القائمة
Find a dlt source for a given API or data provider. Use when the user asks about a source, wants to find a connector, or asks to implement a pipeline for a specific data source.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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 | find-source |
| description | Find a dlt source for a given API or data provider. Use when the user asks about a source, wants to find a connector, or asks to implement a pipeline for a specific data source. |
| argument-hint | [source-name] [context] |
Locate the best dlt source for what the user wants to extract data from.
Parse $ARGUMENTS:
source-name (required): what the user wants to extract data from (e.g., "alpaca markets", "stripe", "postgres", "csv files", "rest api")| User says (examples) | Core source |
|---|---|
| postgres, mysql, mssql, oracle, database, db, sql | sql_database |
| rest api, http api, web api, rest | rest_api |
| files, csv, parquet, jsonl, s3, gcs, azure blob, local files | filesystem |
If it matches a core source, skip to step 5 and report the core source match.
If the request looks like a specific API/service name, run:
dlthub --non-interactive pipeline init --list-sources
Search the output (case-insensitive) for the source name. If found, ensure that the verified source contains the data that the user needs (ask the user explicitly) skip to step 5
Use search_dlthub_sources mcp tool to look for sources. It is full-text search based so pass only essential keywords to it
ie. "claude analytics". You'll get description of the source and set of reference links to use in web search below.
query: <source-name> API documentation
NOTE: we can handle only REST API (step 5) and sometimes GraphQL.
This toolkit builds REST API pipelines. Before continuing, check if the user's data source actually fits.
STOP and hand off if any of these are true:
rest_api — the user needs sql_database, filesystem, or another core source. For filesystem, hand off to the filesystem-pipeline toolkit (create-filesystem-pipeline). For others, tell them which one and the dlthub pipeline init command, then suggest a general coding session.dlthub pipeline init <source> <destination> command. Suggest they try the verified source first.Found: <verified source or non-REST core source>
Command: dlthub pipeline init <source> <destination>
This is outside the REST API pipeline workflow. You can:
1. Use the verified source / core source above (recommended)
2. Start a general coding session if you need a custom pipeline
CONTINUE only when the best path is building a REST API pipeline — either because:
rest_api core source under the hood)Ask user to pick a single endpoint to start the work - do it directly or infer it from conversation.
Do NOT run dlthub pipeline init yet — wait for user confirmation.
After that continue workflow in create-rest-api-pipeline skill