'crd' 검색 결과

여러 기기나 서버의 업데이트를 합치는 데이터 동기화 구현을 돕습니다. 카운터와 집합 등의 CRDT를 사용해 변경분 전송과 작업 간 인과관계를 다룹니다.

• 여러 노드의 개수 집계
• 공유 집합의 동시 업데이트 병합
• 변경분만 보내는 동기화 구현

Build and review Kubernetes Operators, controllers that keep custom resources in their desired state. Included tools check resource definitions and Go reconciliation code, then assess operational capabilities and suggest improvements.

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• Designing custom resource definitions for a new Operator
• Reviewing Go reconciliation code for common mistakes
• Assessing Operator capabilities and planning improvements

Generate FrontendIntegration YAML for frontend-forge from a simplified configuration containing one menu and one or more pages. Define resource tables or embedded iframe pages, with the bundled generator expanding menu placements into the required YAML structure.

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• Generating integration YAML for a CRD resource table
• Defining an embedded iframe page
• Converting a single-menu configuration into canonical YAML

Manage OpenKruise in KubeSphere, from extension installation to troubleshooting. Generate SidecarSet configuration for adding helper containers and CloneSet configuration for application updates, including in-place changes and staged rollouts, with commands to apply and verify each configuration.

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• Install OpenKruise and check its status
• Add and update helper containers with SidecarSet
• Stage application image updates with CloneSet

Manage Fluid in KubeSphere with configuration files and commands for installation, data connections, caching, status checks, and troubleshooting. Prepare Dataset and runtime resources, preload data into cache, and adjust runtime replica counts.

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• Install or upgrade Fluid in KubeSphere
• Configure data connections and caching
• Preload data into cache with DataLoad

Agent skill for crdt-synchronizer - invoke with $agent-crdt-synchronizer

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Manage KubeSphere’s Traefik-based Gateway API extension for external access to applications. Install, upgrade, or remove it across selected clusters, and inspect GatewayProxy resources, which manage proxy instances, to investigate deployment failures and unhealthy gateways.

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• Install or upgrade the extension on selected clusters
• Inspect GatewayProxy status by scope
• Investigate Traefik startup and update failures

Install, upgrade, and remove the ingress-nginx-based KubeSphere Gateway extension, which manages external access to applications. Inspect gateways across cluster, workspace, and project scopes, and investigate stalled operations, Helm installation failures, or pod problems.

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• Installing or upgrading the Gateway extension
• Checking gateways by cluster, workspace, or project
• Investigating stalled gateway operations and pod failures

Plan and configure Kubernetes deployments, Git-based continuous delivery, and cloud resources defined in code. Covers Helm, Kustomize, ArgoCD, Flux, Terraform, Pulumi, and AWS CDK, with patterns for environment separation, secrets, validation, updates, and rollback.

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• Create Helm charts and environment-specific configurations
• Set up Git-based delivery with ArgoCD or Flux
• Organize cloud resources with Terraform or Pulumi

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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Design, build, and operate Kubernetes operators. Use when extending Kubernetes with a custom controller, choosing a framework, designing CRDs, implementing reconciliation loops, or auditing an operator for anti-patterns.

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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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Capstone/orchestration skill — build an M-Agent + N-Human cooperative IDE in Rust gpui (the Harbor): many agents and humans co-editing the same files as co-equal CRDT replicas, governed by claims/guard/salvage, across LAN/shared/remote harbors. The INDEX that dispatches into the sibling rust skills. Use when building the collaborative editor, the agent-fleet console, multiplayer editing with agents-as-peers, or any slice of the Harbor. Trigger on: cooperative IDE, collaborative editor, multiplayer editor, agents and humans co-editing, gpui IDE, Loro CRDT editor, harbor editor, claims/salvage, "build the cooperative IDE". NOT for: a single non-collaborative gpui screen (compose the siblings directly), web editors, or non-editor apps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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Assess Kubernetes security from an existing pod or API access point. Covers permission checks, exposed node services, secrets, host access, and cloud metadata to explain how configuration weaknesses can expand access across a cluster.

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• Review service account permissions and RBAC settings
• Assess host access and secrets exposure from pods
• Trace access paths from a cluster to cloud infrastructure

Configure mirrord to route selected Temporal tasks to a local worker. Generate Kubernetes configuration files, connection settings, and task filters, then check that the settings agree and troubleshoot common setup problems.

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• Route only test workflows to a local worker
• Configure a Temporal Cloud connection
• Filter tasks by workflow input

Configure mirrord to route selected Kafka messages to a local application. Generate Kubernetes resource files and matching message filters, check their consistency, and troubleshoot connection or splitting issues, including Kafka Streams and AWS MSK IAM setups.

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• Set up Kafka message splitting for local debugging
• Filter messages by headers or payload fields
• Configure Kafka Streams or AWS MSK IAM connections

Work with documents in OpenKnowledge through its MCP tools, which connect agents to the knowledge base. Search and edit Markdown, use templates, check internal links, and inspect version history while keeping claims connected to preserved sources.

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• Finding answers in existing knowledge base documents
• Creating and editing documents with templates
• Checking internal links and document rules

Multi-protocol consensus for agent swarms supporting Raft leader election, Byzantine fault tolerance, Gossip state propagation, and CRDT conflict-free merging.

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Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.

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Generate/create/scaffold Kubernetes YAML — Deployment, Service, ConfigMap, Ingress, RBAC, StatefulSet, CRDs.

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Validate, lint, audit, or dry-run Kubernetes manifests (Deployment, Service, ConfigMap, CRD).

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Validate, lint, audit, check Helm charts — Chart.yaml, templates, values.yaml, CRDs, schemas.

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