62 skills · sorted by relevance
Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.
Turn existing PromQL or ListTimeSeries queries into Cloud Monitoring chart definitions in textproto, a text format for Protocol Buffers. Generate readable titles, axis labels, units, and plot types, then validate the resulting widget files.
Design service monitoring with Prometheus, write PromQL queries to analyze metrics, and build Grafana dashboards. Configure actionable alerts and manage metric labels so monitoring stays useful as the volume of collected data grows.
Investigate interruptions affecting GPU and TPU workloads on GKE using maintenance labels, monitoring queries, logs, and node scheduling restrictions. The skill helps determine whether host maintenance caused a disruption and recommends workload protection measures when maintenance is confirmed or expected.
Define reliability goals for Google Cloud services and generate Terraform files for Cloud Monitoring SLO alerts. A guided conversation gathers service scope, measurement metrics, targets, and alert conditions, then explains the PromQL queries used to evaluate them.
Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.
Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
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Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.
Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.
Set up KubeEye in KubeSphere to inspect Kubernetes clusters. Define checks for configurations, nodes, and services, schedule recurring or one-time inspections, and retrieve findings as HTML or spreadsheet reports.
Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.
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Use this skill when gathering, documenting, or validating requirements for a Salesforce marketing automation program — covering MCAE (Account Engagement / Pardot) lifecycle stages, MQL/SQL threshold definitions, scoring model specifications (sources, weights, decay, ceiling), handoff notification design, CRM field updates on status change, and sales SLA. Trigger keywords: MQL criteria, marketing-to-sales handoff, lead lifecycle, scoring requirements, automation program requirements, Marketing Cloud Automation Studio scope. NOT for building the scoring and grading rules in MCAE — use admin/mcae-lead-scoring-and-grading. NOT for a Sales-Cloud composite score built from formula fields — use admin/lead-scoring-requirements.
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Use this skill when configuring MCAE (Account Engagement / Pardot) lead scoring models, grading profiles, score decay rules, or automation rules that fire on score/grade thresholds. Covers: scoring point values per activity, score decay for inactivity, Profiles for fit grading, combined MQL definitions (Score + Grade), and automation rules vs completion actions. NOT for defining the MQL model before build — use admin/lead-scoring-requirements. NOT for Engagement Studio nurture programs — use admin/lead-nurture-journey-design.
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Use this skill when designing lead nurture journeys in MCAE (Account Engagement / Pardot) Engagement Studio — mapping content to funnel stages (Awareness, Consideration, Decision), behavioral trigger rules, branching program paths, and MQL handoff criteria to Sales. NOT for building a journey in Marketing Cloud Journey Builder — use admin/journey-builder-administration. NOT for the scoring and grading model behind MQL — use admin/mcae-lead-scoring-and-grading.
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Design the processes connecting marketing, sales, and customer success. Produce documents for lead scoring, meaning prospect prioritization, along with assignment rules, handoff deadlines, deal stages, CRM workflows, and dashboards that show where prospects stall.
Translate a Python trading strategy or written trading rules into code for TradingView, 通达信, 同花顺, 东方财富, and MT5. Receive platform-specific indicators or strategy code, setup instructions, and notes explaining features that cannot be translated directly.
Query Prometheus metrics through Grafana and manage dashboards as JSON files. Use it to investigate current values and trends, add or adjust panels, and preview dashboard updates before applying them.
Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.
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Generate/create/write PromQL queries, metric expressions, alerting rules, recording rules, Prometheus dashboards.
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Validate, lint, audit, or fix PromQL queries and alerting rules; detects anti-patterns.
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Define lifecycle stages and marketing → sales / CSM handoff rules for leads captured by GTM channels during PRD v0.8 Deployment & Ops. Triggers on requests to set up lead lifecycle, define MQL/SQL criteria, build handoff process, or when user asks "what's an MQL?", "marketing to sales handoff", "lead lifecycle", "lead routing", "RevOps", "MOPS", "marketing operations". Outputs BR-MOPS-* lifecycle rules and GTM-MOPS-* handoff flow entries.
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Use when asked to execute or investigate a PromQL expression against a Prometheus server. Read-only HTTP queries only. No source or remote-system changes.
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Prometheus provider for obz. Covers all 6 metric commands using standard PromQL. This skill should be used when the user mentions "Prometheus", "PromQL", "obz metric -p prom", or needs to query metrics from a Prometheus server.
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