agentleFS
MicrosoftKnown publishermicrosoft/sre-agent/labs/onboardinglab/.github/skills/onboarding-lab/SKILL.md

onboarding-lab

Skill187 starsChanged 6 months agoMITView on GitHub

Set up, resume, inspect or operate the Azure SRE Agent onboarding lab in this checkout. Use when a learner asks for help preparing the lab, understanding a setup failure, or saving their reviewed learner artifact.

The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. About 0 are common, 11 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈11

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read the lab's README.md from labs/onboardinglab.

    line 8
  2. Distinctive:

    If access, quota or prerequisites are missing, name the failed check and its remedy.

    line 20
  3. Distinctive:

    Never invent an assigned environment or grant yourself access.

    line 22
  4. Distinctive:

    Use the documented setup scripts and azd lifecycle.

    line 26
  5. Distinctive:

    Run prerequisite checks before installing anything.

    line 30
  6. Very specific:

    Use -CoreOnly only when explicitly choosing the reduced fallback.

    line 38
  7. Distinctive:

    After a failure, inspect the named deployment and the stage's read-back output.

    line 40
  8. Distinctive:

    Verify checkout and workload telemetry before allowing a fault scenario.

    line 45
  9. Distinctive:

    Check agent configuration separately from workload provisioning.

    line 46
  10. Distinctive:

    Do not claim the deployed agent saved a skill when the local assistant performed the operation.

    line 59
  11. Distinctive:

    Only the facilitator injects or resets faults on shared workloads.

    line 65
  12. Distinctive:

    Do not enable scheduled activity as part of ordinary setup.

    line 69
  13. Distinctive:

    If a run is waiting for approval, report it as waiting, not complete.

    line 72

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Help with the onboarding lab

Read the lab's README.md from labs/onboardinglab. Work from that directory so relative paths and its local instructions resolve correctly.

Find the learner's starting point

  1. For an assigned agent, ask for its portal link or exact resource ID. Inspect only that environment. Do not deploy or overwrite it.
  2. For an existing local setup, read azd env list and the selected environment's values from ticketingapp-source. Check the subscription and resource group before executing any stage.
  3. For a new environment, explain which resources are billable, confirm the target and responsible owner, and run the documented preflight before deployment.
  4. If access, quota or prerequisites are missing, name the failed check and its remedy. Offer the facilitator's assigned environment when one is available. Never invent an assigned environment or grant yourself access.

Prepare and resume

Use the documented setup scripts and azd lifecycle. Inspect their help and parameters before calling them. Do not turn README examples into a separate collection of unverified REST writes.

Run prerequisite checks before installing anything. Obtain approval for missing tools, Azure resources and role assignments. Reuse existing sign-ins; let the learner complete an expired sign-in through the provider's normal UI.

The standard lab includes GitHub and Outlook. Obtain the approved repository and email recipients during preparation. Let the learner complete OAuth in the connection UI, then run setup's Connect stage. Do not ask for credentials in chat or treat consent as permission to create an issue or send email. Use -CoreOnly only when explicitly choosing the reduced fallback.

After a failure, inspect the named deployment and the stage's read-back output. Reconcile an unknown write result before retrying. Preserve the existing app, agent, skills, knowledge and existing connections. Do not use a force flag to silence unexpected drift.

Verify checkout and workload telemetry before allowing a fault scenario. Check agent configuration separately from workload provisioning.

Help the learner teach the agent

Have the deployed agent draft a small, workload-specific skill. A useful example requires fresh successful checkout and PostgreSQL dependency evidence before declaring recovery. Keep it tool-free and give it a learner-specific name.

Show the proposed content and exact agent target before saving. Prefer the deployed agent's supported, approved self-configuration path. If that path fails authorization, stop it; use the documented local learning helper or Skill Builder only after explaining that an authorized operator will perform the write.

Do not claim the deployed agent saved a skill when the local assistant performed the operation. Read the saved content back, then have the learner invoke it in a new conversation without pasting the content again.

Facilitate safely

Only the facilitator injects or resets faults on shared workloads. Inspect a fault command's target before running it. Run the documented reset helper instead of inventing a different recovery action.

Do not enable scheduled activity as part of ordinary setup. For the scheduling exercise, show the target, schedule and read-only handler before enabling it. Observe a real scheduled execution, disable it afterward and verify the state. If a run is waiting for approval, report it as waiting, not complete.

At handoff, list the app and agent URLs, remaining blockers, learner artifacts, enabled schedules, and the resource owner. Teardown requires explicit approval.

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