OpenAgentSkill
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ClickHouse

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chdb-datastore

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

Revisar el código fuenteVer en GitHub
Precio sin confirmar★ 530 Estrellas de GitHubRegistro actualizado · 9 oct 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

chdb DataStore — It's Just Faster Pandas

The Key Insight

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

DataStore is a lazy, ClickHouse-backed pandas replacement. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., print(), len(), iteration).

pip install chdb

Decision Tree: Pick the Right Approach

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

Connect to Any Data Source — One Pattern

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")

All 16+ sources and URI schemes → connectors.md

After Connecting — Full Pandas API

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

209 DataFrame methods supported. Full API → api-reference.md

Cross-Source Join — The Killer Feature

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)

More join examples → examples.md

Writing Data

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()

Troubleshooting

ProblemFix
ImportError: No module named 'chdb'pip install chdb
ImportError: cannot import 'DataStore'Use from datastore import DataStore or from chdb.datastore import DataStore
Database connection timeoutInclude port in host: host="db:3306" not host="db"
Join returns empty resultCheck key types match (both int or both string); use .to_sql() to inspect
Unexpected resultsCall ds.to_sql() to see the generated SQL and debug
Environment checkRun python scripts/verify_install.py (from skill directory)

References

Note: This skill teaches how to use chdb DataStore. For raw SQL queries, use the chdb-sql skill. For contributing to chdb source code, see CLAUDE.md in the project root.

Ver fuente ↗
Metadatos del archivo
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.
license: Apache-2.0
compatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.
metadata:
  author: chdb-io
  version: "4.1"
  homepage: https://clickhouse.com/docs/chdb
Ver texto original
---
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.
license: Apache-2.0
compatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.
metadata:
  author: chdb-io
  version: "4.1"
  homepage: https://clickhouse.com/docs/chdb
---

# chdb DataStore — It's Just Faster Pandas

## The Key Insight

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

DataStore is a **lazy, ClickHouse-backed pandas replacement**. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., `print()`, `len()`, iteration).

```bash
pip install chdb
```

## Decision Tree: Pick the Right Approach

```
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
```

## Connect to Any Data Source — One Pattern

```python
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")
```

All 16+ sources and URI schemes → [connectors.md](references/connectors.md)

## After Connecting — Full Pandas API

```python
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
```

209 DataFrame methods supported. Full API → [api-reference.md](references/api-reference.md)

## Cross-Source Join — The Killer Feature

```python
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)
```

More join examples → [examples.md](examples/examples.md)

## Writing Data

```python
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()
```

## Troubleshooting

| Problem | Fix |
|---------|-----|
| `ImportError: No module named 'chdb'` | `pip install chdb` |
| `ImportError: cannot import 'DataStore'` | Use `from datastore import DataStore` or `from chdb.datastore import DataStore` |
| Database connection timeout | Include port in host: `host="db:3306"` not `host="db"` |
| Join returns empty result | Check key types match (both int or both string); use `.to_sql()` to inspect |
| Unexpected results | Call `ds.to_sql()` to see the generated SQL and debug |
| Environment check | Run `python scripts/verify_install.py` (from skill directory) |

## References

- [API Reference](references/api-reference.md) — Full DataStore method signatures
- [Connectors](references/connectors.md) — All 16+ data source connection methods
- [Examples](examples/examples.md) — 10+ runnable examples with expected output
- [Verify Install](scripts/verify_install.py) — Environment verification script
- [Official Docs](https://clickhouse.com/docs/chdb)

> Note: This skill teaches how to *use* chdb DataStore.
> For raw SQL queries, use the `chdb-sql` skill.
> For contributing to chdb source code, see CLAUDE.md in the project root.

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Licencia
Apache-2.0
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Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: Apache-2.0

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

  1. 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
  2. 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
  3. 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente
ClickHouse/agent-skills
Licencia
Apache-2.0
Versión
1.0.0
Último push de GitHub
6 ago 2026
Registro actualizado
9 oct 2026
Ruta de instrucciones
skills/chdb-datastore/SKILL.md @ 5aec31143796

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

71/100

Sólido

Confianza

63/100

Solo sandbox

Auditoría

76/100

Requiere revisión

Abrir auditoría completaVer informe de evaluación
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Verified installs
—
Resultados
—

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

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Más detalles
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      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 71,
    "label": "Strong"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Data analysis",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "pathwaycom-llm-app",
      "name": "Llm App",
      "url": "https://www.openagentskill.com/skills/pathwaycom-llm-app",
      "stars": 59299,
      "install_command": "",
      "trust_score": 90,
      "audit_score": 91
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use chdb-datastore in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "clickhouse-chdb-datastore (chdb-datastore)",
      "install_command": "npx skills add ClickHouse/agent-skills --skill chdb-datastore",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "clickhouse-chdb-datastore",
      "task": "Use chdb-datastore in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/clickhouse-chdb-datastore",
    "api": "https://www.openagentskill.com/api/agent/skills/clickhouse-chdb-datastore",
    "audit": "https://www.openagentskill.com/skills/clickhouse-chdb-datastore/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=clickhouse-chdb-datastore&task=Use%20chdb-datastore%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chdb-datastore%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chdb-datastore%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/clickhouse-chdb-datastore/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/clickhouse-chdb-datastore"
  }
}

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Creador
ClickHouse
Indexado por
Índice comunitario de OpenAgentSkill

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