152 个 ・ 集成工具: Datadog · 按人气排序
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
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Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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Triage npm packages for install-script malware, exfiltration, and worming behavior.
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Interactive guided tour of all available AI coding skills with live demos. Walks through headline capabilities, offers try-it-now demos, discovers repo-specific tools, and provides a cheat sheet reference. Triggers on what can you do, show skills, skill tour, available tools, capabilities, what skills.
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Handles containerization, CI/CD pipelines, and deployment setup.
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Use when working with error diagnostics smart debug
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Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability.
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Use when working with debugging toolkit smart debug
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Cloud infrastructure and DevOps workflow covering AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, monitoring, and cloud-native development.
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Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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Implement structured logging with JSON formats, log levels (DEBUG, INFO, WARN, ERROR), contextual logging, PII handling, and centralized logging. Use for logging, observability, log levels, structured logs, or debugging.
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Expert en Datadog (APM, logs, infrastructure, monitors, dashboards, SLOs)
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Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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Add PostHog log capture to track application logs. Use after implementing features or reviewing PRs to ensure meaningful log events are captured with structured properties. Also handles initial OTLP exporter setup if not yet configured.
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Inngest イベント駆動 Durable Execution リファレンス。バックグラウンドジョブ、長時間ワークフロー。 createFunction, step.run, step.sleep, step.waitForEvent, step.waitForSignal, step.invoke, step.sendEvent, step.sleepUntil, step.fetch, step.ai.infer, step.ai.wrap, step.metadata, inngest.send, serve, connect, checkpointing, Inngest CLI。 concurrency, throttle, debounce, rate-limit, batching, priority, idempotency, singleton, multi-tenancy フロー制御。 Realtime（channels, topics, publish, subscribe, subscription tokens, useRealtime）、 Agent Evals（scoring, group.experiment）、MCP（Dev Server / Cloud）。 デプロイ先 Next.js, Express, Hono, Cloudflare Workers, Vercel, Netlify, DigitalOcean, Render。
中文简介正在准备中。当前显示原文说明。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
中文简介正在准备中。当前显示原文说明。
Query and filter Datadog logs from the shell using the Composio CLI. Run scoped log searches, pivot across services/environments, and export structured JSON for downstream agents instead of click-driving the Datadog UI.
中文简介正在准备中。当前显示原文说明。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs. NOT for OTel metrics/logs pipelines, vendor backend setup (Datadog/Honeycomb/Jaeger/Tempo), or browser RUM tracing.
中文简介正在准备中。当前显示原文说明。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.
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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...
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You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.
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Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
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