How do I install the Dlthub Router skill?

Run npx skillmds@latest add dlt-hub/dlthub-router in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Dlthub Router safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Dlthub Router?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Dlthub Router free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Dlthub Router?

dlt-hub (@dlt-hub) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Dlthub Router by dlt-hub — Agent Skill

Name: Dlthub Router
Author: dlt-hub

Dlthub Router

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.

dlt-hub Updated Sep 17, 2026

File contents

dlthub-router

Route the user to the right toolkit and skill, then install it. Fast path first — the always-loaded toolkit index (in your project rules / AGENTS.md) already maps intent → toolkit → install command → entry skill, so you usually do not need any discovery round-trip.

Router vs handovers. This skill handles cold start — picking and installing a toolkit when none relevant is installed. Once inside a workflow, a toolkit's workflow.md handover sections take over: they carry context forward (pipeline name, dataset, destination) and route to a specific skill. Do not use this skill mid-workflow when the relevant toolkit is already installed. But when a handover names a toolkit that isn't installed yet, that's your cue — install it via the index below, then follow the handover's entry point + context.

Step 1: Route from the always-loaded index (fast path)

The # toolkits index is already in your context. Match the user's intent to a row, then:

Install it: dlthub --non-interactive ai toolkit install <name>
Confirm (Step 3) and hand over to that toolkit's entry skill (Step 4).

This needs no MCP call — the index is authoritative for the shipped toolkits and is the fast path. Use it whenever the intent matches a row.

Step 2: Live discovery (fallback only)

Use this only when the index has no matching row (an unfamiliar need, or you suspect a newer toolkit exists):

Prefer MCP — list_toolkits from dlt-workspace-mcp for the live catalog, then toolkit_info <name> for skill details.
CLI fallback (MCP not connected): dlthub --non-interactive ai toolkit list, then dlthub --non-interactive ai toolkit info <name>.

Match intent to the best toolkit, then install as in Step 1. Toolkits marked (installed: <version>) are already available.

Step 3: Verify install (only when needed)

Skip this step when the install output already confirms success and the new toolkit's entry skill is available in this session — that is all the confirmation you need. (MCP health was already checked at session start via dlthub ai status.)

Run uv run dlthub ai status only if:

the install output was ambiguous or reported an error,
the entry skill doesn't appear to be available, or
the dlt-workspace-mcp server hasn't been verified this session (no session-start status check and no successful MCP call yet).

If status shows a WARNING about the MCP server (e.g. cannot be started), fix it using the error message before handing over.

Step 4: Handover (no restart needed)

The dlt-workspace-mcp server is already running (installed with init) and toolkits reuse it — installing one adds no new MCP server, so continue in this session. Do not ask the user to restart; that would lose the conversation context.

Load the new toolkit inline — prefer toolkit_info <name> (MCP), which is agent-agnostic and returns the entry skill + workflow rule. If MCP is unavailable, read the installed files directly; the install path depends on the agent (.claude/, .cursor/, or .agents/) — e.g. <agent-dir>/skills/<entry-skill>/SKILL.md and the toolkit's workflow rule.
Follow that workflow rule and start at the entry skill, continuing the user's task with the context you already have. Do not start unrelated workflows on your own.
The new skills become natively registered (/-invocable, always-loaded workflow rule) on the next natural session start — no need to restart now.

Exception: if a future toolkit ever ships its own MCP server (none do today), that server only starts on restart — suggest a restart only in that case, and use CLI fallbacks until then.

---name: dlthub-routerdescription: 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. ---# dlthub-routerRoute the user to the right toolkit and skill, then install it. **Fast path first** — the always-loaded toolkit index (in your project rules / `AGENTS.md`) already maps intent → toolkit → install command → entry skill, so you usually do **not** need any discovery round-trip.> **Router vs handovers.** This skill handles **cold start** — picking and installing a toolkit when none relevant is installed. Once inside a workflow, a toolkit's `workflow.md` **handover** sections take over: they carry context forward (pipeline name, dataset, destination) and route to a specific skill. Do **not** use this skill mid-workflow when the relevant toolkit is already installed. But when a handover names a toolkit that **isn't installed yet**, that's your cue — install it via the index below, then follow the handover's entry point + context.## Step 1: Route from the always-loaded index (fast path)The `# toolkits` index is already in your context. Match the user's intent to a row, then:1. **Install** it: `dlthub --non-interactive ai toolkit install <name>`2. **Confirm** (Step 3) and **hand over** to that toolkit's entry skill (Step 4).This needs **no MCP call** — the index is authoritative for the shipped toolkits and is the fast path. Use it whenever the intent matches a row.## Step 2: Live discovery (fallback only)Use this **only** when the index has no matching row (an unfamiliar need, or you suspect a newer toolkit exists):- **Prefer MCP** — `list_toolkits` from `dlt-workspace-mcp` for the live catalog, then `toolkit_info <name>` for skill details.- **CLI fallback** (MCP not connected): `dlthub --non-interactive ai toolkit list`, then `dlthub --non-interactive ai toolkit info <name>`.Match intent to the best toolkit, then install as in Step 1. Toolkits marked `(installed: <version>)` are already available.## Step 3: Verify install (only when needed)**Skip this step** when the install output already confirms success and the new toolkit's entry skill is available in this session — that is all the confirmation you need. (MCP health was already checked at session start via `dlthub ai status`.)Run `uv run dlthub ai status` only if:- the install output was ambiguous or reported an error,- the entry skill doesn't appear to be available, or- the `dlt-workspace-mcp` server hasn't been verified this session (no session-start status check and no successful MCP call yet).If status shows a **WARNING** about the MCP server (e.g. cannot be started), **fix it** using the error message before handing over.## Step 4: Handover (no restart needed)The `dlt-workspace-mcp` server is already running (installed with `init`) and toolkits reuse it — installing one adds **no new MCP server**, so continue in this session. Do **not** ask the user to restart; that would lose the conversation context.1. **Load the new toolkit inline** — prefer `toolkit_info <name>` (MCP), which is agent-agnostic and returns the entry skill + workflow rule. If MCP is unavailable, read the installed files directly; the install path depends on the agent (`.claude/`, `.cursor/`, or `.agents/`) — e.g. `<agent-dir>/skills/<entry-skill>/SKILL.md` and the toolkit's workflow rule.2. **Follow that workflow rule and start at the entry skill**, continuing the user's task with the context you already have. Do not start unrelated workflows on your own.3. The new skills become natively registered (`/`-invocable, always-loaded workflow rule) on the next natural session start — no need to restart now.> Exception: if a future toolkit ever ships its **own** MCP server (none do today), that server only starts on restart — suggest a restart **only** in that case, and use CLI fallbacks until then.

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