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supreme-ai-engineering

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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,

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

davccavalcante

davccavalcante/supreme-coding-guidelines-skill.ah

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تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·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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davccavalcante/supreme-coding-guidelines-skill.ah

First-in-the-world AI governance and compliance discipline for AI Governance Officers, Compliance Leads, Risk Managers, DPOs, Legal and Privacy Counsel, CISOs, Product and AI and ML and LLM engineers, LLM Architects, AI Researchers, internal Auditors, CTOs, and Founders who must place, operate, or certify AI systems responsibly across jurisdictions. Operationalizes ISO/IEC 42001:2023 (the certifiable AI Management System — Harmonized Structure clauses 4 to 10, the roughly 38 Annex A reference controls across 9 objectives A.2 to A.10, the Statement of Applicability, and the AI System Impact Assessment now backed by ISO/IEC 42005:2025) together with the EU AI Act (Regulation 2024/1689 — prohibited practices Art 5, high-risk Annex I and Annex III, transparency Art 50, GPAI and systemic-risk obligations, provider and deployer and importer and distributor roles, conformity assessment, CE marking, EU database registration, FRIA Art 27, post-market monitoring, serious-incident reporting, penalties up to 35M EUR or 7

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supreme-benchmarking

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Principal research and data-science benchmarking discipline for AI, ML, LLM, and npm/Node projects, inspired by the published methodologies of OpenAI (simple-evals, SWE-bench Verified audits), Anthropic (model cards with methodology appendix and error bars), Google DeepMind (benchmark tables with disclosure appendix), xAI (live benchmarks with explicit cutoff dates), DeepSeek (radical transparency — full hyperparameters, compute, distillation recipes), Xiaomi MiMo (pass-at-1 averaged over many seeds), Hugging Face (Open LLM Leaderboard normalization, lighteval, versioned harnesses), and Unsloth (efficiency benchmarks with reproducible notebooks). Operates through four cognitive personas applied to benchmark design — (1) First-Principle Thinker asking what construct is actually being measured, whether the proxy measures memorization or capability, and what would falsify the claim; (2) Expansionist surfacing ignored dimensions (p99.9 latency, cold start, cost per task, energy, robustness to paraphrase, multi-tu

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supreme-council

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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

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