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Infrastructure · Version 1.1.0 · Reviewed 2026-08-02

GPU Workload Scheduling Advisor

Review and harden sharing strategy and quota design with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Schedules GPU workloads with sharing, quota, and queueing that keeps expensive hardware utilized.

₹99 one-time

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What this skill helps you do

  • Sharing strategy
  • Quota design
  • Queue fairness

How GPU Workload Scheduling Advisor works

You provide

Manifests, plans, and current runtime topology

It inspects

Reversibility and blast radius for sharing strategy

It decides

A quota design change staged by risk

You verify

Platform-native health check after each stage

What it checks first

GPU Workload Scheduling Advisor schedules GPU workloads with sharing, quota, and queueing that keeps expensive hardware utilized. Use it when the work involves Sharing strategy, Quota design, Queue fairness.

  1. Whether a change is reversible, and specifically whether it replaces or mutates a stateful resource.
  2. Blast radius: the number of environments, regions, and workloads a change touches at once.
  3. Identity and permission scope of the executing principal.
  4. Drift between declared and actual state.

Failure modes it recognizes

  • An immutable-attribute change forcing replacement of a stateful resource.
  • A change applied to all environments simultaneously with no canary.
  • Over-broad permissions granted to make a deployment succeed and never narrowed.
  • Manual changes creating drift that the next apply silently reverts.

Answers it will reject

  • Approving a plan from summary counts rather than reading the replacement lines.
  • Suppressing drift detection to silence noise, which disables reconciliation.
  • Granting administrative rights as a debugging shortcut.

Decision rules it applies

  • Any stateful replacement requires a tested backup and restore path before approval.
  • Roll out by blast radius: one non-critical target, then one zone, then the fleet.
  • Grant the narrowest permission that completes the task, with an expiry.

Evidence it asks for

  • Diff the plan in machine-readable form and classify every action.
  • Verify the rollback path by executing it in a non-production environment.
  • Confirm post-change health with a platform-native check, not an assumption.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to sharing strategy.
  2. Trace concrete failure or abuse paths for quota design; do not report checklist items without a mechanism.
  3. Prioritize queue fairness findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Sharing strategy assessment
  • Quota design decision and action plan
  • Queue fairness verification checklist

Evidence requirements

  • Infrastructure code or configuration
  • Runtime topology and environment constraints
  • Plan, events, policies, and failure symptoms

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Our GPU nodes sit at 30 percent utilization while researchers complain they cannot get capacity.

Expected output

Low utilization with unmet demand means allocation is exclusive and coarse: one job holds a whole GPU regardless of what it uses. Introduce time-sliced sharing or fractional allocation for interactive work, and a queue with fair-share so long jobs cannot starve short ones...

Boundaries and compatibility

Ideal for

  • Sharing strategy: produce a decision or artifact grounded in supplied evidence.
  • Quota design: produce a decision or artifact grounded in supplied evidence.
  • Queue fairness: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Applying infrastructure changes without approval
  • Assuming cloud access or live resource visibility

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.