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ufukyuksel-devufukyuksel-dev/SUPER_COMPUTER_AI/skills/ai-engineering/production-ops/SKILL.md

production-ops

Skill0 starsChanged 47 days agoMITView on GitHub

Use when operating deployed AI systems — monitoring, drift, observability, incident response, cost controls, security posture, provider-fallback resilience, rollout/rollback. Do NOT use for scheduled/autonomous job design (see skill ai-automation).

The file

We split this skill into the rules it gives an agent

12 rules. About 0 are common, 11 are distinctive, and 1 is 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.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Control change: Use versioned artifacts, staged rollout, canaries, automated stop conditions, rollback criteria, and independently verified …

    line 23
  2. Distinctive:

    Exercise readiness: Run health, load, dependency-failure, fallback, security, drift, incident, and rollback probes against the target …

    line 25
  3. Distinctive:

    Treat provider fallback as a behavior change.

    line 43
  4. Distinctive:

    Do not declare readiness from configuration review alone.

    line 50
  5. Distinctive:

    Do not use averages alone for tail latency, failure bursts, cost spikes, or quality regressions.

    line 51
  6. Distinctive:

    Do not retry every failure or route blindly to a fallback provider.

    line 52
  7. Distinctive:

    Do not page on signals without an owner and executable response.

    line 53
  8. Distinctive:

    Do not perform an emergency change without scope, audit, verification, and rollback criteria.

    line 54
  9. Distinctive:

    Do not label missing telemetry, drills, or rollback evidence as a pass.

    line 55
  10. Very specific:

    If telemetry or cost data is unavailable, produce the instrumentation contract and mark affected SLO, drift, and budget controls UNVERIFIED.

    line 62
  11. Distinctive:

    If a safe drill environment or deployment access is unavailable, review procedures and provide exact probes, but do not claim fallback or …

    line 63
  12. Distinctive:

    If security authority or incident ownership is unresolved, block production-readiness approval and ask for the named owner or decision.

    line 64

≈ “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

Production Operations

Operating Boundary

  • Own production readiness, service health, quality and drift signals, incidents, cost controls, security posture, operational resilience, and rollout/rollback.
  • For serving-engine and topology selection, see skill model-serving.
  • For evaluation design, see skill eval-harness.
  • For scheduled or autonomous workflows, see skill ai-automation.
  • For current provider, platform, pricing, and security claims, see skill ai-research.

Decision Workflow

  1. Define the service contract. Record owners, users, critical journeys, data classes, dependencies, SLOs, error budget, recovery objectives, and unacceptable outcomes.
  2. Map production risks. Rank availability, latency, output-quality, drift, safety, privacy, security, dependency, quota, and cost risks by impact and detectability.
  3. Design signals and actions together. For each risk, define a measurable signal, threshold, window, owner, runbook, and escalation. Use ops-readiness-checklist.md.
  4. Bound resilience. Define timeouts, retries, circuit breaking, load shedding, provider fallback, state reconciliation, and degraded modes. Gate every fallback with compatibility and quality evidence.
  5. Prepare incidents. Establish severity, command ownership, containment, communication, evidence preservation, recovery, and post-incident review.
  6. Control change. Use versioned artifacts, staged rollout, canaries, automated stop conditions, rollback criteria, and independently verified rollback steps.
  7. Control cost and capacity. Set budgets, quotas, unit-cost signals, saturation forecasts, anomaly alerts, and an explicit response to budget exhaustion.
  8. Exercise readiness. Run health, load, dependency-failure, fallback, security, drift, incident, and rollback probes against the target environment before declaring readiness.

Required Deliverable

Return:

  • the service and ownership contract;
  • an SLO/error-budget table tied to user journeys;
  • a risk-to-signal-to-runbook map;
  • resilience and provider-fallback gates;
  • incident, rollout, rollback, and cost-control procedures;
  • drill output and observed telemetry;
  • a readiness verdict with every unexercised item labeled UNVERIFIED.

Decision Rules

  • Derive alerts from user impact and actionable runbooks, not from available dashboard widgets.
  • Separate system health, model/output quality, safety, drift, and business-impact signals.
  • Treat provider fallback as a behavior change; validate schema, semantics, safety, latency, capacity, and cost before enabling it.
  • Fail closed for authorization, privacy, or data-residency uncertainty; degrade explicitly for availability or quality pressure.
  • Require a rollback path before rollout and verify it using the same artifact and environment boundaries as production.
  • Preserve audit and incident evidence while redacting credentials and unnecessary sensitive content.

Failure Modes

  • Do not declare readiness from configuration review alone.
  • Do not use averages alone for tail latency, failure bursts, cost spikes, or quality regressions.
  • Do not retry every failure or route blindly to a fallback provider.
  • Do not page on signals without an owner and executable response.
  • Do not perform an emergency change without scope, audit, verification, and rollback criteria.
  • Do not label missing telemetry, drills, or rollback evidence as a pass.

Tool Requirements & Degradation

  • Require production-like telemetry, deployment state, dependency health, cost/usage data, security findings, and runbook access for readiness claims.
  • Require a controlled environment and authorized deployment controls for fallback, incident, and rollback drills.
  • For current provider limits, terms, pricing, vulnerabilities, or platform behavior, see skill ai-research.
  • If telemetry or cost data is unavailable, produce the instrumentation contract and mark affected SLO, drift, and budget controls UNVERIFIED; do not infer health from absence of alerts.
  • If a safe drill environment or deployment access is unavailable, review procedures and provide exact probes, but do not claim fallback or rollback readiness.
  • If security authority or incident ownership is unresolved, block production-readiness approval and ask for the named owner or decision.

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