Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
Generates Python code using BigQuery DataFrames (BigFrames).
Install Bigquery Bigframes
$ npx skills add google/skills --skill bigquery-bigframes -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills bigquery-bigframes --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .claude/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .claude/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill bigquery-bigframes -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills bigquery-bigframes --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .agents/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .agents/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill bigquery-bigframes -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills bigquery-bigframes --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .cursor/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .cursor/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/bigquery-bigframes--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill bigquery-bigframes -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills bigquery-bigframes --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .gemini/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .gemini/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills bigquery-bigframesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill bigquery-bigframes -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .github/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .github/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill bigquery-bigframes -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills bigquery-bigframes --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/bigquery-bigframes .opencode/skills/bigquery-bigframes && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bigquery-bigframes" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-bigframes into .opencode/skills/bigquery-bigframes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-bigframes", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
bigquery-bigframes- GitHub stars
- 21k
- Used in
- 1 other repo
- Token cost
- ~1.3k tokens
- SKILL.md length
- 567 words
- Files
- 3 (incl. references)
- Skills in repo
- 145
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Generates Python code using BigQuery DataFrames (BigFrames).
- SQL-first workflows
- SKILL.md covers Dataframe API best practices, Machine Learning and BigFrames ML (Legacy)
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- The google-cloud-bigquery client library — use bigquery-basics
What it does
Bigquery Bigframes is an agent skill from google/skills, published by the product's own GitHub organization. Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/linear_regression.md` and `references/logistic_regression.md`).
It sits in Databases, covering Data warehousing, DataFrames and Machine learning. It works with Google BigQuery, Python, SQL and Google Cloud. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- SQL-first workflows
- The google-cloud-bigquery client library — use bigquery-basics
Example prompts
- “Use the bigquery-bigframes skill to generate Python code using BigQuery DataFrames (BigFrames)”
- “/bigquery-bigframes”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 8a1ac05. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Bigquery Bigframes loads about 1.3k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 567 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from google/skills at commit 8a1ac05, republished under its Apache-2.0 licence (© google). 567 words, ~1,263 tokens.
Download SKILL.mdSave it as
.claude/skills/bigquery-bigframes/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- bigquery-bigframes
- description
- Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
- metadata.version
- 2.0.0
- metadata.category
- BigDataAndAnalytics
BigFrames (BigQuery DataFrame) basics
BigFrames is a Python library that lets you take advantage of BigQuery data processing by using familiar Python APIs.
Dataframe API best practices
Stay in the Cloud: Perform data cleaning, transformation, and analysis via BigFrames methods to leverage BigQuery's scale rather than downloading data.
Prefer partial ordering mode: Enable partial ordering mode right after importing BigFrames. This speeds up data processing significantly by relaxing row-sequence constraints.
python import bigframes.pandas as bpd bpd.options.bigquery.ordering_mode = 'partial'Use
peek()for data preview: Usepeek(n)to preview data instead ofhead(n).peek(n)randomly samplesnrows and is significantly faster.head(n)returns rows in strict order and fails inpartialordering mode unless the DataFrame has been explicitly sorted.Avoid materializing data locally: Methods like
to_pandas()download all data to client memory, bypassing BigQuery’s distributed computation and risking Out of Memory (OOM) errors. Do not materialize data locally unless:- The dataset is small enough to fit safely in memory.
- An error message explicitly requires local materialization.
Prefer Dataframe API over SQL queries: Do not write raw SQL queries via
read_gbq()if a DataFrame/Series method achieves the same result, as it breaks the Pandas abstraction and prevents lazy query execution.Accessors over UDFs/Lambdas:
- Use built-in accessors (e.g.,
df.col.str.*,df.col.dt.*) instead of remote User Defined Functions (UDFs). UDFs require extra resources and time to deploy. - Do not use lambdas with
Series.map()orDataFrame.apply(). These methods do not accept functions withoutudforremote_functiondecorators.
python # Avoid: df["upper"] = df["name"].map(lambda x: x.upper()) # Prefer: df["upper"] = df["name"].str.upper()- Use built-in accessors (e.g.,
Schema Verification: Do not assume the schema of intermediate outputs. Proactively verify schemas using
.dtypesand inspect sample records usingdisplay()with.peek().Visualization: Plot directly from the BigFrames DataFrame/Series when possible. BigFrames is compatible with Matplotlib and Seaborn. If direct plotting fails, use the
.plotaccessor. If the dataset is too large to plot, aggregate or sample the data before calling.to_pandas()to plot locally.
Machine Learning
- Use
bigframes.bigquery.mlpackage: Do not use Scikit-learn or other ML libraries with BigQuery DataFrames. Standard Scikit-learn models require bringing data into local client memory, whereasbigframes.bigquery.mldelegates training directly to BigQuery's scalable ML engine. Import functions frombigframes.bigquery.ml.
Show full SKILL.md (211 more words)Show less
Reference Directory
- Linear Regression: Train a linear regression model to predict numerical values.
- Logistic Regression: Train a logistic regression model to predict boolean values.
BigFrames ML (Legacy)
The BigFrames ML package (bigframes.ml) is a legacy package that mimics the
scikit-learn API but is no longer recommended for new projects. Only use this
package if the user explicitly requests BigFrames ML.
- Legacy Imports: When legacy BigFrames ML is requested, import tools and
classes from
bigframes.mlinstead ofbigframes.bigquery.ml. - DataFrame Return on Prediction: Unlike Scikit-learn, BigFrames'
predict()method always returns a DataFrame containing both predictions and features, rather than a single series of predictions. - No
random_state: Do not pass arandom_stateargument when instantiating BigFrames ML models, as this parameter is not supported in the BigFrames ML package. - Automatic Scaling: Do not use
OneHotEncoderorStandardScalerunless explicitly requested, as scaling is handled automatically. - Hyperparameter Tuning: Write custom loops for hyperparameter tuning, as
BigFrames lacks
GridSearchCVorRandomizedSearchCV. - ARIMA Plus (Forecasting):
- Import from
bigframes.ml.forecasting. - Sort data chronologically and split around a timepoint before training.
- Ensure the prediction horizon is less than or equal to the training horizon.
- Import from
- PCA: BigFrames' PCA class lacks a
transform()method. Usepredict()instead. - Model Persistence: To persist a model, use
model.to_gbq(). To load a persisted model, usebpd.read_gbq_model().
© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (references) in skills/cloud/bigquery-bigframes of google/skills.
- SKILL.md
- references/linear_regression.md
- references/logistic_regression.md
Open the folder on GitHubat commit 8a1ac05
Used in 1 other repository
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in google/skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Bigquery Bigframes next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bigquery Bigframes this skillgoogle/skills | 21k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Chdb Datastorevemetric/vemetric | 394 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Chdb SQLvemetric/vemetric | 394 | 1 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Analysis Artifactswarpdotdev/oz-skills | 825 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Imaging Data CommonsK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~7.8k | Automated safety check: Pass | MIT | |
| Deploying On GCPancoleman/ai-design-components | 526 | — | ~3.9k | Automated safety check: Pass | MIT |
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Works with
Categories
Questions about Bigquery Bigframes
What does Bigquery Bigframes do?
Generates Python code using BigQuery DataFrames (BigFrames). Bigquery Bigframes is an agent skill from google/skills, published by the product's own GitHub organization. Generates Python code using BigQuery DataFrames (BigFrames).
When should I use Bigquery Bigframes?
Bigquery Bigframes fits situations like: SQL-first workflows; the google-cloud-bigquery client library — use bigquery-basics.
How do I install Bigquery Bigframes in Claude Code?
Run `npx skills add google/skills --skill bigquery-bigframes -a claude-code`. Or copy the skill folder (skills/cloud/bigquery-bigframes in google/skills) into .claude/skills/bigquery-bigframes in your project. Claude Code loads it when a task matches its description.
How do I install Bigquery Bigframes in Codex?
Run `npx skills add google/skills --skill bigquery-bigframes -a codex`. Or copy the skill folder (skills/cloud/bigquery-bigframes in google/skills) into .agents/skills/bigquery-bigframes in your project. Codex loads it when a task matches its description.
Can I use Bigquery Bigframes in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill bigquery-bigframes -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bigquery-bigframes, .gemini/skills/bigquery-bigframes, .github/skills/bigquery-bigframes and .opencode/skills/bigquery-bigframes in your project.
What does Bigquery Bigframes need to run?
SKILL.md names no scripts, command-line tools or credentials: Bigquery Bigframes is instructions for the agent only. Our summary lists: Python 3.
Does Bigquery Bigframes access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Bigquery Bigframes safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Bigquery Bigframes use?
Bigquery Bigframes is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Bigquery Bigframes use?
About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 617 tokens, read only when the agent opens those files.
What are the alternatives to Bigquery Bigframes?
Skills that share tags, products or a category with Bigquery Bigframes: Chdb Datastore (vemetric/vemetric, 394 stars), Chdb SQL (vemetric/vemetric, 394 stars), Analysis Artifacts (warpdotdev/oz-skills, 825 stars) and Imaging Data Commons (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Bigquery Bigframes?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.