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google-bigquery-dotnet

Use when implementing, modifying, reviewing, troubleshooting, or optimizing Google BigQuery integrations in .NET, including BigQueryClient, authentication…

Skill by dprakash2101

IndexedMIT★ 4
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Use when implementing, modifying, reviewing, troubleshooting, or optimizing Google BigQuery integrations in .NET, including BigQueryClient, authentication, parameterized queries, DATE/DATETIME/TIMESTAMP types, query costs, serialization, and result mapping.

Use cases: data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/google-bigquery-dotnet

What's inside

Skill name
google-bigquery-dotnet
Description
Use when implementing, modifying, reviewing, troubleshooting, or optimizing Google BigQuery integrations in .NET, including BigQueryClient, authentication, parameterized queries, DATE/DATETIME/TIMESTAMP types, query costs, serialization, and result mapping.
License
MIT
Instructions
20 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • query-safety.md · references/

SKILL.md (first lines)

# Google BigQuery for .NET

Follow existing integration/data-access boundaries and the installed `Google.Cloud.BigQuery.V2` version. Reuse `BigQueryClient` according to repository lifetime conventions. Authenticate with Application Default Credentials and deployed workload/service identities; never embed service-account keys.

Use parameterized Standard SQL for every untrusted or runtime value. Never concatenate values into SQL. Identifiers cannot generally be value parameters, so select them only from an explicit trusted allowlist when dynamic identifiers are unavoidable.

- Select named columns rather than `SELECT *`. Apply partition filters and clustering-compatible predicates, and avoid scanning repeated data. Query design directly affects cloud cost.
- Choose immediate query execution versus explicit query jobs based on existing patterns and whether job ID, polling, labels, destination tables, or long-running lifecycle control is needed.
- Pass cancellation through async job creation/result retrieval where supported. Do not block async calls or add polling loops when the client already provides completion behavior.
- Page or stream results and project only required data. Do not call materializing helpers for an unbounded or potentially large result set; cap exports and use destination tables/storage workflows when appropriate.
- Map nulls deliberately and validate schema/type assumptions. Treat BigQuery `TIMESTAMP` as an absolute instant and `DATETIME` as a timezone-free civil value; do not silently convert between them using a machine-local timezone.
- Classify API, quota, permission, invalid-query, timeout, and cancellation failures. Preserve useful job/correlation IDs in safe structured logs, but do not log credentials, full sensitive SQL, or parameter values by default.

Do not add custom query/job retries merely because failures can be transient. Add them only when explicitly requested or required by existing architecture, prefer client/platform resilience, and account for job identity, duplicate submission, cost, timeout budget, bounded attempts, cancellation, and ambiguous completion. Never retry permission, invalid-query, or other permanent failures.

Before finalizing, inspect estimated/scanned bytes or repository query safeguards when available, and test mapping boundaries, nulls, pagination, cancellation, and date/time semantics relevant to the query.

Read [references/query-safety.md](references/query-safety.md) for job, pagination, and date/time decision details.

Where supported by the installed client and repository, use query dry runs or maximum-bytes controls to bound cost. Preserve job identity when investigating ambiguous completion.

From dprakash2101/dotnet-production-agent-skills, MIT.

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Last commit (this folder)
Sep 22, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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google-bigquery-dotnet skill. Agent Mart. https://agentmart.dev/skills/dprakash2101-google-bigquery-dotnet (last updated 2026-10-24)