Chdb Datastore

What it does

When your agent uses it

• The user has tabular data (pandas DataFrame
• Json) and wants to filter
• Speed up slow pandas
• : user mentions DataFrame

Example prompts

• “fast pandas”
• “speed up pandas”
• “/chdb-datastore”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.9+, macOS or Linux. pip install chdb.

What it can do on your machine

Read from SKILL.md and the folder at commit 2352ee8. 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

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • pip
  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • clickhouse.com

  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.

• Compatibility

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

  From compatibility in the SKILL.md frontmatter.

Context cost

Chdb Datastore loads about 1.4k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 173 tokens; SKILL.md has 210 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~173

When it runs · the whole SKILL.md, loaded when a task matches

~1.4k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.4k

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

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); the scripts in this folder are not scanned.

SKILL.md

The full file from vemetric/vemetric at commit 2352ee8, republished under its Apache-2.0 licence (© vemetric). 210 words, ~1,381 tokens.

name

chdb-datastore

description

Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

compatibility

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

license

Apache-2.0

metadata.author

chdb-io

metadata.version

4.1

metadata.homepage

https://clickhouse.com/docs/chdb

# Change this:
import pandas as pd
# To this:
import chdb.datastore as pd
# Everything else stays the same.

pip install chdb

1. "I have a file/database and want to analyze it with pandas"
   → DataStore.from_file() / from_mysql() / from_s3() etc.
   → See references/connectors.md

2. "I need to join data from different sources"
   → Create DataStores from each source, use .join()
   → See examples/examples.md #3-5

3. "My pandas code is too slow"
   → import chdb.datastore as pd — change one line, keep the rest

4. "I need raw SQL queries"
   → Use the chdb-sql skill instead

from datastore import DataStore

# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)
ds = DataStore.from_file("sales.parquet")

# Database
ds = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")

# Cloud storage
ds = DataStore.from_s3("s3://bucket/data.parquet", nosign=True)

# URI shorthand — auto-detects source type
ds = DataStore.uri("mysql://root:pass@db:3306/shop/orders")

result = ds[ds["age"] > 25]                                          # filter
result = ds[["name", "city"]]                                        # select columns
result = ds.sort_values("revenue", ascending=False)                  # sort
result = ds.groupby("dept")["salary"].mean()                         # groupby
result = ds.assign(margin=lambda x: x["profit"] / x["revenue"])     # computed column
ds["name"].str.upper()                                               # string accessor
ds["date"].dt.year                                                   # datetime accessor
result = ds1.join(ds2, on="id")                                      # join
result = ds.head(10)                                                 # preview
print(ds.to_sql())                                                   # see generated SQL

from datastore import DataStore

customers = DataStore.from_mysql(host="db:3306", database="crm", table="customers", user="root", password="pass")
orders = DataStore.from_file("orders.parquet")

result = (orders
    .join(customers, left_on="customer_id", right_on="id")
    .groupby("country")
    .agg({"amount": "sum", "rating": "mean"})
    .sort_values("sum", ascending=False))
print(result)

source = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")
target = DataStore("file", path="summary.parquet", format="Parquet")

target.insert_into("category", "total", "count").select_from(
    source.groupby("category").select("category", "sum(amount) AS total", "count() AS count")
).execute()

© vemetric, 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 5 other files (scripts, references) in .agents/skills/chdb-datastore of vemetric/vemetric.

• SKILL.md
• README.md
• examples/examples.md
• references/api-reference.md
• references/connectors.md
• scripts/verify_install.py

Open the folder on GitHubat commit 2352ee8

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in vemetric/vemetric, which our catalogue first saw on October 7, 2026.

• ClickHouse/agent-skillsskills/chdb-datastore
• Kilo-Org/kilo-marketplaceskills/chdb-datastore