监控
日志、指标与可观测性
用途与选择建议
这类技能用于构建、交付和基础设施维护。使用前确认哪些命令会修改远程资源、是否兼容当前云平台，以及是否提供可执行的回滚步骤。
performance-analysis
ruvnet
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
ccboard
FlorianBruniaux
Comprehensive TUI/Web dashboard for Claude Code monitoring
render-monitor
render-oss
Monitor Render services in real-time. Check health, performance metrics, logs, and resource usage. Use when users want to check service status, view metrics, monitor performance, or verify deployments are healthy.
test-tui
openai
Guide for testing Codex TUI interactively
analyzing-linux-system-artifacts
mukul975
Examine Linux system artifacts including auth logs, cron jobs, shell history, and system configuration to uncover
instrumenting-first-party-metrics
PostHog
"How to instrument PostHog's own Metrics product from PostHog-owned code — record counters, gauges, and histograms that land in posthog.metrics, the same way customers do. Use when adding application metrics in this monorepo (web, Celery, Temporal), when asked to push or ship metrics into posthog metrics, or when unsure whether the SDK in this environment supports posthog.metrics yet. Covers the environment decision (SDK-first per the public docs, OTel fallback when the SDK path is not available), the exact version gates per SDK, what is already wired internally, and how to validate metrics actually arrive."
monitoring-capture-service
PostHog
Guide for using the Grafana MCP to monitor and diagnose the capture service (rust/capture) in production. Use when investigating latency, event loss, Kafka backpressure, Redis issues, rate limiting, Envoy proxy issues, or any capture health question. Covers prod-us and prod-eu environments.
hogli
PostHog
PostHog developer CLI and repo tooling reference. Use when the user mentions hogli, asks about repo CLI tools, bin scripts, Makefiles, how to run/build/test/lint, or any dev environment commands.
exploring-ai-failures
PostHog
Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces. Use when someone wants to understand what's going wrong with an AI feature, find and categorize failure modes, triage errors, or investigate quality issues (wrong answers, ignored instructions, hallucinations, tool misuse) — "what's failing in my agent", "surface error patterns", "why are the responses bad", "find the common failure modes", "what should I fix next". Covers scoping to one use case, finding failing traces by whichever signal fits the context (code errors, metric outliers, trace-type slices, manual review, existing-eval spikes, clustering), and reading them into a ranked failure taxonomy.
analyzing-experiment-query-performance
PostHog
Pull and interpret production experiment query-performance data from the staff-only /api/debug_ch_queries endpoints backing the /experiments/staff scene: slowest experiment queries, precompute read/build health, and preaggregation cache footprint. Covers prod-US and prod-EU via a query_performance:read personal API key, all query params, and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
understanding-billing-usage
PostHog
Explains PostHog billing usage and spend from the customer's visible Billing MCP tools. Use when the user asks why usage or spend is high, which product or project is driving usage, what a usage type means, how to reduce usage, what changed over time, why they got a usage change alert, or whether a spike/drop alert was real or noisy. Also use before product-specific analytics skills when the user names a billable PostHog product metric such as events, recordings, feature flag requests, exceptions, survey responses, synced rows, logs, AI events, AI credits, or Inbox credits. Starts from Billing usage/spend tools, then routes to customer-visible product MCP surfaces for deeper investigation.
context-status
brycewang-stanford
Show current context status and session health. Use to check how much context has been used, whether auto-compact is approaching, and what state will be preserved.
watchers
NousResearch
Poll RSS, JSON APIs, and GitHub with watermark dedup.
weekly-production-review
langfuse
Prepare Langfuse weekly production reviews covering failures, fixes, open issues, and tracking gaps. Use for "what broke last week," production bugs, Datadog alerts or error patterns, incident.io activity, or pager load.
datadog-query-recipes
langfuse
Research Langfuse production telemetry with reusable Datadog queries. Use for tenant or project activity, API usage, queue behavior, spans, logs, metrics, or ad hoc measurements across production regions; pair with debug-issue-with-datadog for root-cause analysis.
debug-issue-with-datadog
langfuse
Establish root cause by combining Datadog telemetry with the Langfuse repo. Use when investigating or triaging a user report, Linear or GitHub issue, incident, or pasted production error.
incident-alert-tickets
langfuse
Read and, after human approval, update the Linear incident-alert knowledge base. Use before and after investigating a named Datadog monitor, incident.io alert or incident, or on-call page to find or record root causes.
agent-evaluation
sickn33
Testing and benchmarking LLM agents including behavioral testing,
agent-orchestration-multi-agent-optimize
sickn33
"Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability."
chat-perf
microsoft
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
otel
microsoft
OpenTelemetry instrumentation for the Copilot Chat extension — covers the four agent execution paths, the IOTelService abstraction, span/metric/event conventions, and the relationship between code and the user/developer monitoring docs. Use when adding/changing OTel spans, metrics, or events; instrumenting a new agent surface; touching the Copilot CLI bridge or Claude span emission; or updating extensions/copilot/docs/monitoring/agent_monitoring*.md.
tracing
langwatch
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.
agent-improve
langwatch
Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that capture production signals, and experiments that settle open questions. Use when you want to know what to do next to improve your agent.
eval-triage
langwatch
Investigate failing experiments and evaluations with LangWatch. Triage a failing experiment run to the exact rows and evaluator scores that regressed, then to a root cause. Use when an experiment fails, scores drop, or evaluations regress.