DevOps 与云
部署、CI/CD、云平台和基础设施
Skills 列表
loki-label-analyzer
Expert evaluator for Grafana Loki label strategy. Audits, designs, and improves label schemas using cardinality scoring, access-pattern alignment, static vs. dynamic label rules, and consistency checks. Use when the user asks to evaluate, audit, design, or improve a Loki label strategy — or asks why their Loki queries are slow.
grafanadpm-finder
Find the Prometheus metrics that drive your Grafana Cloud bill. `dpm-finder` is a Grafana Professional Services CLI that ranks metrics by Data Points per Minute (DPM) with per-label-set breakdown, optional `--cost-per-1000-series` pricing, and a Prometheus-exporter mode. Use when investigating high Grafana Cloud spend, hunting noisy / high-cardinality metrics, comparing pre/post recording-rule cardinality, or feeding cost data into dashboards — even when the user says "why is my Mimir bill so high?", "find the biggest metrics", "cardinality offenders", or "optimize Prometheus cost" without naming dpm-finder.
grafanaapp-sdk-concepts
Use when starting any grafana-app-sdk work — scaffolding a Grafana app, initializing a Grafana App Platform app, picking a deployment mode (standalone operator / grafana/apps / frontend-only), wiring app-specific config, or onboarding to the SDK. Covers `grafana-app-sdk` CLI install, `project init` per deployment mode, project layout, the schema-centric workflow (CUE kinds → generated code → reconciler/admission logic), and `SpecificConfig` for env-driven app configuration. Use even if the user just says "make a new app", "Grafana app platform", "kinds and watchers", "operator scaffold", or asks about `apps/` inside the Grafana repo, without naming the SDK explicitly.
grafanainfrastructure
Ship Kubernetes, host, container, and cloud-provider telemetry into Grafana Cloud — `k8s-monitoring` Helm chart for K8s clusters (metrics + logs + traces + events + cost), Alloy `prometheus.exporter.unix` for Linux hosts, cAdvisor + Docker discovery for containers, and CloudWatch / Azure Monitor / Google Cloud Monitoring datasource setup. Use when onboarding a new cluster or VM fleet to Grafana Cloud, picking the right Helm values for K8s scraping, wiring kube-state-metrics + node-exporter + cAdvisor, alerting on `PodCrashLooping` / node memory / PVC capacity, or pulling AWS / Azure / GCP cloud metrics — even when the user says "monitor my cluster", "send K8s metrics to Grafana", "scrape EC2 metrics", "cluster pod logs", or "install the monitoring helm chart" without naming `k8s-monitoring` or Alloy.
grafanamimir
Stand up Grafana Mimir for horizontally scalable, multi-tenant, long-term Prometheus + OTLP metrics storage. Covers monolithic / read-write / microservices deployment, S3 / GCS / Azure / filesystem block storage, Prometheus `remote_write` and OTLP ingestion, multi-tenancy with `X-Scope-OrgID`, ingester replication factor, compactor retention, and per-tenant limits. Use when running Mimir locally or on Kubernetes (Helm `mimir-distributed`), scaling Prometheus past a single node, picking ingest / query / backend split, configuring tenants and ingestion rate, debugging `/ready` 503s or `429 Too Many Requests`, or pointing Grafana at a Mimir datasource — even when the user says "I need long-term Prometheus storage", "scale Prometheus", "multi-tenant metrics backend", "Cortex replacement", "remote_write target", or "store 10M active series" without naming Mimir.
grafanadashboarding
Build, modify, and ship Grafana dashboards as JSON via the HTTP API — panel types (timeseries / stat / gauge / table / heatmap / logs / traces / node-graph), `gridPos` 24-column layout, units, thresholds, template + datasource + chained variables, transformations (`organize` / `calculateField` / `filterByValue`), panel + dashboard links with `${__field.labels.x}` / `${__from}`, and Loki/Prometheus annotations. Use when scripting dashboard creation, writing the dashboard JSON for a new service, adding a `$job` dropdown variable, computing an "Error %" column with a transformation, overlaying deploys as annotations, or pushing a dashboard via `POST /api/dashboards/db` — even when the user says "create a dashboard for this metric", "add a service dropdown", "show errors as percentage", "overlay our deploys", or "export the dashboard JSON" without naming the API or schema. After every API push, verify with the returned `version` plus a GET on the dashboard UID.
grafanaalloy
Build a unified telemetry pipeline with Grafana Alloy — one OpenTelemetry-compatible binary that collects metrics, logs, traces, and profiles and ships to Grafana Cloud / Prometheus / Loki / Tempo / Pyroscope. Covers the Alloy config language (blocks, `sys.env`, component refs), `prometheus.scrape` → `remote_write`, `loki.source.file` + `loki.process` → `loki.write`, `otelcol.receiver.otlp` → `otelcol.exporter.otlp`, `pyroscope.scrape`, K8s / Docker / EC2 discovery, relabeling, modules (`import.file/git/http`), clustering, Fleet Management `remotecfg`, the Alloy UI at `:12345`, and `alloy fmt` / `alloy validate`. Use when writing a `config.alloy`, replacing Grafana Agent / OTel Collector, scraping K8s pods, parsing logs, ingesting OTLP, or debugging "Alloy isn't sending anything" — even when the user says "set up the agent", "write me a scrape config", "drop these logs before sending", or "OTel collector config" without naming Alloy.
grafanatempo
Stand up Grafana Tempo as a cost-efficient distributed-tracing backend that only needs object storage, and write TraceQL queries against it. Covers OTLP + Jaeger + Zipkin ingestion, the distributor → live-store → block-builder → object-storage write path, metrics-generator for RED spanmetrics + service graphs, Helm `tempo-distributed` deployment, multi-tenant `X-Scope-OrgID`, TraceQL span / resource / event scopes, structural operators (`>>`, `<<`), `rate()` + `quantile_over_time` metrics, and the traces-to-logs / metrics / profiles datasource links. Use when deploying Tempo, writing a TraceQL query for slow / errored requests, debugging "no traces showing in Explore", sizing queriers / compactors, configuring S3 / GCS / Azure block storage, or wiring trace ↔ log ↔ profile correlation — even when the user says "tracing backend", "find slow requests", "show me the service graph", "store traces in S3", "Jaeger compatible store", or "what called this span" without naming Tempo.
grafana
eval-model
Measures the task accuracy of text models served by MAX using standard benchmarks such as GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. Use when benchmarking a served model, comparing it with model-card or reference scores, verifying that a new MAX model produces correct answers, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.
modular
temporal-developer
Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET, Ruby, and Rust. Use when the user is building workflows, activities, or workers with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns. Also use when the user mentions "run a Temporal workflow from the CLI", "start a dev server", "run temporal server start-dev", "temporal workflow start", "temporal workflow execute", "temporal workflow signal", "temporal workflow query", "temporal workflow update".
temporalio
byted-mediakit-audio
面向音频文件或视频中的音轨，处理语音边界定位、音频媒资信息探测、音频转码与码流封装适配、人声与背景声分离等目标。若对象和目标族已明确属于音频内容理解、音频转码、音频格式治理或音轨分离，但具体做法不确定，可先加载本 Skill 探索。
volcenginebyted-mediakit-video
面向视频文件的智能处理、媒资理解、画质治理与画质检测、抽帧、隐私保护、语音转字幕、字幕提取、字幕擦除、水印处理、精彩片段与高光拆条分析生成、剧情结构化与剧本整理、场景与语义分段、画面文字识别、视频转码转封装及抠像换脸等目标。若对象和目标族已明确属于视频增强、视频分析理解、视频内容结构化、从视频提取字幕、语音转字幕、视频字幕识别或擦除、视频隐私脱敏、视频媒资探测或分发适配，但具体能力不确定，可先加载本 Skill 探索。
volcenginebyted-mediakit-image
面向单张或批量图片的视觉处理、质量优化、内容理解与基础编辑目标，适用于图片尺寸缩放与体积治理、元信息探测、裁剪旋转翻转与圆角、颜色与锐化清晰度调整、负片、模糊与打码、水印、背景移除、文字识别、画质评估与智能裁剪等。若对象和目标族已明确属于图片优化、图片理解或图片隐私保护，但具体做法不确定，可先加载本 Skill 探索。
volcenginebyted-mediakit-editing
面向音频、视频或图片素材组成成片的编辑制作目标，适用于时间线裁剪与拼接、速度和音量调整、视频滤镜、运镜特效、转场、画面裁切旋转翻转、画面叠加、字幕压制、动图截取、淡入淡出、音视频提取与合流、音频混合、文字滚屏成片、图转视频以及多画面空间组合等操作。若用户要给视频添加滤镜效果，或对象和目标族已明确是对现有素材做剪辑、合成、叠加、混合或成片编排，但具体做法不确定，可先加载本 Skill 探索。
volcengine
workflow-model-design
Design and create AEM Workflow models on AEM as a Cloud Service. Use when creating workflow models via the Workflow Model Editor or content package XML, defining step types (PROCESS, PARTICIPANT, DYNAMIC_PARTICIPANT, OR_SPLIT, AND_SPLIT), configuring step properties, declaring workflow variables, and deploying models through the Cloud Manager pipeline.
adobeconfig-authoring
Create, modify, review, and harden configuration for the Adobe Dispatcher Apache HTTP Server module and Apache HTTPD in AEM as a Cloud Service environments only. Use for `.any`, vhost, rewrite, cache, and filter changes.
adobe
dtctl-release
Explain and operate the dtctl release process, which is automated by release-please. Use this skill whenever the user says "release", "ship it", "cut a release", "new version", "bump version", "publish", or asks how dtctl releases work, why a release PR exists, or how to trigger/finish a release.
dynatrace-ossoutdated-dependencies
AEM Cloud Service expert skill — upgrade outdated Maven dependencies in pom.xml, both literal <version> and same-pom ${property} shapes. Use for \"update my aem-sdk-api\", \"upgrade mockito\", or scanning a project for stale dependency versions. Discovery can find <dependency> blocks but \"outdated\" needs a target version, which the user supplies. Pattern A/B locators and editing strategy are in recipe.md.
adobe
mongodb-atlas-stream-processing
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
mongodb
dt-sec-contextualization
Resolve security signals, IoC matches, or Smartscape nodes to runtime Dynatrace entities and connect findings on different entity levels through a shared runtime entity. Covers identity-to-Smartscape mapping (incl. container-image digest/ID to workload), cross-level topology (K8s pod detection vs. node CVE via pod-to-node), per-entity risk summarization, and coverage match recipes shared by dt-sec-insights. Trigger: "map these findings to workloads/hosts", "which workload does this container image run as", "do these findings relate through the same runtime entity", "enrich this IoC match with entity context", "which threat report mentions this IoC". Queries security.events ONLY for THREAT_REPORT IoC enrichment (matched IoC to attributing reports); Do NOT use for broad security.events posture/overview (use dt-sec-insights), general DQL (use dt-dql-essentials), IoC hunting in logs/spans (use dt-sec-ioc-hunting), or K8s observability outside the security cross-level context (use dt-obs-kubernetes).
dynatracedt-obs-network-flows
Network flow analysis in Dynatrace across three sources: OneAgent flows (host/process/pod-to-peer connections in the `default_network_flows` Grail bucket), NetFlow/IPFIX/sFlow (via an OpenTelemetry Collector), and cloud flow logs (AWS VPC / Transit Gateway; Azure and GCP planned). Use to analyze traffic between entities, find top talkers by bandwidth, map communication dependencies, investigate connection health (resets, timeouts, retransmissions, RTT), and resolve peers to monitored entities. Routes each question to the right source; source-specific DQL lives in the reference files. Trigger: "network flows", "top talkers", "traffic between hosts", "connection resets", "TCP retransmissions", "RTT", "pod connections", "network dependencies", "NetFlow", "IPFIX", "sFlow", "VPC flow logs", "cloud network traffic". Do NOT use for host NIC throughput or packet drops (use dt-obs-hosts), service request rate or latency (use dt-obs-services), or synthetic/uptime monitoring (use dt-obs-ext-monitors).
dynatracedt-platform-costs
Query and analyze a Dynatrace tenant's ACTUAL billing and usage data with DQL against dt.system.events — DPS consumption breakdown, cost-normalized spend ranking, included volume deduction, chargeback/showback, cost drivers, spending trends, cost investigation, metrics ingest optimization, query cost attribution, workflow total cost, entity-level cost drill-down (RUM, hosts, synthetic, K8s), and AI/LLM cost (AI Units, AI Function Standard Calls, AI-generated query consumption). Directs licensing/entitlement questions to documentation (not via DQL). USE ONLY to query/analyze the tenant's actual consumption. Do NOT use for conceptual 'explain' questions about how DPS billing/pricing works or what units/weights/the rate card mean — see Dynatrace documentation. Do NOT use for making DQL queries faster or cheaper to run (query optimization, reducing scanned data/consumption per run, filter-early best practices) — that belongs to dt-dql-essentials. This skill MEASURES consumption; it does not tune queries.
dynatrace
dd-apm
APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.
datadog-labs
test-reliability
Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.
petrkindlmann