Chdb Datastore

What it does

This source presents DataStore as a lazy pandas-style interface: filtering, grouping and joining are planned before evaluation. It routes connector setup, supported methods and worked examples into separate references. The distinction between constructing a plan and actually materializing results matters when diagnosing performance.

Best for

Analysts with existing pandas-shaped transformations who want to investigate a different execution engine, or combine a local file with a database table.

Tradeoffs and requirements

API resemblance does not prove identical behavior for every pandas operation. The declared environment is Python on macOS or Linux. Remote connectors also introduce credentials, network permissions and data-transfer costs that a local demonstration does not exercise.

Requires Python 3.9+, macOS or Linux. pip install chdb.

Install from the source

Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code; this catalog never runs it.

npx skills add https://github.com/ClickHouse/agent-skills/tree/69a8354b05b5942e5cea77b6c87be7788303fc18/skills/chdb-datastore --skill chdb-datastore

Review pinned skill folder ↗ · CLI documentation ↗

After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.

Safety & review signals

Static review only. This is not a security audit or a guarantee of safety.

SKILL.md text only; scripts, dependencies and runtime behavior not audited.

• Network Access — review the source context.
• Shell Or Package Install — review the source context.

Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.

AI Craft Pad Skill Score

86/100 · Metadata coverage 100% · Model 1.0

Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.

Documentation

100/100

100

Maintenance

100/100

100

License Clarity

100/100

100

Repository Health

100/100

100

Compatibility Evidence

80/100

80

Popularity

50/100

50

Originality

80/100

80

Static Safety

60/100

60

Supporting-file static inspection

Bounded source inspection, not a runtime security audit. No source code was executed.

2 text files inspected at this exact repository snapshot. 4 files outside the inspection budget or type allowlist. Dependencies were not installed or independently audited.

• Network Client: 1 file — inspect context before use.

Absence of a pattern is not proof of safety. Permission needs and runtime behavior remain Unknown.

Suggested workflow

Start with a small representative file, inspect the generated SQL, then add one remote connector. Keep the original pandas calculation available as an independent reference before attempting a larger join.

How to judge the result

Compare row counts, null handling, join cardinality and numeric aggregates against the reference. Measure the complete evaluation, including materialization and transfer, rather than timing only the lazy expression construction.