OpenAgentSkill
GitHub200+
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

查看并核实来源在 GitHub 查看
价格未确认★ 530 GitHub Stars目录更新于 · 2026年10月9日agent-skill

概览

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.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

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.

查看来源 ↗
文件元数据
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
查看原始文本
---
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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安装前审查: 避免自动安装

许可证: 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
打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

  1. 1阅读来源，确认输入、预期输出、依赖和权限。
  2. 2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
  3. 3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库
ClickHouse/agent-skills
许可证
Apache-2.0
版本
1.0.0
最近 GitHub 推送
2026年8月6日
目录更新于
2026年10月9日
技能指令路径
skills/chdb-datastore/SKILL.md @ 5aec31143796

版本来自目录元数据，使用前请核实来源发布记录。

质量

71/100

强

信任

63/100

仅限沙盒

审计

76/100

需审查

打开完整审计查看评估报告
  • 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
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复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

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      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "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"
    ]
  },
  "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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