What does this skill do, and when should you use it?
This skill supports administration and development for Google Bigtable, including instance, cluster, table, and column-family configuration. It emphasizes row-key design, performance, hotspotting, SQL queries, and client-library code. It distinguishes gcloud control-plane operations from cbt data-plane operations and covers Java, Go, and Python client scenarios, with Java or Go preferred for production. Explicit user confirmation is required before changing non-emulator database data or structure.
Uses gcloud for Bigtable instances, clusters, app profiles, backups, IAM, tables, and views; uses cbt for table updates, column families, and data reads and writes; guides SQL queries, row-key and column-family design, and Java, Go, and Python client code; recommends Key Visualizer, hot-tablets, gcloud table statistics, and cbt read with include-stats=full for performance and hotspot diagnosis; provides schema-evolution workflows using Terraform or cbt.
- A Bigtable infrastructure engineer needs to provision instances, clusters, tables, or backups.
- A data-modeling developer needs to design row keys, column families, and access patterns that reduce scans and hotspots.
- A developer needs structured queries or aggregations through Bigtable SQL.
- An engineering team is building a Bigtable application with Java, Go, or Python.
- An operator is investigating slow queries or hotspotting and needs guidance on Bigtable diagnostic tools.
How do you install this skill?
- Treat instance, table, column-family, and row deletion commands as high-risk; reconfirm the project, instance, exact target, and backup state before execution.
- Do not place access tokens from the curl example in shared shell history or logs; add secure credential-handling and least-privilege guidance.
- Before using the examples in production, verify the current gcloud, cbt, SQL-dialect, and client-library versions and test in an emulator or isolated project.
- Claims about row keys, timestamps, GC policies, and multi-cluster atomic operations lack independent test evidence and should be checked against current official documentation and the target environment.
- Shell / CLI
- Network access
gcloud CLIcbt CLI
Run npx skills add google/skills, then select Bigtable Basics from the interactive selection. The README does not document a specific installation directory or non-interactive selection flags.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/bigtable-basics" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
After installation, provide a concrete Bigtable request, for example: Help me design a Bigtable row key and column families for a high-throughput time-series workload. Before issuing commands that modify a non-emulator database's data or structure, obtain explicit user confirmation.
What are this skill's strengths and limitations?
- Covers Bigtable provisioning, querying, client development, schema design, and performance diagnosis.
- Clearly separates gcloud control-plane responsibilities from cbt data-plane responsibilities.
- Addresses additional access patterns with continuous materialized views and Bigtable Data Boost.
- Includes CLI, SQL, client-library, and schema-design reference areas.
- Its scope is Bigtable and explicitly excludes generic Cloud SQL administration.
- The README provides no version-compatibility matrix, test suite, or platform-validation results.
- The referenced guide files are not included in the supplied material, so their full syntax and examples cannot be verified here.
- Some workflows depend on the gcloud and cbt CLIs and access to a Google Cloud Bigtable environment.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Google Bigtable Basics this page ✓ Google · Official | 56 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Supabase Postgres Performance Guide | 52 · Use with care | ★ 2.8k | 24d ago | MIT |
| Google Analytics Admin API Assistant ✓ Google · Official | 45 · Not recommended | ★ 21k | 12d ago | Apache-2.0 |
| Skill Creator | 61 · Recommended | ★ 20k | 16d ago | MIT |
| Supabase Development Guide | 55 · Use with care | ★ 2.8k | 24d ago | MIT |
The skill explicitly distinguishes gcloud from cbt for control-plane and data-plane work, and SQL APIs from key-value APIs for querying versus data modification. It does not provide a full comparison with other database skills.
How did FollowSkills review this skill?
The skill explicitly requires user confirmation before non-emulator database changes, separates control-plane and data-plane tooling, and includes a backup-restore path. Points are deducted because instance, table, and row deletion commands lack per-action confirmation, impact scope, and rollback prerequisites; least-privilege IAM, sensitive-data handling, and exposure risks for access tokens embedded in commands are not addressed.
The material covers querying, writes, schema changes, emulator use, and hotspot diagnosis, with generally coherent primary workflows. Points are deducted because the evidence is static command text with no test suite, CI, or reproducible results; several commands, SQL forms, and product constraints lack version prerequisites, failure examples, and diagnosable error guidance. The static ceiling also applies.
The audience and scenarios are reasonably clear: Bigtable administration, schema design, SQL, client code, and hotspot diagnosis, while generic Cloud SQL administration is excluded. Points are deducted because trigger boundaries, input/output expectations, and non-fit cases remain underspecified; Chinese-language support is not described, and constrained environments lack alternatives to gcloud, cbt, Cloud Console, and Google Cloud services.
The information architecture is readable, with progressive links to CLI, schema, SQL, client, Dataplex, and emulator references; the repository README supplies installation, Apache-2.0 licensing, official attribution, and an issue path. Points are deducted because the skill lacks a compatibility matrix, changelog, named maintenance responsibility, and update policy; placeholders, installation notes, FAQs, known limitations, and troubleshooting are incomplete.
The material could directly support common Bigtable queries, schema design, and CLI operations, with useful guidance on row keys, hotspotting, and multi-cluster atomic operations. Points are deducted because no committed real outputs, test results, or end-to-end verification are provided, so commands and product details still require manual review; static scoring is capped at 7.
The files provide concrete commands, SQL, a configuration template, reference links, and a performance checklist, giving limited auditability. Points are deducted because there is no test suite, CI coverage, version pinning, or independent review evidence, and key technical claims are primarily documentation assertions; static evidence cannot justify a higher score.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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