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docker-databricks-lab-ops

DevOps Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
DevOps
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@tomevault-io · no license declared
Token usage
Lean
Setup complexity
Manual integration
External API key
Not required
Operating systems
Docker
Runtime requirements
Docker
Permissions
  • Read-only
  • Write / modify
  • Shell exec
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview docker-databricks-lab-ops.preview 
---
name: docker-databricks-lab-ops
description: Start and verify the local Docker CDC lab (dvdrental), run the PostgreSQL load generators, reset…
category: devops
runtime: Docker
---

# docker-databricks-lab-ops output preview

## PART A: Task fit
- Use case: Start and verify the local Docker CDC lab (dvdrental), run the PostgreSQL load generators, reset Databricks tables, trigger Databricks notebook jobs through databricks.sdk, and check whether Bronze/Silver notebooks completed successfully. Use when Codex needs to bring up the repo's local infrastructure, generate CDC traffic, reset or clean Databricks tables, execute Databricks jobs, poll run status, inspect failures, or validate notebook outputs for this lab. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Workflow / 1. Inspect the repo inputs first” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Start and verify the local Docker CDC lab (dvdrental), run the PostgreSQL load generators, reset Databricks tables, trigger Databricks notebook jobs through databricks.sdk, and check whether Bronze/Silver notebooks completed successfully. Use when Codex needs to bring up the repo's local infrastructure, generate CDC traffic, reset or clean Databricks tables, execute Databricks jobs, poll run status, inspect failures, or validate notebook outputs for this lab. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Overview / Workflow / 1. Inspect the repo inputs first” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Start and verify the local Docker CDC lab (dvdrental), run the PostgreSQL load generators, reset Databricks tables, trigger Data…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Overview”, “Workflow”, “1. Inspect the repo inputs first”, “2. Bring up the local CDC stack”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name docker-databricks-lab-ops directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Overview / Workflow / 1. Inspect the repo inputs first” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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