Databricks Connector

Databricks Connector

Quick Reference

Why This Skill Exists

We need this skill because Databricks workflows degrade when auth, schema, and side-effect handling drift when integrations are run ad hoc. This specific skill turns Databricks Connector into a deterministic, auth-checked workflow for unified read/write connector for databricks apis and data..

Trigger Checklist

• The task explicitly requires Databricks Connector rather than generic brainstorming.
• The provider tenant, workspace, or environment is known before execution begins.
• Credential reuse has been checked before asking for new secrets.
• Success criteria, side effects, and handoff owner are clear.
• If the run mutates provider state, the relevant approval gates are available.

Auth & Access Profile

Inputs (contract)

Outputs (contract)

Step-by-Step Implementation Guide

1. Define the objective, target resources, and permitted side effects for Databricks Connector, then confirm the smallest read-only probe that proves access to Databricks.
2. Resolve credential reuse first, validate identity and scopes, and only ask for new secrets if the existing auth context is missing, invalid, or expired.
3. Specify the request and response contract for unified read/write connector for databricks apis and data., including idempotency rules and provider rate-limit posture.
4. Execute the read-only probe, widen to the minimum required write scope, and capture deterministic request/response traces for every call.
5. Run integration and regression checks that cover auth expiry, scope mismatch, and rate limiting before approving downstream automation reuse.
6. Publish a connection bundle with scopes, created artifacts, and next-action guidance for any workflow that depends on this provider session.

Operational Runbook

Preflight

• Verify credential reuse candidates before asking for new secrets.
• Start with the minimum read-only probe that proves identity, scope, and rate-limit posture.

Execution

• Execute the smallest valid API operation first, then widen scope only after validation passes.
• Capture endpoint, request shape, and response identifiers in the handoff bundle.

Recovery

• On auth or permission failure, stop writes, preserve diagnostics, and fall back to read-only verification.
• On mutating error, revoke the write session and reconcile resulting state before retry.

Handoff

• Return the validated connection state, scopes used, and any newly created IDs or URLs.
• Document whether the session is safe for downstream automation or requires human re-approval.

Validation Gates & Test Matrix

• Required validation suites: unit, integration, regression-baseline

Failure Modes & Recovery Playbook

Tool Call Implementation

• Reuse existing credentials first. Check environment variables, secure stores, and active sessions before prompting.
• Start with the smallest authenticated read or validation call that proves identity and scope.
• Preserve request, response, and approval traces in databricks-connection-trace-bundle so downstream owners do not need to rediscover context.
• If any auth, contract, or approval gate fails, halt execution and attach remediation guidance instead of guessing.

Credential Reuse Policy

• Reuse valid provider credentials by default and prefer tenant-scoped sessions over newly created secrets.
• Prompt for credentials only when they are missing, invalid, expired, or point at the wrong environment.
• For webhook flows, validate the signing secret against a known sample before accepting live traffic.

Guardrails

• quality: Require an auth preflight and read-only probe before any write-capable call. (auth-preflight+read-probe)
• reliability: Throttle retries and reconcile provider state after write failures. (retry-budget+state-reconcile)
• compliance: Enforce least privilege and preserve the scopes used for the run. (scope-audit)

Acceptance Checklist

• Credential preflight and scope validation completed successfully.
• Required validation suites ran and all fail-closed gates passed.
• databricks-connection-trace-bundle, scorecard, and handoff packet were produced.
• Any mutations, approvals, or rollbacks are reflected in the artifact bundle.

Anti-Patterns

• Do not ask for new credentials before checking reusable auth context.
• Do not skip the read-only or dry-run validation step for mutating work.
• Do not proceed when approval gates, signing secrets, or rollback checkpoints are missing.
• Do not hand off partial or ambiguous provider state as complete.

Handoff Contract

• Produces: databricks-connection-trace-bundle, execution scorecard, approval trace, and next actions.
• Consumes: auth context, resource scope, request contract.
• Readiness rule: release only after auth, contract, approval, and reliability gates all pass.
• Downstream hint: route to databricks:connector consumers with approval and credential context attached.

Observability & Continuous Improvement

• SLO: >=99.7% successful runs per 7-day window
• Error budget: <=0.3% critical failures per 7-day window
• Alert triggers:
• credential validation failures exceed baseline
• schema or contract regressions persist for two consecutive runs
• critical posture or rollback events exceed tolerance
• Primary outcome metric: auth success rate
• Secondary metrics: request validation pass rate, side-effect correctness
• Review cadence: weekly