Create a Compute2 workflow

SkillDev tools

Create a Datagrok Compute2 workflow (pipeline configuration with steps, links, and actions)

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Create a Compute2 workflow skill

What this skill tells your AI

The instructions your AI receives, as published by datagrok-ai/public in .claude/skills/create-workflow/SKILL.md and read by ahel’s review.

The user wants a PipelineConfiguration — a Datagrok Compute2 workflow that wires multiple scripts together with reactive data links, validators, and metadata handlers.

The authoritative reference lives in the docs. This skill is a working procedure; when you need to know what something is or how a field behaves, read the docs.

Reading order

Read these before you write any configuration:

  • help/compute/workflows/overview.mdx — terms (node, link, controller, action, FuncCall, nqName, RichFunctionView).
  • help/compute/workflows/configuration.mdx — every field of PipelineConfiguration, the workflow types, states, custom exports, and the constraints / review checklist (consult the section before publishing).
  • help/compute/workflows/link-types.mdx — link/action types, controller methods, handler signatures.

Read on demand:

  • help/compute/workflows/links-spec.mdx — Link Query Language grammar. Needed only when the workflow uses tag selectors, template queries, or relative (base / @base) refs.
  • help/compute/workflows/examples.mdx — Wine Quality walkthrough end-to-end.
  • help/compute/workflows/code-usage.mdx — only if the workflow will be launched programmatically.

Reference examples (also linked from examples.mdx):

FileUse when
examples/minimal-static.tsFixed sequence of scripts, no links, user fills inputs manually.
examples/dynamic-with-links.tsUser can add/remove steps; outputs propagate to all downstream instances.
examples/validators-and-meta.tsCross-field validation, conditional input visibility, user-triggered actions.

Setup (install + dayjs/timezone imports) is covered in examples.mdx#dependencies.

Instructions

Phase 1: Understand the requirements

  1. Ask the user:

    • What scripts (Datagrok functions) should the workflow connect?
    • What data flows between them? (which outputs feed which inputs)
    • Is the set of steps fixed (static) or user-configurable (dynamic)?
    • Are there validation rules? (required fields, value ranges, cross-field checks)
    • Should any inputs be visually customized? (hidden, readonly, dropdowns)
  2. Verify that every referenced script exists. A script may be deployed, scaffolded locally but not yet published, or only an idea in the user's head. Check each location and stop searching once you find a match:

    • Deployed on the server: grok s functions list --filter "<nqName>". A non-empty result means the script is live and the nqName is correct.
    • Local package.ts: grep for //name:\s*<FunctionName> in src/package.ts (and any src/package-*.ts entries). Each annotated export becomes a function with nqName: <PackageName>:<FunctionName> once published.
    • Local scripts/ directory: grep for ^#name:\s*<scriptName> in scripts/**/*.{py,r,js,jl,m,sql}. Each #name-annotated file becomes a function with nqName: <PackageName>:<scriptName> once published.

    If a script is found locally but not on the server, note it as "scaffolded — will be published with this workflow". If a script is missing in all three places, ask the user whether to scaffold it (and follow the appropriate skill: see /init or the scripting docs) or to drop it from the workflow.

  3. Present a plain-language summary of the workflow for approval before coding. Mark each step as deployed, scaffolded, or to be created so the user can see the integration surface at a glance.

Phase 2: Design the configuration

  1. Choose the workflow type. See configuration.mdx for the discriminated union of static / dynamic / action / ref.
  2. Sketch the PipelineConfiguration object: steps with id and nqName; data links with from/to; validators and meta links if needed; actions for user-triggered operations.
  3. If any link uses tag selectors, template queries, or relative references, consult links-spec.mdx.
  4. Present the configuration skeleton for approval. Do not implement handlers yet.

Phase 3: Implement

  1. Create the provider function in the package:
    import type {PipelineConfiguration} from '@datagrok-libraries/compute-api';
    
    //name: MyWorkflow
    //description: Description of the workflow
    //tags: model
    //editor: Compute2:TreeWizardEditor
    //input: object params
    //output: object result
    export function myWorkflow(): PipelineConfiguration {
      return {
        id: 'my-workflow',
        nqName: 'MyPackage:MyWorkflow',
        version: '1.0',
        /* approved configuration */
      };
    }
    
  2. Implement link handlers using the controller methods documented in link-types.mdx.
  3. Register the function in package.ts if not already there.
  4. Run grok api to regenerate wrappers.

Phase 4: Review

Validate the configuration against the constraints and review checklist. Spawn a sub-agent for an independent pass if the configuration is non-trivial. Fix anything that fails before proceeding.

Phase 5: Build and verify

  1. grok check --soft — verify function signatures.
  2. webpack or npm run build — build the package.
  3. grok publish --release--release is mandatory; debug-mode packages are only visible to the publishing user.
  4. Tell the user where to open the workflow: Apps → Compute → ModelHub.

Behavior

  • Do not invent scripts. Only reference functions that exist on the server or in the package.
  • Present config for approval before writing handler code. Handlers are the expensive part.
  • Use simple LQL paths unless the user needs dynamic matching. Prefer in1:step1/a over complex selectors.
  • Keep handlers pure. Handlers should transform data, not perform side effects. Use actions for user-triggered operations.
  • One data link per script input. Each input of a downstream node should receive data from at most one data link. Use validators or meta links for additional concerns.
  • Import from @datagrok-libraries/compute-api. This is the public API. Do not import from @datagrok-libraries/compute-utils directly — those are internal paths.
  • Always publish with --release.
  • Use the /ui skill only in two cases: (1) an action needs to show custom inputs inline (e.g. a confirmation form with extra fields), or (2) a script's output viewer needs tweaks applied through its DG.Viewer JS API inside a viewersHook. Workflow scaffolding, links, validators, and meta-driven UI changes do not need /ui.

Signals

GitHub stars
72
Forks
32
Last commit
Sep 2026

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Advanced
Catalog kind
skill
Gateway key
create-workflow-datagrok-ai
Source
github.com/datagrok-ai/public