What does this skill do, and when should you use it?
This skill provides Python coding guidance for BigFrames, also known as BigQuery DataFrames, using a pandas- and scikit-learn-style API. It favors keeping cleaning, transformation, and analysis in BigQuery instead of downloading full datasets locally. The guidance covers DataFrame operations, previews, schema inspection, visualization, and BigQuery ML. It is not intended for SQL-first workflows or direct use of the google-cloud-bigquery client library.
It generates Python code using bigframes.pandas and recommends enabling partial ordering, using peek() for previews, inspecting results with dtypes and display(), and plotting directly from BigFrames objects where possible. It favors DataFrame/Series methods and built-in accessors over unnecessary to_pandas(), UDFs, and lambdas. For machine learning, it uses bigframes.bigquery.ml; it documents legacy bigframes.ml behavior only when that package is explicitly requested, including rules for prediction output, PCA, ARIMA Plus, tuning, and model persistence.
- A data analyst wants to process large BigQuery datasets with a pandas-style API in a notebook without downloading the full dataset locally.
- A Python developer needs BigFrames guidance for data cleaning, transformation, and analysis.
- A machine-learning developer wants regression or classification training delegated to BigQuery's scalable ML engine.
- An analyst needs to visualize BigFrames DataFrame or Series objects with Matplotlib or Seaborn, aggregating or sampling oversized data first.
- A developer maintaining a legacy BigFrames ML project needs its documented prediction, tuning, and persistence constraints.
How do you install this skill?
- The examples use replace=True when creating models without documenting confirmation, backup, or recovery for overwriting existing models.
- Installation, dependency versions, compatibility, and API-change guidance are absent; verify the current library versions before use.
- BigQuery data may be sensitive, but the skill does not address least-privilege IAM, region, cost controls, or data residency.
- Core functionality depends on Google BigQuery, with no mainland-China reachability guidance or offline alternative.
PythonBigFramesBigQuery
Install the skill collection with the command provided in the repository README: npx skills add google/skills. The installer allows selecting specific skills from the repository. The source does not document installation, versions, authentication, or permissions for Python, BigFrames, or BigQuery.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/bigquery-bigframes" ~/.claude/skills/
rm -rf "$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?
Once installed, send your agent any of these to trigger it:
- Use BigFrames to clean and analyze this BigQuery data, and avoid downloading the full dataset locally.
After installing it in an Agent Skills-compatible client, use a request such as: “Use BigFrames to clean and analyze this BigQuery data, and avoid downloading the full dataset locally.” For machine learning, explicitly request bigframes.bigquery.ml; request bigframes.ml only when legacy compatibility is required.
What are this skill's strengths and limitations?
- Provides concrete, actionable conventions for the BigFrames DataFrame API.
- Clearly prioritizes BigQuery-side execution, lazy queries, and avoiding local memory exhaustion.
- Covers data processing, schema verification, visualization, and BigQuery ML.
- Documents important compatibility rules for legacy BigFrames ML.
- The source does not document BigFrames or BigQuery installation, authentication, permissions, or versions.
- The source provides no test suite or platform-validation details.
- It is unsuitable for SQL-first workflows or direct use of the google-cloud-bigquery client library.
- Some recommendations depend on specific BigFrames API behavior without providing a complete API reference.
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 |
|---|---|---|---|---|
| BigQuery BigFrames Development Standards this page ✓ Google · Official | 50 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Trackio Experiment Tracking Skill | 53 · Use with care | ★ 38k | 14d ago | MIT |
| D3.js Visualization Skill | 60 · Recommended | ★ 243 | 1y ago | — |
| Lieflat Charts | 57 · Use with care | ★ 6.1k | 1mo ago | NOASSERTION |
| OfficeCLI Data Dashboard Skill | 57 · Use with care | ★ 32k | 14d ago | Apache-2.0 |
Compared with SQL-first workflows and the google-cloud-bigquery client library, this skill prioritizes the BigFrames DataFrame/Series API. The README identifies bigquery-basics as the skill for the other workflow category.
How did FollowSkills review this skill?
The skill promotes staying in the cloud, avoiding unnecessary local materialization, and makes BigQuery reads, writes, and model-creation flows visible; however, it does not require confirmation for cost, access, or overwrite effects, and lacks sensitive-data handling, least-privilege, rollback, and dependency-security guidance, so points are deducted.
The main instructions and regression examples are broadly consistent and cover ordering mode, previews, schema checks, and several API limitations; however, there are no tests, version pinning, abnormal-input handling, or diagnostic failure feedback, and static review cannot reproduce key paths, so the score is conservatively limited.
The name, audience, and trigger boundary are fairly clear, distinguishing BigFrames from SQL-first workflows and the BigQuery client library, with data-processing and ML scenarios provided; non-fit ranges, input/output contracts, Chinese support, and mainland-China network reachability are underdocumented, and the core service may depend on overseas reachability, so points are deducted.
The documentation is readable and progressively structured, includes regression examples, and the repository provides an Apache-2.0 license and issue-reporting path; it lacks installation and dependency versions, compatibility guidance, FAQs, a changelog, and clearly assigned maintenance responsibility, so points are deducted.
The examples cover loading, cleaning, training, evaluation, and prediction and can serve as a direct starting point for common BigFrames tasks; however, they are unexecuted static examples with no verified results, complete task coverage, or alternative comparison, so the score remains below the static ceiling.
The evidence consists mainly of auditable instructions and code examples in the skill files; there are no committed tests, CI coverage, third-party execution results, or cross-source corroboration, so independent reproduction is limited and the score is conservative.
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.
See the full review method →