311 skills · sorted by relevance
Design, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.
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Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP.
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Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.
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Full trip planning orchestration. Combines flight search, hotel search, and points optimization into one cost analysis. Use when the user says "plan a trip" or wants the complete picture for a destination.
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Rules for writing concise, token-efficient workflow and skill files. Prevents over-building that requires costly optimization passes. Use when creating or editing workflow files, SKILL.md files, or new skill definitions.
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Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
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Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.
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Collection of AWS development skills covering CDK infrastructure as code, cost optimization, and serverless/event-driven architectures with integrated MCP servers.
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Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
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This skill should be used when the user asks to "review architecture", "Well-Architected review", "check bestpractices", "security assessment",or "cost optimization analysis".
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Finds orphaned, idle, and underutilized cloud resources across AWS, GCP, or Azure accounts. Use when someone needs to audit cloud spending, find unused EBS volumes, stale snapshots, unattached IPs, idle load balancers, or oversized RDS instances. Trigger words: cloud waste, orphaned resources, unused volumes, cloud audit, infrastructure cleanup, cloud bill analysis.
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Launch and manage Amazon EC2 instances for scalable compute. Configure AMIs, security groups, key pairs, and EBS volumes. Set up auto-scaling groups for high availability and cost optimization across availability zones.
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Store and manage unstructured data with Azure Blob Storage. Create containers, upload and organize blobs, configure access tiers (Hot, Cool, Archive) for cost optimization, generate SAS tokens for secure temporary access, and set lifecycle management policies.
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The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.
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Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, file_patterns, code_patterns, " mentioned.
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DevOps 与站点可靠性工程 (SRE) — 平台 / 基础设施 / 可靠性工程师的认知操作系统, 覆盖软件交付 + 运维全生命周期 (CI/CD 与发布工程 trunk-based + 渐进式发布 canary/blue-green/feature flag + GitOps Argo CD/Flux / 基础设施即代码 Terraform/OpenTofu/Pulumi/Ansible + policy-as-code OPA / 容器与编排 Docker/Kubernetes + Helm/Kustomize + service mesh Istio/Linkerd / 可观测性 Prometheus + Loki + OpenTelemetry + Honeycomb + eBPF + RED/USE / SLO-SLI-error budget 与可靠性工程 Google SRE 学科 + 容量规划 + 优雅降级 / 事件管理与 on-call 事件指挥 + PagerDuty + runbook + 无指责复盘 + MTTR / 云平台与 FinOps AWS/GCP/Azure + 成本优化 + 弹性伸缩 / 平台工程与开发者体验 IDP + Backstage + golden path + Team Topologies / DevSecOps 与供应链安全 shift-left + SBOM + SLSA + sigstore + Vault / 韧性与混沌工程 fault injection + game day + 安全科学 / DORA 指标与工程效能 部署频率 + 变更前置时间 + 变更失败率 + Accelerate 研究 / 数据库与有状态运维 schema 迁移 + 备份容灾) — 不含 通用应用开发 / 纯云销售认证速成 / 'DevOps = 跑 Jenkins 的岗位' 窄化误解 / ITIL 工单文化传统运维 (旧范式仅做边界) / 把手工运维 ClickOps 当稳态 (是 toil, 本 skill 核心反模式) (DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, Helm / Kustomize, service mesh Istio / Linkerd), (d) observability (the three pillars + beyond — metrics Prometheus / VictoriaMetrics, logs Loki / ELK, traces OpenTelemetry / Jaeger / Tempo, high-cardinality observability Honeycomb, eBPF, RED / USE methods, SLO-based alerting), (e) SLO / SLI / error budgets & reliability engineering (Google SRE discipline — service level objectives, error budget policy, toil budgets, capacity planning, load shedding, graceful degradation), (f) incident management & on-call (incident command, PagerDuty / Opsgenie, runbooks, blameless postmortems, MTTR / MTTD, error budget burn), (g) cloud platforms & FinOps (AWS / GCP / Azure well-architected, multi-region, cost optimization, autoscaling), (h) platform engineering & developer experience (internal developer platforms, Backstage, golden paths, self-service, Team Topologies), (i) DevSecOps & supply-chain security (shift-left, SAST / DAST, SBOM, SLSA, sigstore / cosign, secrets management Vault), (j) resilience & chaos engineering (chaos experiments, fault injection, game days, resilience engineering / safety science), (k) DORA metrics & engineering effectiveness (deployment frequency, lead time, change failure rate, MTTR, the Accelerate research), (l) databases & stateful operations (schema migrations, backups / DR, replication); NOT generic software development / app feature coding (是 平行学科, DevOps/SRE 关注 delivery + operability 不是 product feature), NOT pure cloud sales / certification cram without operational depth, NOT 'DevOps = a job title that runs Jenkins' 的窄化误解 (DevOps 是 文化 + 实践, SRE 是 Google 对 reliability 的工程化具体实现), NOT ITIL-heavy 传统运维 工单文化 (是 被 DevOps 取代的旧范式, 仅做边界标注), NOT manual ops / ClickOps as a steady state (是 toil, 本 skill 的核心反模式).) Master OS — automated mastery of DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, He
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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Audits SaaS, cloud, payment, and AI spend. Use when analyzing bills, right-sizing plans, sizing Savings Plans or CUDs, cutting payment-processing fees, or LLM self-host breakeven.
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Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.
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Expert guide for AI FinOps, dynamic model routing (LiteLLM, Portkey, RouteLLM), cost optimization, budget enforcement, semantic caching, and latency-based fallback architectures / Panduan ahli untuk AI FinOps, routing model dinamis, optimasi biaya, penegakan anggaran, semantic caching, dan arsitektur fallback berbasis latensi.
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Retirement projection with Monte Carlo simulation logic. Calculates required nest egg via 4% rule and 25x expenses, projected vs needed gap, contribution recommendations, asset allocation by age, Social Security claim optimization, healthcare cost projections, and withdrawal sequence planning. Use when the user says "/finance retirement", "am I on track for retirement", "how much do I need to retire", "Social Security timing", or any retirement question.
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Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
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Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.
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