Uipath Training Lab Scaffolder

UiPath Training Lab Scaffolder

Create a navigable, non-empty lab skeleton that makes learner tasks, reference implementations, fixtures, prerequisites, and verification conventions predictable across a course.

Maturity: pilot.

Ownership Boundary

This custom skill owns: Create a navigable, non-empty lab skeleton that makes learner tasks, reference implementations, fixtures, prerequisites, and verification conventions predictable across a course.

Keep current product commands, schemas, artifact validation, live tenant operations, and policy administration with official UiPath skills.

Workflow

1. Parse the curriculum

Extract ordered sections, exercises, variants, objectives, prerequisites, expected artifacts, and which exercises need UiPath projects or only conceptual material.

Completion criterion: Every planned exercise has a unique position and purpose.

2. Create the structure

Use the bundled scaffolder with a reviewed JSON plan to create numbered section and exercise directories and requested problem, solution, explainer, fixture, and reference variants.

Completion criterion: The directory tree matches the approved plan.

3. Write meaningful stubs

Populate each README with the exercise title, objective, learner task or explanation purpose, expected evidence, prerequisites, and completion signal. Do not use empty placeholders.

Completion criterion: Every primary variant is understandable without instructor narration.

4. Add UiPath-specific pointers

For project exercises, state the artifact type, official skill owner, sample-data policy, setup prerequisites, and validation expectations without fabricating product files.

Completion criterion: Learners know how the lab connects to current UiPath tooling.

5. Validate the scaffold

Check naming, ordering, required variants, non-empty files, duplicate identifiers, broken internal links, and plan-to-tree completeness.

Completion criterion: The scaffold passes deterministic structural validation.

6. Hand off authoring

List the exercises that still need real project assets, solutions, diagrams, recordings, or test data and assign the next authoring route.

Completion criterion: The skeleton is ready for content development rather than mistaken for a finished course.

Output Contract

• Numbered lab directory tree.
• Meaningful exercise README stubs.
• Structural validation report.
• Remaining content-authoring backlog.

Guardrails

• Do not create empty README files or meaningless placeholder-only stubs.
• Do not invent working UiPath project assets when the request is only scaffolding.
• Do not expose solution content inside problem variants.
• Do not renumber existing exercises without preserving references and history.

Example Requests

• "Scaffold a ten-module UiPath Agent Builder course."
• "Create problem and solution folders for RPA exercises."
• "Generate a workshop lab skeleton from this lesson plan."

Finish

Report completed work, observed evidence, the next official owner, and every blocker. Mark unobserved actions as pending.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: 1aifanatic
• Source: 1aifanatic/uipath-boost
• License: MIT

Install and usage instructions live in the source repository linked above.