atlan-sql-connector-patterns
Select and apply the correct SQL connector implementation pattern (SDK-default minimal or source-specific custom). Use when building or extending SQL metadata/query extraction connectors.
Best use case
atlan-sql-connector-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Select and apply the correct SQL connector implementation pattern (SDK-default minimal or source-specific custom). Use when building or extending SQL metadata/query extraction connectors.
Teams using atlan-sql-connector-patterns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/atlan-sql-connector-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How atlan-sql-connector-patterns Compares
| Feature / Agent | atlan-sql-connector-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Select and apply the correct SQL connector implementation pattern (SDK-default minimal or source-specific custom). Use when building or extending SQL metadata/query extraction connectors.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Atlan SQL Connector Patterns Choose the right connector strategy and implement it consistently. ## Workflow 1. Use `references/decision-tree.md` to choose `postgres-minimal` or `redshift-custom`. 2. Implement required components for selected path. 3. Verify auth, preflight, workflow map, and transformation behavior against references. 4. Run `atlan-fact-verification-gate` if requirements imply source-specific behavior or SDK override risk. 5. Hand off to `atlan-e2e-contract-validator` for contract generation. ## Rules - Default to minimal path unless requirements justify custom path. - For custom path, explicitly document why SDK defaults are insufficient. - Reuse source-specific patterns only when corresponding requirements are present. ## References - Decision tree: `references/decision-tree.md` - Shared verification map: `../_shared/references/verification-sources.md`
Related Skills
atlan-workflow-args-secrets-state
Implement workflow argument retrieval, credential_guid usage, and state store updates using Atlan SDK patterns. Use when workflows or activities handle credentials, runtime args, or persisted workflow state.
atlan-sdk-objectstore-io-defaults
Enforce object store and IO defaults from the Atlan SDK for output paths, prefixes, and file writes. Use when implementing or reviewing raw/transformed output handling.
atlan-review-doc-sync
Run findings-first review for Atlan app changes and synchronize app documentation with implemented behavior. Use when completing a change set, preparing handoff, or auditing regressions.
atlan-fact-verification-gate
Verify Atlan app behavior against SDK docs/code and CLI docs/code before behavior-changing decisions; use lightweight checks by default and deep checks when risk is high.
atlan-e2e-contract-validator
Generate and validate e2e test contracts for Atlan workflows, including API responses, output paths, and schema assertions. Use when adding or updating workflow e2e coverage.
atlan-cli-run-test-loop
Run Atlan app execution loops using CLI-first commands with automatic CLI availability checks and safe fallbacks.
atlan-cli-install-configure
Install Atlan CLI with OS-aware binaries (or Homebrew on macOS) only when missing, then configure baseline tenant auth/log settings.
atlan-app-scaffold-standard
Scaffold new Atlan apps from CLI templates as the default behavior when users ask for a new app, then align files to sample-app standards.
detecting-mimikatz-execution-patterns
通过命令行模式、LSASS 访问签名、二进制指标和已知模块的内存检测,检测 Mimikatz 的执行。
detecting-beaconing-patterns-with-zeek
对 Zeek conn.log 连接间隔进行统计分析,检测 C2 信标(Beaconing)模式。使用 ZAT 库将 Zeek 日志加载到 Pandas DataFrame,计算到达时间间隔标准差,标记具有低抖动(Low Jitter)的周期性连接。适用于在网络数据中狩猎命令与控制(C2)回调行为。
detecting-anomalous-authentication-patterns
使用用户和实体行为分析(UEBA)分析、统计基线和机器学习模型检测异常认证模式,识别不可能旅行、撞库攻击、暴力破解、密码喷洒和账户被攻陷行为,覆盖所有认证日志来源。 适用于认证异常检测、登录行为分析、UEBA 实施或可疑登录调查等相关请求。
analyzing-cloud-storage-access-patterns
通过分析 CloudTrail Data Events、GCS 审计日志和 Azure Storage Analytics,检测 AWS S3、GCS 和 Azure Blob Storage 中的异常访问模式。识别非工作时间的批量下载、来自新 IP 地址的访问、异常 API 调用(GetObject 峰值)以及使用统计基线和时间序列异常检测的潜在数据外泄行为。