用があるか確認。

Step 2: プロファイル作成(新規の場合)

bash
# プロファイル作成
gcloud config configurations create {PROFILE_NAME}

# プロジェクト設定
gcloud config set project {PROJECT_ID}

Step 3: gcloud認証

bash
# メイン認証(ブラウザが開く)
gcloud auth login

# Python SDK用認証(ブラウザが開く)
gcloud auth application-default login --quiet

注意: 両方のコマンドでブラウザ認証が必要です。

Step 4: 認証確認

bash
# 現在のプロファイル確認
gcloud config configurations list

# プロジェクト確認
gcloud config get-value project

# ADCトークン確認
gcloud auth application-default print-access-token

Step 5: BigQuery接続テスト

python
import os
# 環境変数競合を回避
if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ:
    del os.environ["GOOGLE_APPLICATION_CREDENTIALS"]

from google.cloud import bigquery
client = bigquery.Client(project="{PROJECT_ID}")
datasets = list(client.list_datasets())
print(f"接続成功!{len(datasets)}個のデータセット")

プロファイル切り替え

bash
# プロファイル一覧
gcloud config configurations list

# 切り替え
gcloud config configurations activate {PROFILE_NAME}

登録済みプロファイル(テンプレート)

ADC認証(gcloud login)

プロファイルプロジェクトIDアカウント用途
default{YOUR_PROJECT_ID}{YOUR_EMAIL}デフォルト
{PROFILE_2}{PROJECT_ID_2}{EMAIL_2}分析用

自分のプロジェクト情報に置き換えてください。

サービスアカウント認証(外部プロジェクト)

プロファイルプロジェクトIDキーファイル用途
{SA_PROFILE}{SA_PROJECT_ID}~/.gcp/{SA_KEY_FILE}.json外部プロジェクト分析

サービスアカウント認証の使い方

外部プロジェクトにサービスアカウントで接続する場合:

python
import os
from google.cloud import bigquery
from google.oauth2 import service_account

# サービスアカウントキーで認証
credentials = service_account.Credentials.from_service_account_file(
    os.path.expanduser("~/.gcp/{SA_KEY_FILE}.json")
)

# BigQueryクライアント作成
client = bigquery.Client(
    project="{SA_PROJECT_ID}",
    credentials=credentials
)

# 接続テスト
datasets = list(client.list_datasets())
print(f"接続成功!{len(datasets)}個のデータセット")

トラブルシューティング

エラー原因対処法
File xxx was not foundGOOGLE_APPLICATION_CREDENTIALS が無効unset GOOGLE_APPLICATION_CREDENTIALS
Reauthentication needed認証期限切れ再度認証実行
Permission deniedBigQuery権限なしIAM設定を確認

重要な注意事項

環境変数の競合

GOOGLE_APPLICATION_CREDENTIALS 環境変数が設定されている場合、ADCより優先されます。 Pythonコードで以下を実行して回避:

python
import os
if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ:
    del os.environ["GOOGLE_APPLICATION_CREDENTIALS"]

marimo notebook使用時

.cursor/rules/notebook.mdc のルールに従い:

  1. 作業開始前に「どのGCPプロジェクトで作業しますか?」と確認
  2. gcloud config configurations list でプロファイル一覧を表示
  3. 必要に応じてプロファイルを切り替え

Requirements

  • Google Cloud SDK (gcloud) インストール済み
  • ブラウザでGoogleアカウントにログイン可能
  • 対象プロジェクトへのBigQuery閲覧権限

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bigquery Auth AI skill do?

GCPプロジェクト単位でBigQuery認証を設定するスキル。 gcloud設定プロファイルで複数プロジェクトを安全に分離管理。 「BigQueryに繋ぎたい」「BQ認証」「gcloud認証」「データ分析の認証設定」等のリクエストで発動。

Why use Bigquery Auth on TypingMind?

Because you install it once and use it with any model. Bigquery Auth is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bigquery Auth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/bigquery-auth. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bigquery Auth?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bigquery Auth?

As many as you like. As long as a model supports skills, you can use Bigquery Auth with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bigquery Auth AI skill free?

It is published on GitHub by minicoohei. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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