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Verify dlthub workspace is ready for dltHub Platform. Use when user wants to deploy for the first time, or when another skill reports missing prerequisites like .workspace file or dlt[hub] dependency.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Build and deploy a minimal custom REST API pipeline to dltHub Platform. Use when the user says "Help me build and deploy a minimal pipeline", "I've finished onboarding, now what?", or "I just ran dlthub-start and want to build my own pipeline".
Deploy and run the pre-shipped Jaffle Shop sample pipeline on dltHub Platform — the final onboarding step after uvx dlthub-start. Use when the user says 'deploy the pipeline', 'deploy the pre-built pipeline', 'deploy the onboarding pipeline', 'do the onboarding task', 'finish the onboarding task', or is ready to complete onboarding. Assumes scaffolding, login, and playground workspace connection are already done.
The guided entry point for dltHub workbench use cases — ingestion from APIs, data exploration, transformations, deployment, data quality. Use when the user names a use case or wants to be oriented before starting: 'I want to ingest from Stripe', 'show me how to go from data to dashboard', 'take me through the full workflow', 'explore the workbench', 'what can I do with dlthub', 'give me a quick start', 'show me a demo', 'walk me through ingestion to visualization', 'I want to try everything end-to-end', 'teach me dltHub'. Do NOT use when the user is asking what's available or where to start in general — use the `dlthub-router` skill (in init) for capability-discovery questions ('what can you do', 'what toolkits are there', 'I'm new to dlthub'). Do NOT use when the user already has a specific task underway (debugging, adding an endpoint, deploying).
Create trigger evaluation setup for a toolkit skill. Use when the user wants to test whether a skill's description triggers correctly, set up eval workspaces, or generate trigger test queries for a skill. Use when user says 'create eval', 'test triggers', 'eval skill', or wants to measure skill triggering accuracy.
Run trigger evaluation for a skill and analyze results. Use when the user wants to run evals, check trigger accuracy, analyze clashes, or improve a skill description based on eval results. Use when user says 'run eval', 'test triggers', 'check skill triggering', or 'analyze eval results'.
The entry point for building anything with dlthub. Use this skill to route the user to the right workflow toolkit and install it on demand. MUST use when the user asks 'what can you do', 'what can I build', 'what are toolkits', 'how do I build a pipeline', 'I want to pull data from a REST API', 'ingest from a SQL database', 'load CSVs from S3', 'make reports / dashboards', 'transform / model my data', 'add data quality checks', 'how do I deploy / schedule a pipeline', 'I'm new to dlthub', 'where do I start', or seems unsure what to do next after setup. Also use whenever the user expresses a data-engineering goal but no matching workflow toolkit is installed yet — this skill installs it on demand. Do NOT use when the toolkit matching the user's intent is already installed — go straight to its entry skill instead; only route/install when the matching toolkit is missing. Do NOT use when a specific task is already in progress (debugging a pipeline, validating data, adding endpoints) and its toolkit is installed.
| name | setup-runtime |
| description | Verify dlthub workspace is ready for dltHub Platform. Use when user wants to deploy for the first time, or when another skill reports missing prerequisites like .workspace file or dlt[hub] dependency. |
Lightweight check that the workspace is ready for runtime work. Run through each check and fix issues as found.
Reference:
dlt[hub], create a workspace): https://dlthub.com/docs/hub/getting-started/installation.mdCheck pyproject.toml exists in the project root. If not:
uv init
dltHub Platform uses pyproject.toml to install dependencies remotely.
.dlt/.workspace filels .dlt/.workspace
This file enables profiles and the runtime CLI. If missing, use dlthub init (preferred):
dlthub init # creates .dlt/.workspace, prompts for workspace name
dlthub init --name <workspace> # skip prompt
dlthub init --dry-run # preview only
Or manually as fallback: touch .dlt/.workspace
Heads up: the workspace description shown in the dltHub Platform UI comes from the first line of the docstring in
__deployment__.py. You can set it now or later when creating the manifest in (prepare-deployment).
dlt[hub] dependencyVerify dlt with the hub extra is installed:
uv pip show dlt
If not installed or missing the hub extra:
uv add "dlt[hub]"
If adding dlt to pyproject.toml, pin the exact installed version (==) — uv add may downgrade pre-release versions.
dlthub login
dlthub workspace connect # interactive prompt to select or create
dlthub workspace connect <name_or_id> # skip prompt
dlthub workspace connect <name_or_id> --org-id <id> # specify org
config.toml under [runtime] workspace_iddlthub workspace connect <name_or_id>dlthub logoutls .dlt/*.toml
List existing config and secrets files. At minimum these should exist:
.dlt/config.toml.dlt/secrets.toml.dlt/.workspaceProfile-scoped files (dev.*, prod.*, access.*) may or may not exist yet — that's fine, (prepare-deployment) handles their creation.
Tell the user what's present and what the next step is: use (prepare-deployment) to set up production credentials and destinations.