shiny-course-based-learning

course.beta.shiny.everything.training.aws.dev

Preceptor — orchestrates an AWS course from an authored SPEC (Persona→Domain→Job Task→LO).

What we verified

Everything in this section is a check agenttru.st performed itself.

Assurance

bronze Bronze — agent card fetched over HTTPS with a valid certificate

Certificate

Issued by Amazon domain-validated

Valid until 14 Feb 2027.

Control of the hostname was checked; nothing about who operates it.

DANE / TLSA

Not verified (TLSA query returned RCodeNameError)

Discovery

Well-known document

Protocols

A2A verified by handshake or card fetch, not merely advertised

Hosted in

🇺🇸 US · Amazon.com, Inc. (AS14618)

First seen

2 Aug 2026

Last checked

2h ago

What the agent claims

Copied from the agent's own card. Not verified — the operator of course.beta.shiny.everything.training.aws.dev controls every value below.

Protocol

a2a

Version

1.0.0

Capabilities

streaming

Card completeness

a2a.proto v1.0 requires eight top-level fields. This card omits:

supportedInterfaces

Missing fields do not affect listing — they describe how much the operator has published, not whether the agent was verified.

Agent card

https://course.beta.shiny.everything.training.aws.dev/.well-known/agent.json

Advertised skills

• list_course
  Return the course structure: persona + every job task with its ordered Learning Objective (LO) ids and its proficiency assessment. Call this FIRST to know what to teach and in what order.
• get_lo
  Return a Learning Objective's statement and cognitive (Bloom's) level. Use the cognitive level to choose the instructional pattern arc for the LO.
• get_content
  Fetch authored teaching content for a Learning Objective. contentKind is one of: activation, prerequisites, boundaries, overview, deep-dive, scaffold, modality, scenario, exit-criteria. Use `activatio…
• get_proficiency_assessment
  Return the summative Proficiency Assessment spec for a job task: type, scenario setup, exit criteria, acceptable-vs-optimal, time constraints, and the diagnostic mapping (which LO to revisit on each f…
• propose_lab_plan
  Start a hands-on AWS lab plan for the learner. Provide a natural-language description of the lab to build (service, what they should learn, optional target minutes). Returns a proposed lab plan (title…
• refine_lab_plan
  Refine the current proposed lab plan with a follow-up instruction (e.g. "add a task on multipart-upload cleanup", "make it shorter"). Requires a plan already proposed via propose_lab_plan. Returns the…
• launch_lab
  Provision the actual AWS lab environment once the learner accepts the plan. Requires a plan proposed via propose_lab_plan. If a prior active lab conflicts, pass force=true to stop it and start fresh.…
• bootstrap_lab
  Kick off provisioning of the launched lab (fire-and-forget). Requires launch_lab first. After this, call poll_lab_status until the lab is READY. Takes ~2-5 minutes.
• poll_lab_status
  Check the launched lab provisioning status ONCE. Returns the current status; when READY, includes the consoleUrl to hand to the learner. Call repeatedly (every ~7s) after bootstrap_lab until status is…
• show_content_card
  Visual learning card. Call whenever explaining an AWS concept, service, or lesson step. Always call alongside your text response.
• show_message
  Send a structured text message to the learner. Optional — plain text responses are also accepted and stream more naturally. Use show_message when you want explicit control over message rendering.
• set_layout
  Switch the surface layout. "focused": Full-screen surface mode — no chat rail. Use when showing deep lesson content the learner should focus on. · "two-pane": Chat rail on the left, surface canvas on…

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