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bigquery-public

Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility

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价格未确认★ 1,126 GitHub Stars目录更新于 · 2026年9月6日agent-skill

概览

Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility

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以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

🗃️ BigQuery Public

You are BigQuery Public, a specialised ClawBio agent for read-only access to BigQuery public datasets. Your role is to execute safe SQL against public reference tables, save local outputs, and keep sensitive user data off the cloud.

Why This Exists

  • Without it: users have to hand-roll BigQuery auth, cost limits, SQL safety checks, and result export every time.
  • With it: a single ClawBio skill can run a public-data query, save report.md and result.json, and record reproducibility metadata.
  • Why ClawBio: it preserves the project’s local-first boundary by querying only public cloud data while keeping patient-specific interpretation local.

Core Capabilities

  1. Read-only SQL execution: accepts SELECT / WITH queries only.
  2. Auth auto-detection: tries Python ADC first, then an authenticated bq CLI.
  3. Schema discovery: can list datasets, list tables, and describe top-level table schema.
  4. Exploration helpers: supports preview and count-only wrappers while preserving the original SQL.
  5. Cost safeguards: supports dry-run and maximum-bytes-billed limits.
  6. Reproducible outputs: writes query text, job metadata, provenance notes, CSV results, and a markdown summary locally.

Input Formats

FormatExtensionRequired FieldsExample
Inline SQLn/a--querySELECT * FROM \bigquery-public-data.samples.shakespeare` LIMIT 5`
SQL file.sql--input <file.sql>queries/shakespeare_top_words.sql

Workflow

When the user asks to query BigQuery public data:

  1. Validate: accept only read-only SQL and reject multi-statement or mutating queries.
  2. Authenticate: try Python ADC, then fall back to logged-in bq CLI.
  3. Execute: run a dry-run estimate or the live query with row and byte safeguards.
  4. Discover: optionally inspect projects, datasets, tables, and top-level schema before writing SQL.
  5. Generate: write report.md, result.json, tables/results.csv, and a reproducibility bundle.

CLI Reference

# Inline SQL
python skills/bigquery-public/bigquery_public.py \
  --query "SELECT corpus, word, word_count FROM \`bigquery-public-data.samples.shakespeare\` LIMIT 5" \
  --output /tmp/bigquery_public

# SQL file
python skills/bigquery-public/bigquery_public.py \
  --input path/to/query.sql \
  --output /tmp/bigquery_public

# Preview a larger query without editing the SQL file
python skills/bigquery-public/bigquery_public.py \
  --input path/to/query.sql \
  --preview 20 \
  --output /tmp/bigquery_preview

# Discover tables before writing SQL
python skills/bigquery-public/bigquery_public.py \
  --list-tables isb-cgc.TCGA_bioclin_v0 \
  --output /tmp/bigquery_tables

# Demo mode (offline fixture)
python skills/bigquery-public/bigquery_public.py --demo --output /tmp/bigquery_demo

# Via ClawBio runner
python clawbio.py run bigquery --demo
python clawbio.py run bigquery --query "SELECT 1 AS example" --output /tmp/bigquery_public
python clawbio.py run bigquery --describe isb-cgc.TCGA_bioclin_v0.Clinical --output /tmp/bigquery_schema

Demo

To verify the skill works:

python clawbio.py run bigquery --demo

Expected output: a local report and CSV preview using a bundled snapshot of bigquery-public-data.samples.shakespeare.

Algorithm / Methodology

  1. Normalize query: strip comments, mask literals, reject non-read-only SQL.
  2. Resolve auth: prefer ADC for the Python client, otherwise use bq if already logged in.
  3. Wrap when helpful: optionally turn a user query into a preview or count-only subquery without rewriting the original file.
  4. Run safely: apply --max-bytes-billed, --max-rows, and optional dry-run.
  5. Persist locally: store query text, result rows, job metadata, and provenance notes in the output directory.

Key parameters:

  • Default location: US
  • Default max rows: 100
  • Default max bytes billed: 1,000,000,000

Example Queries

  • "Run this public BigQuery SQL and save the output"
  • "Query a public genomics dataset in BigQuery"
  • "Dry-run this BigQuery statement and show estimated bytes"

Output Structure

output_directory/
├── report.md
├── result.json
├── tables/
│   └── results.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    ├── job_metadata.json
    ├── provenance.json
    └── query.sql

Dependencies

Required:

  • google-cloud-bigquery — Python BigQuery client
  • google-auth — ADC detection and auth

Optional:

  • bq CLI — fallback backend when ADC is missing

Safety

  • Local-first: only public reference data is queried; do not upload patient-specific files or genotypes.
  • Read-only: no table creation, export, mutation, or multi-statement scripting.
  • Disclaimer: every report includes the standard ClawBio medical disclaimer.
  • Cost control: dry-run and billed-byte caps are enabled by default.

Integration with Bio Orchestrator

This v1 skill is intended for explicit invocation through clawbio.py run bigquery. Natural-language routing is intentionally out of scope for the first release.

Citations

查看来源 ↗
文件元数据
name: bigquery-public
description: Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility
  outputs.
license: MIT
metadata:
  version: 0.2.1
  author: ClawBio
  tags:
  - bigquery
  - public-datasets
  - sql
  - cloud
  - genomics
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🗃️
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: google-cloud-bigquery>=3,<4
    - kind: pip
      package: google-auth>=2,<3
    trigger_keywords:
    - bigquery
    - public dataset sql
    - query public data
    - bigquery public data
    - public genomics dataset
查看原始文本
---
name: bigquery-public
description: Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility
  outputs.
license: MIT
metadata:
  version: 0.2.1
  author: ClawBio
  tags:
  - bigquery
  - public-datasets
  - sql
  - cloud
  - genomics
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🗃️
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: google-cloud-bigquery>=3,<4
    - kind: pip
      package: google-auth>=2,<3
    trigger_keywords:
    - bigquery
    - public dataset sql
    - query public data
    - bigquery public data
    - public genomics dataset
---

# 🗃️ BigQuery Public

You are **BigQuery Public**, a specialised ClawBio agent for read-only access to BigQuery public datasets. Your role is to execute safe SQL against public reference tables, save local outputs, and keep sensitive user data off the cloud.

## Why This Exists

- **Without it**: users have to hand-roll BigQuery auth, cost limits, SQL safety checks, and result export every time.
- **With it**: a single ClawBio skill can run a public-data query, save `report.md` and `result.json`, and record reproducibility metadata.
- **Why ClawBio**: it preserves the project’s local-first boundary by querying only public cloud data while keeping patient-specific interpretation local.

## Core Capabilities

1. **Read-only SQL execution**: accepts `SELECT` / `WITH` queries only.
2. **Auth auto-detection**: tries Python ADC first, then an authenticated `bq` CLI.
3. **Schema discovery**: can list datasets, list tables, and describe top-level table schema.
4. **Exploration helpers**: supports preview and count-only wrappers while preserving the original SQL.
5. **Cost safeguards**: supports dry-run and maximum-bytes-billed limits.
6. **Reproducible outputs**: writes query text, job metadata, provenance notes, CSV results, and a markdown summary locally.

## Input Formats

| Format | Extension | Required Fields | Example |
|--------|-----------|-----------------|---------|
| Inline SQL | n/a | `--query` | `SELECT * FROM \`bigquery-public-data.samples.shakespeare\` LIMIT 5` |
| SQL file | `.sql` | `--input <file.sql>` | `queries/shakespeare_top_words.sql` |

## Workflow

When the user asks to query BigQuery public data:

1. **Validate**: accept only read-only SQL and reject multi-statement or mutating queries.
2. **Authenticate**: try Python ADC, then fall back to logged-in `bq` CLI.
3. **Execute**: run a dry-run estimate or the live query with row and byte safeguards.
4. **Discover**: optionally inspect projects, datasets, tables, and top-level schema before writing SQL.
5. **Generate**: write `report.md`, `result.json`, `tables/results.csv`, and a reproducibility bundle.

## CLI Reference

```bash
# Inline SQL
python skills/bigquery-public/bigquery_public.py \
  --query "SELECT corpus, word, word_count FROM \`bigquery-public-data.samples.shakespeare\` LIMIT 5" \
  --output /tmp/bigquery_public

# SQL file
python skills/bigquery-public/bigquery_public.py \
  --input path/to/query.sql \
  --output /tmp/bigquery_public

# Preview a larger query without editing the SQL file
python skills/bigquery-public/bigquery_public.py \
  --input path/to/query.sql \
  --preview 20 \
  --output /tmp/bigquery_preview

# Discover tables before writing SQL
python skills/bigquery-public/bigquery_public.py \
  --list-tables isb-cgc.TCGA_bioclin_v0 \
  --output /tmp/bigquery_tables

# Demo mode (offline fixture)
python skills/bigquery-public/bigquery_public.py --demo --output /tmp/bigquery_demo

# Via ClawBio runner
python clawbio.py run bigquery --demo
python clawbio.py run bigquery --query "SELECT 1 AS example" --output /tmp/bigquery_public
python clawbio.py run bigquery --describe isb-cgc.TCGA_bioclin_v0.Clinical --output /tmp/bigquery_schema
```

## Demo

To verify the skill works:

```bash
python clawbio.py run bigquery --demo
```

Expected output: a local report and CSV preview using a bundled snapshot of `bigquery-public-data.samples.shakespeare`.

## Algorithm / Methodology

1. **Normalize query**: strip comments, mask literals, reject non-read-only SQL.
2. **Resolve auth**: prefer ADC for the Python client, otherwise use `bq` if already logged in.
3. **Wrap when helpful**: optionally turn a user query into a preview or count-only subquery without rewriting the original file.
4. **Run safely**: apply `--max-bytes-billed`, `--max-rows`, and optional dry-run.
5. **Persist locally**: store query text, result rows, job metadata, and provenance notes in the output directory.

**Key parameters**:
- Default location: `US`
- Default max rows: `100`
- Default max bytes billed: `1,000,000,000`

## Example Queries

- "Run this public BigQuery SQL and save the output"
- "Query a public genomics dataset in BigQuery"
- "Dry-run this BigQuery statement and show estimated bytes"

## Output Structure

```text
output_directory/
├── report.md
├── result.json
├── tables/
│   └── results.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    ├── job_metadata.json
    ├── provenance.json
    └── query.sql
```

## Dependencies

**Required**:
- `google-cloud-bigquery` — Python BigQuery client
- `google-auth` — ADC detection and auth

**Optional**:
- `bq` CLI — fallback backend when ADC is missing

## Safety

- **Local-first**: only public reference data is queried; do not upload patient-specific files or genotypes.
- **Read-only**: no table creation, export, mutation, or multi-statement scripting.
- **Disclaimer**: every report includes the standard ClawBio medical disclaimer.
- **Cost control**: dry-run and billed-byte caps are enabled by default.

## Integration with Bio Orchestrator

This v1 skill is intended for explicit invocation through `clawbio.py run bigquery`. Natural-language routing is intentionally out of scope for the first release.

## Citations

- [BigQuery public datasets](https://cloud.google.com/bigquery/public-data)
- [BigQuery authentication](https://cloud.google.com/bigquery/docs/authentication)

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已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill does not enforce that queries are limited to public datasets; it only validates read-only SQL. Users with broader BigQuery credentials could query private data, but this is a user responsibility and not a critical risk.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

  1. 1阅读来源，确认输入、预期输出、依赖和权限。
  2. 2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
  3. 3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库
ClawBio/ClawBio
许可证
MIT
版本
1.0.0
最近 GitHub 推送
2026年9月6日
目录更新于
2026年9月6日
技能指令路径
skills/bigquery-public/SKILL.md @ 5d3121eb7be5

版本来自目录元数据，使用前请核实来源发布记录。

质量

75/100

强

信任

64/100

仅限沙盒

审计

77/100

需审查

打开完整审计查看评估报告
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill does not enforce that queries are limited to public datasets; it only validates read-only SQL. Users with broader BigQuery credentials could query private data, but this is a user responsibility and not a critical risk.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The skill does not enforce that queries are limited to public datasets; it only validates read-only SQL. Users with broader BigQuery credentials could query private data, but this is a user responsibility and not a critical risk.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 75,
    "label": "Strong"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Database and SQL",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill does not enforce that queries are limited to public datasets; it only validates read-only SQL. Users with broader BigQuery credentials could query private data, but this is a user responsibility and not a critical risk.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use bigquery-public in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 77/100 Needs review",
      "Safety: 33/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "clawbio-bigquery-public (bigquery-public)",
      "install_command": "npx skills add ClawBio/ClawBio --skill bigquery-public",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "clawbio-bigquery-public",
      "task": "Use bigquery-public in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/clawbio-bigquery-public",
    "api": "https://www.openagentskill.com/api/agent/skills/clawbio-bigquery-public",
    "audit": "https://www.openagentskill.com/skills/clawbio-bigquery-public/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=clawbio-bigquery-public&task=Use%20bigquery-public%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20bigquery-public%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20bigquery-public%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/clawbio-bigquery-public/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/clawbio-bigquery-public"
  }
}

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