Chdb SQL

What it does

When your agent uses it

• The user wants to run SQL — especially analytical SQL — on local files (parquet/csv/json)
• Remote databases (Postgres
• ClickHouse Cloud
• Delta Lake) without setting up a server

Example prompts

• “/chdb-sql”

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 6b7b01a. 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 SQL loads about 1.2k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 218 tokens; SKILL.md has 166 words of instructions outside code blocks.

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

~218

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

~1.2k

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

~6.7k

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 6b7b01a, republished under its Apache-2.0 licence (© vemetric). 166 words, ~1,237 tokens.

name

chdb-sql

description

Use when the user wants to run SQL — especially analytical SQL — on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB — embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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

pip install chdb

1. One-off query on files or databases → chdb.query()
2. Multi-step analysis with tables      → Session
3. DB-API 2.0 connection                → chdb.connect()
4. Pandas-style DataFrame operations    → Use chdb-datastore skill instead

import chdb

chdb.query("SELECT * FROM file('data.parquet', Parquet) WHERE price > 100 LIMIT 10")       # local files
chdb.query("SELECT * FROM mysql('db:3306', 'shop', 'orders', 'root', 'pass')")              # databases
chdb.query("SELECT * FROM s3('s3://bucket/data.parquet', NOSIGN) LIMIT 10")                 # cloud storage
chdb.query("SELECT * FROM deltaLake('s3://bucket/delta/table', NOSIGN) LIMIT 10")           # data lakes

# Cross-source join
chdb.query("""
    SELECT u.name, o.amount FROM mysql('db:3306', 'crm', 'users', 'root', 'pass') AS u
    JOIN file('orders.parquet', Parquet) AS o ON u.id = o.user_id ORDER BY o.amount DESC
""")

data = {"name": ["Alice", "Bob"], "score": [95, 87]}
chdb.query("SELECT * FROM Python(data) ORDER BY score DESC")                                # Python data
df = chdb.query("SELECT * FROM numbers(10)", "DataFrame")                                   # output formats
chdb.query("SELECT toDate({d:String}) + number FROM numbers({n:UInt64})",
    "DataFrame", params={"d": "2025-01-01", "n": 30})                                      # parametrized

from chdb import session as chs
sess = chs.Session("./analytics_db")   # persistent; Session() for in-memory

sess.query("CREATE TABLE users ENGINE=MergeTree() ORDER BY id AS SELECT * FROM mysql('db:3306','crm','users','root','pass')")
sess.query("CREATE TABLE events ENGINE=MergeTree() ORDER BY (ts,user_id) AS SELECT * FROM s3('s3://logs/events/*.parquet',NOSIGN)")
sess.query("""
    SELECT u.country, count() AS cnt, uniqExact(e.user_id) AS users
    FROM events e JOIN users u ON e.user_id = u.id
    WHERE e.ts >= today() - 7 GROUP BY u.country ORDER BY cnt DESC
""", "Pretty").show()
sess.close()

from chdb import dbapi
conn = dbapi.connect()
cur = conn.cursor()
cur.execute("SELECT * FROM file('data.parquet', Parquet) WHERE value > 100")
print(cur.fetchall())
cur.close()
conn.close()

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

• SKILL.md
• README.md
• examples/examples.md
• references/api-reference.md
• references/sql-functions.md
• references/table-functions.md
• scripts/verify_install.py

Open the folder on GitHubat commit 6b7b01a

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in vemetric/vemetric, which our catalogue first saw on October 7, 2026.

• ClickHouse/agent-skillsskills/chdb-sql