Workflows

A workflow is a typed graph of data production: ordered stages whose member nodes do the work — deterministic compute (UDFs), judgment-making agents, statistical samplers, and control flow — joined by typed data edges. You push it from the terminal and it renders as a live canvas.

How you use it

One round, from a sentence to a published dataset. Skills live in dreamlake-skills.

1. Install the CLI and the three skills.

   Claude Code is where you talk to it — install that first.

   $ curl -fsSL https://dl.dreamlake.ai/install.sh | sh

   $ dreamlake login --env prod

   $ git clone https://github.com/dreamlake-ai/dreamlake-skills.git

   $ mkdir -p ~/.claude/skills

   $ cp -r dreamlake-skills/{video-labeling-workflow,\

   remote-source-check,workflow-publish} ~/.claude/skills/

   all three — they call each other by name

   restart Claude Code to load them

   Unwrap

   Skill	Does
   video-labeling-workflow	fills the template, drives the whole round
   remote-source-check	proves your source really holds those paths
   workflow-publish	validates and pushes, and asks which namespace

   Install all three — the first invokes the other two by name.

   --env prod is worth typing. Without it login targets whichever environment is active on that machine, so anyone who has ever logged into staging lands there silently — and the failures that follow (an empty source list, a push to the wrong place) never point back at the cause. dreamlake auth env list shows which login environment is active.

   Install the standalone CLI from the CLI guide and check dreamlake workflow --help. The Python dreamlake package provides the SDK; it is not the supported CLI installation path.

2. Say what you want — it asks for the data, verifies it, shows you what it found.

   claude code · video-labeling-workflowcollect → check → confirm

   Create a video labeling workflow

   Four things:

   1 · source name — a connected S3 / Dropbox / HF store

   2 · video path inside it

   3 · reference annotation path (.json, scored against)

   4 · task description — one line, e.g. mount a wall shelf

   footage · clips/713488-assembly-shelf.mp4 · gold/713488_annotation.json · mount a wall shelf

   Checked. Confirm before I create it:

   source footage (namespace acme)

   video clips/713488-assembly-shelf.mp4 · 287 MB

   reference gold/713488_annotation.json · 64 phases

   task mount a wall shelf

   publish to acme

   Confirmed.

   the check is not a formality — a path that lists but will not fetch, or a swapped video/annotation pair, costs one line here and a failed run later

3. It pushes — validated against the schema and the graph rules first.

   $ dreamlake workflow push video-labeling-0807-1501.workflow.json \

   --namespace acme

   workflow: video-labeling-0807-1501 v1 · 5 stages · 9 nodes · 10 edges

   ✓ pushed video-labeling-0807-1501 v1

   open: dreamlake.ai/acme/workflows/video-labeling-0807-1501

4. Review on DreamLake — iterate, hot-reload.

   dreamlake.ai/acme/workflows/video-labeling-0807-1501spec v2 ▾

   The canvas renders the spec; click nodes to audit config, permissions, and wiring. Ask for a change — Claude pushes v2 and the open page hot-reloads in place within ~2.5s. No refresh.v1 → v2 → v3 · picker pins any

5. Run it — nodes light up, output streams.

   queued

   Run

   task on the queue

   →

   running

   worker claims it

   nodes light up, logs stream

   →

   awaiting

   gate

   waits for a person — no cost

   →

   completed

   dataset

   published, versioned

   a run's four states — the gate is the only one that waits on a human

6. Review at the gate, then approve.

   The run publishes its dataset and stops. Select the gate node: ↗ Review dataset opens what was produced, ✓ Approve & continue completes the run. The link stays after approval — it is the way back to what was signed off.

What a spec is made of

The schema also accepts condition, switch, loop and foreach control types. They pass validation, then stop a run with not supported yet — vocabulary to design against, not behaviour to rely on.

A uda node declares the tools and permissions its agent may use, so judgment never runs on raw credentials. Intermediate results stay in the worker's run directory; what reaches DreamLake is what a publish node writes.

Version it

Pushes never overwrite. Each one appends an immutable version, and the spec picker on the workflow page pins the canvas to any of them — pick the latest to resume following pushes.

Editing a node's settings on the canvas and pressing Apply does the same thing: the edited spec is validated and appended as the next version. Nothing is edited in place, so a run always names the version it executed.

Next steps