Workshop Tester Skill

Type: Process-oriented (no upstream repository)

Overview

The Workshop Tester skill teaches the AI assistant to act as an “AI-as-student” — reading a workshop module (AsciiDoc or markdown), executing each student step against a live environment, verifying expected outcomes, and producing a step-by-step pass/fail report with failure classification.

This skill fills the gap between environment readiness (student-readiness) and grading automation (/ftl:rhdp-lab-validator). While student-readiness checks if the environment is ready and the lab validator generates grading playbooks, neither answers: “Do the exercise steps in this module actually work?”

When the AI Uses This Skill

Your AI assistant will activate this skill when you’re:

  • Asking “run through module X against this environment”
  • Testing workshop exercises on a live deployment
  • Verifying that module steps work before handing a lab to students
  • Re-testing after fixes to confirm issues are resolved
  • Comparing test results across runs to track progress

Validation Lifecycle

The workshop-tester sits between readiness checks and grading automation:

student-readiness → workshop-tester → ftl:rhdp-lab-validator
(env ready?)        (steps work?)     (grade automation)

Failure Classification

When a step fails, the AI classifies it into one of three categories:

Category Meaning Action
Instruction Fix The module text is wrong but the env is fine Update the .adoc/.md file
Infra / Deployment Fix The environment or deployment pipeline is misconfigured (RBAC, operators, Helm values, ArgoCD) Fix AgnosticD config, workload variables, Helm values, or ArgoCD Application spec
Rethink The exercise design itself is flawed Redesign the module flow or add prerequisites

Step Parsing

The skill identifies executable steps in two content formats:

  • Showroom AsciiDoc: Extracts [source,role="execute"] blocks, skips [source,role="copypaste"] blocks, and uses “Expected output” sections for verification
  • Standard Markdown: Extracts fenced bash/shell code blocks, skips yaml/json/text blocks

Sample Report 

Module Test Report — module-02.adoc — GUID: abc123
──────────────────────────────────────────────────────────
 #  Step                    Status  Category              Notes
 1  oc login                PASS    —                     —
 2  oc new-project myapp    PASS    —                     —
 3  oc apply -f deploy.yml  FAIL    Instruction Fix       File path wrong: deploy.yml not in examples/
 4  curl app route          SKIP    —                     Skipped (depends on #3)
 5  oc get pods             FAIL    Infra/Deploy Fix      Operator CSV pending: openshift-gitops
 6  argocd app sync myapp   FAIL    Infra/Deploy Fix      ArgoCD app Degraded: Helm values missing
──────────────────────────────────────────────────────────
 Result: 2 PASS, 3 FAIL, 1 SKIP
 Breakdown: 1 Instruction Fix, 2 Infra/Deployment Fix, 0 Rethink
Skill Relationship
Student Readiness Pre-flight check — run before module testing to verify environment health
Showroom Content format — workshop-tester parses Showroom AsciiDoc for executable steps
AgnosticD v2 Infrastructure — Infra / Deployment Fix failures often require AgnosticD config changes
Field-Sourced Content Deployment pipeline — Helm/ArgoCD failures traced back to Field Content configs

Complementary Marketplace Tools

Tool Purpose When to Use
/showroom:verify-content Content quality (AsciiDoc, Red Hat standards) Before testing — ensure content is well-formed
/health:deployment-validator Infrastructure health (pods, routes, operators) When Infra / Deployment Fix failures are found
/ftl:rhdp-lab-validator Lab grading automation (Solve/Validate buttons) After testing — generate grading for passing modules

See ADR-012 for the full design rationale.

Install 

./install.sh install --skill workshop-tester