databricks-helper
v1.1.0Query and control Databricks jobs via text by checking status, listing recent runs, finding failures, and triggering pipelines using the REST API.
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by@nerikko
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
The skill's name, description, SKILL.md, README, and bundled Python code all align: they call Databricks REST APIs to list/run/retry/cancel jobs and to run SQL and Unity Catalog queries. The requested permissions (CAN_VIEW, CAN_MANAGE_RUN, SQL warehouse access) are appropriate for the claimed features.
Instruction Scope
Runtime instructions and the code show only Databricks API calls (host/api/...), SQL execution against a configured warehouse, and local output formatting. There are no instructions to read unrelated files, contact external endpoints outside the configured Databricks host, or exfiltrate data to third-party hosts in the provided materials.
Install Mechanism
There is no remote download/install step; the package includes Python scripts that use only the stdlib (urllib). No extract-from-arbitrary-URL or third-party package installs are present, which is low-risk for install-time code execution.
Credentials
The SKILL.md, README, and code clearly require DATABRICKS_HOST, DATABRICKS_TOKEN, and (for SQL) DATABRICKS_SQL_WAREHOUSE_ID plus optional safety vars. However, the registry metadata lists no required environment variables and no primary credential. That mismatch is a transparency/integrity problem: the skill will need a secret token to function but the registry does not declare it or mark the token as the primary credential.
Persistence & Privilege
The skill is not forced-always (always:false), does not request system config paths, and contains no code that modifies other skills or global agent settings. Default autonomous invocation is allowed (normal).
What to consider before installing
This skill appears to implement the Databricks functionality it claims, but the registry metadata fails to declare the sensitive environment variables it needs. Before installing: 1) Ask the publisher to update the registry entry to declare DATABRICKS_HOST, DATABRICKS_TOKEN (as the primary credential), and DATABRICKS_SQL_WAREHOUSE_ID so you can see what secrets will be used. 2) Only provide a Databricks personal access token with least privilege: give separate tokens for read-only vs run/manage operations and avoid broad admin scopes. 3) Keep DATABRICKS_ALLOW_WRITE_SQL unset (default false) unless you intentionally need DDL/DML and trust the code. 4) Review or run the bundled tests locally and, if possible, audit the full databricks_helper.py (ensure there are no hidden network calls to unexpected domains). 5) Consider running the skill in a sandboxed agent or with a short-lived token first, and monitor Databricks audit logs for unexpected activity. These steps address the main transparency gap and reduce risk before trusting the skill with production credentials.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.