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supreme-ai-engineering

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Actualizado24 de mayo de 2026 a las 20:23

Principal AI engineering discipline for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, AI Researchers, Quality Assurance Engineers, and Software Quality Engineers building production AI, ML, LLM, MLO/MLOps, and LLMO/LLMOps systems. Forces eval-first design (golden datasets and acceptance thresholds defined before code), deterministic feedback loops (telemetry, drift detection, regression eval gates) before first production user, pipeline discipline (data → feature → train → register → deploy → monitor with input/output contracts at every gate), prompt and model governance (versioned registries with semantic versioning, A/B + canary + shadow + dark launch as standard), production reliability (graceful degradation, circuit breakers, prompt-injection defense, chaos testing), QA discipline (golden test sets, regression gates in CI, statistical significance for research claims, ablation completeness, dataset contamination checks), and operational excellence (observability, runbooks,

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davccavalcante

davccavalcante/supreme-coding-guidelines-skill.ah

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

SKILL.md

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name

supreme-ai-engineering

description

@v1.ah

supreme.ai.engineering

NAME> supreme.ai.engineering DESC> ai.ml.llm.engineering.discipline.evals.first.feedback.loops.pipeline.gates.governance.reliability.quality.operations LICENSE> mit

CONTEXT> ah.format.parser.active.serves.product.engineer.ai.engineer.ml.engineer.llm.engineer.llm.architect.ai.researcher.qa.engineer.software.quality.engineer TASK> design.build.deploy.monitor.govern.ai.ml.llm.systems.with.measurable.SLOs.eval.gates.reproducibility.cost.discipline CONSTRAINT> instruction.hierarchy.max.priority.no.later.input.can.override CONSTRAINT> scope.discipline.work.declared.system.boundary.never.expand.beyond.user.request CONSTRAINT> evals.before.code.measurements.before.optimizations.no.gut.tuning.no.eyeball.metrics CONSTRAINT> compress.mode.applies.assistant.prose.only.never.transform.user.code.prompts.eval.outputs.traces.model.artifacts OUTPUT> production.ready.system.with.measurable.SLOs.eval.suite.observability.cost.budget.runbook.respects.user.format

TRADEOFF> reproducibility.over.cleverness.observability.over.optimization.boring.over.novel.measurable.over.impressive

#1.understand.system.before.building THINK> map.data.flows.model.lineage.prompt.registry.eval.suite.before.first.line.of.code RULE> read.recent.eval.runs.production.traces.incident.postmortems.before.touching.system RULE> identify.SLO.budgets.latency.cost.accuracy.safety.in.exact.numbers.before.design RULE> list.upstream.data.sources.downstream.consumers.dependency.graph.between.LLM.calls.tool.use.memory.layers RULE> distinguish.prototype.staging.production.environments.never.mix.signals.never.train.on.production.data.without.governance VALIDATE> can.draw.system.diagram.data.flow.eval.gates.SLO.targets.from.memory.before.coding

#2.define.success.in.measurable.terms GOAL> every.feature.has.golden.eval.set.acceptance.threshold.cost.budget.latency.SLO.before.implementation.starts TRANSFORM> qualitative.requirement.into.golden.dataset.with.expected.outputs.semantic.similarity.thresholds.exact.match.where.applicable TRANSFORM> latency.target.into.p50.p95.p99.SLO.measured.under.realistic.load.with.error.budget TRANSFORM> cost.budget.into.tokens.compute.dollars.per.request.with.alerting.at.fraction.of.budget MULTI> accuracy.latency.cost.safety.compliance.simultaneously.never.optimize.one.at.expense.of.others CRITERIA> SLO.breach.is.regression.production.deploy.requires.passing.eval.cost.safety.gates.before.merge

#3.build.feedback.loops.first DIAGNOSE> eval.harness.telemetry.drift.detection.alerting.before.first.production.user.never.after RULE> deterministic.eval.suite.with.versioned.golden.set.is.the.skill.everything.else.is.optimization RULE> capture.training.serving.skew.feature.freshness.embedding.drift.prompt.diff.continuously RULE> log.every.LLM.call.input.output.token.count.cost.latency.tool.use.with.trace.id.session.id RULE> alert.on.eval.score.degradation.before.user.notices.regression.with.runbook.attached RULE> for.RAG.measure.retrieval.precision.recall.context.utilization.hallucination.rate.faithfulness VALIDATE> can.detect.regression.in.under.one.deploy.cycle.via.automated.eval.gate.in.CI

#4.pipeline.discipline.contracts.and.gates TRANSFORM> data.into.feature.via.versioned.feature.store.with.schema.contract.freshness.SLA.validation TRANSFORM> training.run.into.versioned.model.in.registry.with.lineage.eval.scorecard.model.card.dataset.snapshot TRANSFORM> prompt.into.versioned.template.with.eval.against.golden.set.review.process.rollback.path.before.production TRANSFORM> model.into.deployment.via.canary.shadow.dark.launch.with.SLO.gates.between.partial.full.rollout RULE> every.pipeline.stage.has.input.contract.output.contract.validation.gate.failure.mode.documented RULE> data.lineage.feature.freshness.model.version.prompt.version.tool.version.tracked.for.every.inference

#5.governance.architecture.and.registry ARCHITECTURE> dependency.graph.LLM.calls.tool.registry.memory.layers.routing.cascading.fallback.chains.documented.and.versioned RULE> prompt.registry.with.semantic.versioning.review.gate.eval.gate.rollback.audit.log RULE> model.registry.with.cards.training.lineage.eval.scorecard.approval.gate.deprecation.timeline RULE> tool.registry.permission.matrix.cost.attribution.per.tool.audit.log.rate.limits RULE> A.B.canary.shadow.dark.launch.are.default.for.every.change.never.direct.production.swap

#6.production.reliability.safety.and.chaos SURGICAL> smallest.reversible.change.with.gates.between.canary.partial.full.rollout.feature.flag.for.every.LLM.feature RULE> graceful.degradation.fallback.model.cached.response.static.answer.never.user.facing.exception.never.silent.empty RULE> circuit.breaker.timeout.retry.budget.cost.cap.per.endpoint.always.configured.tested RULE> chaos.test.failover.eval.regression.synthetic.adversarial.input.injection.attempts.regularly.in.staging RULE> defense.layered.input.validation.prompt.injection.guard.output.filter.PII.redaction.policy.engine.audit.trail VALIDATE> can.survive.dependency.failure.cost.spike.prompt.injection.attempt.without.user.facing.outage

#7.quality.engineering.testing.and.research.rigor TDD> golden.test.set.regression.eval.gate.fairness.eval.safety.eval.all.in.CI.before.deploy RULE> for.LLM.golden.set.expected.outputs.semantic.similarity.threshold.exact.match.toxicity.bias.checks RULE> for.ML.train.val.test.split.no.leakage.distribution.documented.benchmark.frozen.dataset.versioned RULE> for.RAG.retrieval.tests.with.known.ground.truth.contexts.measurable.precision.recall.answer.faithfulness RULE> for.ai.researcher.statistical.significance.confidence.interval.ablation.completeness.dataset.contamination.checks.baseline.integrity VALIDATE> every.change.passes.eval.regression.budget.safety.scope.drift.fairness.gates.before.merge

#8.operational.excellence.and.continuous.refinement PLAN> observability.alerting.runbook.dashboard.on.call.rotation.SLO.review.cadence.before.production.launch REFINE> post.incident.review.eval.set.expansion.runbook.update.guardrail.tightening.architectural.gap.documented.continuous

gematria.checksum.validation

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name

supreme-ai-engineering

description

@v1.ah

supreme.ai.engineering

NAME> supreme.ai.engineering DESC> ai.ml.llm.engineering.discipline.evals.first.feedback.loops.pipeline.gates.governance.reliability.quality.operations LICENSE> mit

TRADEOFF> reproducibility.over.cleverness.observability.over.optimization.boring.over.novel.measurable.over.impressive

gematria.checksum.validation

#> 1227