aidp-sqlserver

Included with Lifetime

$97 forever

Read or write Microsoft SQL Server from an AIDP notebook via the AIDP `aidataplatform` Spark format handler. Use when the user mentions SQL Server, MSSQL, Azure SQL Database, or has a TDS host/port. Auth is host/port + database + user/password.

Backend & APIs

What this skill does


# `aidp-sqlserver` — Microsoft SQL Server via AIDP `aidataplatform`

## When to use
- Read or write a Microsoft SQL Server (or Azure SQL Database) from an AIDP notebook.
- Mentioned: "SQL Server", "MSSQL", "Azure SQL", "TDS".

## When NOT to use
- For Postgres → [`aidp-postgresql`](../aidp-postgresql/SKILL.md).
- For MySQL → [`aidp-mysql`](../aidp-mysql/SKILL.md).

## Read
```python
import os
from oracle_ai_data_platform_connectors.aidataplatform import (
    AIDP_FORMAT, aidataplatform_options,
)

opts = aidataplatform_options(
    type="SQLSERVER",
    host=os.environ["MSSQL_HOST"],
    port=int(os.environ.get("MSSQL_PORT", "1433")),
    user=os.environ["MSSQL_USER"],
    password=os.environ["MSSQL_PASSWORD"],
    schema=os.environ.get("MSSQL_SCHEMA", "dbo"),
    table=os.environ["MSSQL_TABLE"],
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()
df.show(5)
```

## Write
```python
opts = aidataplatform_options(
    type="SQLSERVER",
    host=os.environ["MSSQL_HOST"],
    port=int(os.environ.get("MSSQL_PORT", "1433")),
    database_name=os.environ["MSSQL_DATABASE"],   # required on write
    user=os.environ["MSSQL_USER"],
    password=os.environ["MSSQL_PASSWORD"],
    schema=os.environ.get("MSSQL_SCHEMA", "dbo"),
    table=os.environ["MSSQL_TARGET_TABLE"],
    extra={"write.mode": "CREATE"},
)
df.write.format(AIDP_FORMAT).options(**opts).save()
```

## Gotchas
- **`database.name` is required on write but not strictly on read.** If a read fails with "object not found", supply `database_name=` too — some SQL Server installs require it for the connector to disambiguate.
- **Schema vs database**: SQL Server has both. `schema` here is the SQL-Server schema (typically `dbo`); `database.name` is the catalog (e.g. `master`, `AdventureWorks`).
- **TDS port 1433** by default. Azure SQL Database exposes port 1433 over public TLS; AIDP must reach it via its egress route.
- **Azure SQL** uses the same `SQLSERVER` type. Use the fully-qualified host (`<srv>.database.windows.net`) and the SQL-auth user (NOT Active Directory; AIDP doesn't broker AAD tokens).

## References
- Helper: [scripts/oracle_ai_data_platform_connectors/aidataplatform.py](../../scripts/oracle_ai_data_platform_connectors/aidataplatform.py)
- Official sample: [oracle-samples/oracle-aidp-samples → `data-engineering/ingestion/Read_Write_External_Ecosystem_Connectors.ipynb`](https://github.com/oracle-samples/oracle-aidp-samples/blob/main/data-engineering/ingestion/Read_Write_External_Ecosystem_Connectors.ipynb)

Files: 1

Size: 2.8 KB

Complexity: 7/100

Category: Backend & APIs

Source: https://github.com/oracle-samples/oracle-aidp-samples/tree/main/ai/claude-code-plugins/oracle-ai-data-platform-workbench-spark-connectors/skills/aidp-sqlserver

Related in Backend & APIs

jfrog

Included

Interact with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.

Backend & APIsscripts

cupynumeric-migration-readiness

Included

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Backend & APIsscripts

alibabacloud-data-agent-skill

Included

Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".

Backend & APIsscripts

token-optimizer

Included

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Backend & APIsscripts

resend-cli

Included

Use this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.

Backend & APIsscripts

alibabacloud-odps-maxframe-coding

Included

Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.

Backend & APIsscripts