From ea105d6a3bc7f80319da51d8bc57d8cd862dddd3 Mon Sep 17 00:00:00 2001 From: Joaquin Coromina Date: Wed, 8 Jul 2026 11:14:36 +0200 Subject: [PATCH] chore(ai): bump AI workbench to e82e1029 and regenerate scaffolds Bumps WORKBENCH_REF f290a9b -> e82e1029 and regenerates the bundled Claude/Codex/Cursor scaffolds. Upstream changes: - adds a `performance` toolkit to the router index - deploy-run-sample-pipeline explains data loads into a hosted playground destination (not for production) - dlthub-router only runs `dlthub ai status` when the install output is unclear, instead of always Co-Authored-By: Claude Opus 4.8 --- CHANGELOG.md | 1 + src/create_dlthub_workspace/config.py | 2 +- .../claude/.claude/rules/init-dlthub-workspace.md | 3 ++- .../skills/deploy-run-sample-pipeline/SKILL.md | 4 ++-- .../claude/.claude/skills/dlthub-router/SKILL.md | 15 +++++++++------ .../_agents/claude/.dlt/.toolkits | 6 +++--- .../skills/deploy-run-sample-pipeline/SKILL.md | 4 ++-- .../codex/.agents/skills/dlthub-router/SKILL.md | 15 +++++++++------ .../.agents/skills/init-dlthub-workspace/SKILL.md | 3 ++- .../_agents/codex/.dlt/.toolkits | 6 +++--- .../minimal_workspace/_agents/codex/AGENTS.md | 3 ++- .../.cursor/rules/init-dlthub-workspace.mdc | 3 ++- .../skills/deploy-run-sample-pipeline/SKILL.md | 4 ++-- .../cursor/.cursor/skills/dlthub-router/SKILL.md | 15 +++++++++------ .../_agents/cursor/.dlt/.toolkits | 6 +++--- 15 files changed, 52 insertions(+), 38 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7696102..d304a88 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - The onboarding notebook is a single page; its "Next step" button navigates to the organization's setup page on dltHub. - The dataset viewer now hides the notebook header, and opens on whichever stack (local or hosted) you're connected to instead of always pointing to production. - Refreshed the bundled workspace `uv.lock` (`dlthub-client` 0.28.1, `marimo` 0.23.13; `pandas` 3.0.3 — 3.0.4 was yanked). +- Refreshed the bundled AI workbench: added a `performance` toolkit for speeding up slow pipelines, and the onboarding agent now explains that sample data loads into a hosted playground destination (not for production). ## [0.10.1] - 2026-07-01 diff --git a/src/create_dlthub_workspace/config.py b/src/create_dlthub_workspace/config.py index c6cfe24..89deac2 100644 --- a/src/create_dlthub_workspace/config.py +++ b/src/create_dlthub_workspace/config.py @@ -60,7 +60,7 @@ # To bump: pick a new SHA (the workbench repo has no tags today), update the # constant below, run `make generate-ai`, commit the resulting scaffold diff # alongside this change. -WORKBENCH_REF: str | None = "f290a9b3eab9db7ebb7b6e1e56d75856674488e7" +WORKBENCH_REF: str | None = "e82e1029ca56512631d4e7133f5d3b7186f54965" @dataclass(frozen=True) diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/rules/init-dlthub-workspace.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/rules/init-dlthub-workspace.md index 09ee7c8..e54bf64 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/rules/init-dlthub-workspace.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/rules/init-dlthub-workspace.md @@ -61,8 +61,9 @@ deploy / schedule pipelines on the dltHub platform → dlthub-platform guided end-to-end tour, ingest to dashboard (uses the real toolkits) → quick-start | dlthub --non-interactive ai toolkit install quick-start | quick-start test/try dlthub end-to-end — minimal pipeline + educational test deploy, NOT production → one-shot | dlthub --non-interactive ai toolkit install one-shot | deploy-run-sample-pipeline build and deploy a minimal custom REST API pipeline after uvx dlthub-init setup → dlthub-init-skills | dlthub --non-interactive ai toolkit install dlthub-init-skills | deploy-minimal-ingestion-pipeline +optimize / speed up a slow or memory-heavy pipeline — parallelism, workers, batching → performance | dlthub --non-interactive ai toolkit install performance | optimize-performance ``` * `one-shot` vs `rest-api-pipeline`: one-shot is for **testing / trying dlthub / onboarding / a quick demo** — a minimal single-endpoint, row-limited pipeline on local DuckDB plus an educational test deploy. Educational examples only, NOT production-grade. For a **real or production** REST pipeline (auth, incremental, multiple endpoints, production deploy), use `rest-api-pipeline`. `quick-start` is the guided tour that walks the real toolkits end-to-end. -* After installing, run `uv run dlthub ai status` to confirm, then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. +* After installing, confirm success from the install output (run `uv run dlthub ai status` only if the output is unclear or the MCP server hasn't been verified this session), then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. * The `dlthub-router` skill wraps this flow and is the fallback for needs not covered above (it uses live `list_toolkits` to discover newer toolkits). * DO NOT start data engineering work if no workflow toolkit is installed. diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/deploy-run-sample-pipeline/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/deploy-run-sample-pipeline/SKILL.md index 7779c40..6ca4903 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/deploy-run-sample-pipeline/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/deploy-run-sample-pipeline/SKILL.md @@ -44,7 +44,7 @@ Summarize which jobs were created or updated. **Run:** -Print to the user: `Running your pipeline and preparing your data in the background.` +Print to the user: `Running your pipeline and preparing your data in the background. Your data is loading into a dltHub-hosted playground destination — zero setup and no credentials needed, but it's limited to playground/onboarding use, not production.` Run both commands at the same time — start `serve_headless.py` in the background, then run the pipeline in the foreground: @@ -97,7 +97,7 @@ Print to the user: `- [x] Opening dltHub dataset browser` After Step 4 completes, immediately print to the user: -> "Onboarding complete! When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" +> "Onboarding complete! Your data lives in the dltHub-hosted playground destination — when you build your own pipeline, you'll choose a real destination of your own. When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" ## When the user says "Help me get started building and running a data pipeline on dltHub" diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/dlthub-router/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/dlthub-router/SKILL.md index 1512380..840dbff 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/dlthub-router/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.claude/skills/dlthub-router/SKILL.md @@ -27,13 +27,16 @@ Use this **only** when the index has no matching row (an unfamiliar need, or you Match intent to the best toolkit, then install as in Step 1. Toolkits marked `(installed: )` are already available. -## Step 3: Confirm & enable MCP +## Step 3: Verify install (only when needed) -``` -uv run dlthub ai status -``` -1. You should see the new toolkit and its entry skill. -2. If you see any **WARNING** about the MCP server (e.g. cannot be started), **fix it** using the error message. +**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) diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.dlt/.toolkits b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.dlt/.toolkits index e53712e..ddd6ea1 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.dlt/.toolkits +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/claude/.dlt/.toolkits @@ -11,7 +11,7 @@ init: agent: claude files: .claude/skills/dlthub-router/SKILL.md: - sha3_256: b0e91d5f6926a091cfb1cbfcfedf653f982d95ff1c14585b558f1b2e7ae3fc85 + sha3_256: af56355d874845d1e58f9e5dcefbc929b9cdd2ce92162cf16814a66016c58396 .claude/skills/improve-skills/SKILL.md: sha3_256: c52b222d82d101eaacbbf19d271d3f24b20d8e2bc70d49919d56144d448cd5c4 .claude/skills/setup-secrets/SKILL.md: @@ -21,7 +21,7 @@ init: .claudeignore: sha3_256: 1aa4dc9286dd219dcb55399f388c5ee1f940ab5e399a42b644d488cba57a9ec0 .claude/rules/init-dlthub-workspace.md: - sha3_256: 75f470e9587a3d29589d655a135cc882a340b362478e0b11b1d67fa5a76331cd + sha3_256: bd0d85b8bf6b0e0ce454982cfa1f63baddc67cbd1f9d05254a4b6185b48643c0 mcp_servers: - dlt-workspace-mcp name: init @@ -41,6 +41,6 @@ one-shot: agent: claude files: .claude/skills/deploy-run-sample-pipeline/SKILL.md: - sha3_256: 0d318282b0c0e848e43c99978f4e95be29154b6f19a06883186f9518cae5f1d3 + sha3_256: 91eb1893d0eff52a11af80459d54663428254a9ad17bda38940154d2600a056e .claude/rules/one-shot-workflow.md: sha3_256: 35dd0e85bc37f1702998d362bddc596c4a6b1d16d2350ec990d37c61af228f5a diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/deploy-run-sample-pipeline/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/deploy-run-sample-pipeline/SKILL.md index 7779c40..6ca4903 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/deploy-run-sample-pipeline/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/deploy-run-sample-pipeline/SKILL.md @@ -44,7 +44,7 @@ Summarize which jobs were created or updated. **Run:** -Print to the user: `Running your pipeline and preparing your data in the background.` +Print to the user: `Running your pipeline and preparing your data in the background. Your data is loading into a dltHub-hosted playground destination — zero setup and no credentials needed, but it's limited to playground/onboarding use, not production.` Run both commands at the same time — start `serve_headless.py` in the background, then run the pipeline in the foreground: @@ -97,7 +97,7 @@ Print to the user: `- [x] Opening dltHub dataset browser` After Step 4 completes, immediately print to the user: -> "Onboarding complete! When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" +> "Onboarding complete! Your data lives in the dltHub-hosted playground destination — when you build your own pipeline, you'll choose a real destination of your own. When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" ## When the user says "Help me get started building and running a data pipeline on dltHub" diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/dlthub-router/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/dlthub-router/SKILL.md index a988649..5f8f325 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/dlthub-router/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/dlthub-router/SKILL.md @@ -39,13 +39,16 @@ Use this **only** when the index has no matching row (an unfamiliar need, or you Match intent to the best toolkit, then install as in Step 1. Toolkits marked `(installed: )` are already available. -## Step 3: Confirm & enable MCP +## Step 3: Verify install (only when needed) -``` -uv run dlthub ai status -``` -1. You should see the new toolkit and its entry skill. -2. If you see any **WARNING** about the MCP server (e.g. cannot be started), **fix it** using the error message. +**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) diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/init-dlthub-workspace/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/init-dlthub-workspace/SKILL.md index 895f2a7..4d4e7a3 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/init-dlthub-workspace/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.agents/skills/init-dlthub-workspace/SKILL.md @@ -65,8 +65,9 @@ deploy / schedule pipelines on the dltHub platform → dlthub-platform guided end-to-end tour, ingest to dashboard (uses the real toolkits) → quick-start | dlthub --non-interactive ai toolkit install quick-start | quick-start test/try dlthub end-to-end — minimal pipeline + educational test deploy, NOT production → one-shot | dlthub --non-interactive ai toolkit install one-shot | deploy-run-sample-pipeline build and deploy a minimal custom REST API pipeline after uvx dlthub-init setup → dlthub-init-skills | dlthub --non-interactive ai toolkit install dlthub-init-skills | deploy-minimal-ingestion-pipeline +optimize / speed up a slow or memory-heavy pipeline — parallelism, workers, batching → performance | dlthub --non-interactive ai toolkit install performance | optimize-performance ``` * `one-shot` vs `rest-api-pipeline`: one-shot is for **testing / trying dlthub / onboarding / a quick demo** — a minimal single-endpoint, row-limited pipeline on local DuckDB plus an educational test deploy. Educational examples only, NOT production-grade. For a **real or production** REST pipeline (auth, incremental, multiple endpoints, production deploy), use `rest-api-pipeline`. `quick-start` is the guided tour that walks the real toolkits end-to-end. -* After installing, run `uv run dlthub ai status` to confirm, then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. +* After installing, confirm success from the install output (run `uv run dlthub ai status` only if the output is unclear or the MCP server hasn't been verified this session), then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. * The `dlthub-router` skill wraps this flow and is the fallback for needs not covered above (it uses live `list_toolkits` to discover newer toolkits). * DO NOT start data engineering work if no workflow toolkit is installed. diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.dlt/.toolkits b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.dlt/.toolkits index cff31e0..ac17393 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.dlt/.toolkits +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/.dlt/.toolkits @@ -11,7 +11,7 @@ init: agent: codex files: .agents/skills/dlthub-router/SKILL.md: - sha3_256: 5eab0df88b50966b41dfe1fca3e0e3f92c496627a59a0414beb6409bab0b2ac9 + sha3_256: 5d383df737cd33c30a5418af43f5b470d5bcc51aa25b9e346b8f4c3698a6e27f .agents/skills/improve-skills/SKILL.md: sha3_256: c52b222d82d101eaacbbf19d271d3f24b20d8e2bc70d49919d56144d448cd5c4 .agents/skills/setup-secrets/SKILL.md: @@ -21,7 +21,7 @@ init: .codexignore: sha3_256: 1aa4dc9286dd219dcb55399f388c5ee1f940ab5e399a42b644d488cba57a9ec0 .agents/skills/init-dlthub-workspace/SKILL.md: - sha3_256: 049543af4bf8621ef57dc708aa600a76e6181773f995b60d916a23ef0531c445 + sha3_256: 0641e3c3fc78e924050bf6dda703fc2072d1f52f73c500f66bdc964b0df96a3c mcp_servers: - dlt-workspace-mcp name: init @@ -41,6 +41,6 @@ one-shot: agent: codex files: .agents/skills/deploy-run-sample-pipeline/SKILL.md: - sha3_256: 0d318282b0c0e848e43c99978f4e95be29154b6f19a06883186f9518cae5f1d3 + sha3_256: 91eb1893d0eff52a11af80459d54663428254a9ad17bda38940154d2600a056e .agents/skills/one-shot-workflow/SKILL.md: sha3_256: 0f985a1242bd241020f1ff4b8e56ccc157004314f164e483186b238bd0860a9d diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/AGENTS.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/AGENTS.md index a45b915..ab981fa 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/AGENTS.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/codex/AGENTS.md @@ -8,7 +8,7 @@ they are essential for ANY work in this project CRITICAL: never ask for credentials in chat. Always let the user edit secrets directly and do not attempt to read them. ## toolkits — match intent → install → open the entry skill (no discovery round-trip needed) -Workflow toolkits are installed on demand. This index is authoritative for shipped toolkits: match the user's intent, run the install command, confirm with `dlthub ai status`, then hand over to the entry skill. No discovery call needed for these. +Workflow toolkits are installed on demand. This index is authoritative for shipped toolkits: match the user's intent, run the install command, confirm from its output (`dlthub ai status` only if unclear), then hand over to the entry skill. No discovery call needed for these. ``` @@ -23,6 +23,7 @@ deploy / schedule pipelines on the dltHub platform → dlthub-platform guided end-to-end tour, ingest to dashboard (uses the real toolkits) → quick-start | dlthub --non-interactive ai toolkit install quick-start | quick-start test/try dlthub end-to-end — minimal pipeline + educational test deploy, NOT production → one-shot | dlthub --non-interactive ai toolkit install one-shot | deploy-run-sample-pipeline build and deploy a minimal custom REST API pipeline after uvx dlthub-init setup → dlthub-init-skills | dlthub --non-interactive ai toolkit install dlthub-init-skills | deploy-minimal-ingestion-pipeline +optimize / speed up a slow or memory-heavy pipeline — parallelism, workers, batching → performance | dlthub --non-interactive ai toolkit install performance | optimize-performance ``` * `one-shot` vs `rest-api-pipeline`: one-shot is for **testing / trying dlthub / onboarding / a quick demo** — a minimal single-endpoint, row-limited pipeline on local DuckDB plus an educational test deploy. Educational examples only, NOT production-grade. For a **real or production** REST pipeline (auth, incremental, multiple endpoints, production deploy), use `rest-api-pipeline`. `quick-start` is the guided tour that walks the real toolkits end-to-end. * Use the `dlthub-router` skill for needs not covered above — it uses live `list_toolkits` to discover newer toolkits. diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/rules/init-dlthub-workspace.mdc b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/rules/init-dlthub-workspace.mdc index 582c4e9..69038de 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/rules/init-dlthub-workspace.mdc +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/rules/init-dlthub-workspace.mdc @@ -65,8 +65,9 @@ deploy / schedule pipelines on the dltHub platform → dlthub-platform guided end-to-end tour, ingest to dashboard (uses the real toolkits) → quick-start | dlthub --non-interactive ai toolkit install quick-start | quick-start test/try dlthub end-to-end — minimal pipeline + educational test deploy, NOT production → one-shot | dlthub --non-interactive ai toolkit install one-shot | deploy-run-sample-pipeline build and deploy a minimal custom REST API pipeline after uvx dlthub-init setup → dlthub-init-skills | dlthub --non-interactive ai toolkit install dlthub-init-skills | deploy-minimal-ingestion-pipeline +optimize / speed up a slow or memory-heavy pipeline — parallelism, workers, batching → performance | dlthub --non-interactive ai toolkit install performance | optimize-performance ``` * `one-shot` vs `rest-api-pipeline`: one-shot is for **testing / trying dlthub / onboarding / a quick demo** — a minimal single-endpoint, row-limited pipeline on local DuckDB plus an educational test deploy. Educational examples only, NOT production-grade. For a **real or production** REST pipeline (auth, incremental, multiple endpoints, production deploy), use `rest-api-pipeline`. `quick-start` is the guided tour that walks the real toolkits end-to-end. -* After installing, run `uv run dlthub ai status` to confirm, then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. +* After installing, confirm success from the install output (run `uv run dlthub ai status` only if the output is unclear or the MCP server hasn't been verified this session), then continue **in the same session** — load the new toolkit's entry skill + workflow rule via `toolkit_info` (or read the installed files) and proceed. No restart needed (toolkits reuse the already-running `dlt-workspace-mcp`); don't lose the user's context. * The `dlthub-router` skill wraps this flow and is the fallback for needs not covered above (it uses live `list_toolkits` to discover newer toolkits). * DO NOT start data engineering work if no workflow toolkit is installed. diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/deploy-run-sample-pipeline/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/deploy-run-sample-pipeline/SKILL.md index 7779c40..6ca4903 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/deploy-run-sample-pipeline/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/deploy-run-sample-pipeline/SKILL.md @@ -44,7 +44,7 @@ Summarize which jobs were created or updated. **Run:** -Print to the user: `Running your pipeline and preparing your data in the background.` +Print to the user: `Running your pipeline and preparing your data in the background. Your data is loading into a dltHub-hosted playground destination — zero setup and no credentials needed, but it's limited to playground/onboarding use, not production.` Run both commands at the same time — start `serve_headless.py` in the background, then run the pipeline in the foreground: @@ -97,7 +97,7 @@ Print to the user: `- [x] Opening dltHub dataset browser` After Step 4 completes, immediately print to the user: -> "Onboarding complete! When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" +> "Onboarding complete! Your data lives in the dltHub-hosted playground destination — when you build your own pipeline, you'll choose a real destination of your own. When you're ready to continue, ask me: `Help me get started building and running a data pipeline on dltHub`" ## When the user says "Help me get started building and running a data pipeline on dltHub" diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/dlthub-router/SKILL.md b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/dlthub-router/SKILL.md index 1512380..840dbff 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/dlthub-router/SKILL.md +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.cursor/skills/dlthub-router/SKILL.md @@ -27,13 +27,16 @@ Use this **only** when the index has no matching row (an unfamiliar need, or you Match intent to the best toolkit, then install as in Step 1. Toolkits marked `(installed: )` are already available. -## Step 3: Confirm & enable MCP +## Step 3: Verify install (only when needed) -``` -uv run dlthub ai status -``` -1. You should see the new toolkit and its entry skill. -2. If you see any **WARNING** about the MCP server (e.g. cannot be started), **fix it** using the error message. +**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) diff --git a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.dlt/.toolkits b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.dlt/.toolkits index b0c698a..e851883 100644 --- a/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.dlt/.toolkits +++ b/src/create_dlthub_workspace/scaffolds/minimal_workspace/_agents/cursor/.dlt/.toolkits @@ -11,7 +11,7 @@ init: agent: cursor files: .cursor/skills/dlthub-router/SKILL.md: - sha3_256: b0e91d5f6926a091cfb1cbfcfedf653f982d95ff1c14585b558f1b2e7ae3fc85 + sha3_256: af56355d874845d1e58f9e5dcefbc929b9cdd2ce92162cf16814a66016c58396 .cursor/skills/improve-skills/SKILL.md: sha3_256: c52b222d82d101eaacbbf19d271d3f24b20d8e2bc70d49919d56144d448cd5c4 .cursor/skills/setup-secrets/SKILL.md: @@ -21,7 +21,7 @@ init: .cursorignore: sha3_256: 1aa4dc9286dd219dcb55399f388c5ee1f940ab5e399a42b644d488cba57a9ec0 .cursor/rules/init-dlthub-workspace.mdc: - sha3_256: 708f634425d226bc9a81ce7f770b714d873e7537a7b7fe3f2a7ec7ccd122514b + sha3_256: 31aae335247d5cee437892abe8a9610dbddb6c644113c85ac054519f887f2f91 mcp_servers: - dlt-workspace-mcp name: init @@ -41,6 +41,6 @@ one-shot: agent: cursor files: .cursor/skills/deploy-run-sample-pipeline/SKILL.md: - sha3_256: 0d318282b0c0e848e43c99978f4e95be29154b6f19a06883186f9518cae5f1d3 + sha3_256: 91eb1893d0eff52a11af80459d54663428254a9ad17bda38940154d2600a056e .cursor/rules/one-shot-workflow.mdc: sha3_256: e6d8e9b8a41bae64ae7c2153fe0bc3b90af3861014303f31c424c37b45ee08c0