359 skills · sorted by relevance
Set up KubeSphere’s WizTelemetry Data Pipeline to collect, transform, and route logs, audit data, events, and notifications. Configure delivery to OpenSearch, deploy agents across selected clusters, and check their operating status.
Inspect a vehicle or another object in a running CARLA simulation. Read position, speed, and acceleration from the same simulation frame, check vehicle controls and steering angle, or stream the object's state over time.
Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".
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Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".
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Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit
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AgentCore の Observability ナレッジ。OpenTelemetry 必須4点セット（OTEL_EXPORTER設定・StrandsTelemetry・traceable デコレータ・Langfuse連携）、EMF メトリクス、Jaeger/コンソールエクスポーターでのトレース確認、トレースが出ない・繋がらない場合のデバッグ手順等
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Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. Activates for requests to build a telemetry baseline, find rare or first-seen activity, or compute frequency baselines for anomaly hunting.
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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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Run and troubleshoot OpenClaw QA scenarios using simulated responses or live services. Inspect reports and saved outputs, extend test scenarios, and compare response styles across models through character evaluations with rankings and transcripts.
Investigate slow Paperclip runs, page requests, and CI suites using Honeycomb traces, which show timed operations. Find relevant traces, separate overlapping work, identify dominant delays, and configure optional tracing or add missing measurements.
Set up telemetry, data about application operation, for web apps hosted in Azure. Choose automatic or manual instrumentation, create an Azure App Insights resource, and configure application code to send monitoring data.
Helps implement iii workers in Node.js, browsers, Python, and Rust with language-specific guidance on installation, function registration, calls, data channels, and logging. Explains package choices and connection constraints for each environment.
Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.
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Declarative OpenTelemetry-aligned telemetry vocabulary and instrumentation conventions for traces, metrics, logs, and PII handling
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Provides guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.
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Add OpenTelemetry tracing to MATLAB code. Use when the user asks to "add tracing", "instrument with spans", "add OpenTelemetry", "trace my code", "add observability" (when about tracing), or mentions "spans", "distributed tracing", or "OTel tracing" in the context of MATLAB functions. Covers span creation, parent-child context propagation, error handling, attributes, events, and semantic conventions.
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Expert knowledge for Azure Microsoft Opentelemetry development including configuration. Use when setting sampling, exporters, resource attributes, env vars, or tuning tracing/metrics behavior, and other Azure Microsoft Opentelemetry related development tasks. Not for Azure Monitor (use azure-monitor).
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Enforce session-cost telemetry and execution-metadata reporting. Use when explicitly invoking get_session_cost, reporting token/cost usage, applying telemetry or cost guidance during a workflow handoff, or writing artifacts/session-cost.md at a workflow terminal state.
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The telemetry each Active Directory technique generates and what alerts a defender: Kerberoasting produces Event 4769 with RC4 encryption (0x17) and an MDI alert, DCSync produces Event 4662 with the DS-Replication-Get-Changes GUID, AS-REP roasting produces Event 4768 with no pre-auth, LSASS dumping is blocked by EDR, plus a lateral-movement telemetry table by protocol (SMB/WMI/WinRM/RDP/DCOM). Use this whenever you run or plan an offensive AD technique and need to know what noise it makes, when writing the engagement's detection notes, or when a defender wants to know what to monitor. Teaches red teamers what is loud and blue teamers what to watch. Standard-tooling knowledge, no vendor engine.
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Expert guide for observability, analytics, telemetry, and data pipelines (OpenTelemetry, PostHog, Mixpanel) / Panduan ahli untuk observabilitas, telemetri, dan analitik.
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Instrument applications with OpenTelemetry for distributed tracing, including auto and manual instrumentation, context propagation, sampling strategies, and integration with Jaeger or Tempo. Use when debugging latency issues in distributed systems, understanding request flow across microservices, correlating traces with logs and metrics for root cause analysis, measuring end-to-end latency, or migrating from legacy tracing systems to OpenTelemetry.
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Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, `sqlite<Contract>(...)` from `@prisma-next/sqlite/runtime`, or `mongo<Contract>(...)` from `@prisma-next/mongo/runtime`; middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres, SQLite, and Mongo façades. Use for db.ts, postgres(), sqlite(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.
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Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.
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Instrument applications with Azure Application Insights and OpenTelemetry for end-to-end observability. Use this skill when users ask about telemetry, tracing, metrics, logging, or monitoring setup. Covers SDK setup, correlation, sampling, dashboards, and alerts per OpenTelemetry standards.
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