427건 · 관련도순
Record and query iii engine logs, traces that show how calls flow, and operational metrics. Set up reactions to incoming errors, inspect alerts, and review metrics aggregated over time windows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Set up distributed tracing in KubeSphere to follow requests across services. Covers extension installation, trace collection, automatic application instrumentation, and API queries for traces, individual processing steps, and service connections.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Set up and manage a local Kubernetes development environment for OpenShell using k3d, Skaffold, and Helm. Build and deploy changes, inspect traces, configure optional authentication components, and test behavior across multiple gateway replicas.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Investigate and fix production errors reported in Sentry through MCP, a connection to external tools. Analyze error details and execution history, check them against source code, implement fixes, and verify them with tests and regression checks.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create terminal-style review slides for command-line tools, AI agents, and developer tools. Present execution traces, code differences, and performance comparisons using dark backgrounds, mint-green text, outlined bar charts, and amber, green, or red tags.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Choose diagnostic tools and collect performance traces for production .NET problems such as high CPU usage, memory growth, or slow requests. Get environment-specific collection commands and guidance on which tools to use for later analysis.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze gh-aw execution traces, which record individual processing steps, to investigate slow or failing workflows. Using local JSONL files or telemetry backends, the skill checks timing, errors, missing attributes, and trace connections to support focused improvements.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Test APIs and forms with unexpected input to uncover error responses that expose internal details. The skill checks response bodies for stack traces, server paths, and library information, preserving specific evidence rather than treating every server error as a vulnerability.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Investigate bugs in a running local application using service logs, traces that follow requests across services, and a shared browser. Connect frontend actions to backend activity, inspect failures, and verify that tracing records appear as expected.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Add OpenTelemetry tracing to frontend and backend flows. Connect execution records across services and background jobs, record phase durations and entity IDs, and mark failures so developers can investigate slow or broken operations.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze Chrome, Chromium, Electron, React DevTools, or Perfetto-compatible JSON traces and audit user-reported profiling findings without loading large artifacts into context; prove trigger-to-render/layout chains, separate measured facts from source inference, find exact code choke points, classify forced layout and render fanout, implement semantically safe fixes, and verify behavior plus repository budgets. Use for trace files, reported profiling durations or call chains, dropped frames, long tasks, resize or scroll jank, render storms, layout thrashing, selector hot paths, interaction latency, or requests to locate exact source-level bottlenecks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
[omh] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: "agent not working", "wrong answer", "agent error", "tool call failing", "debug agent", "check logs", "read traces", "broken", "500 error", "424 error", "model access denied", "command not found", "stuck in DELETING", "maxVms exceeded", "cold start diagnosis", "cold start slow", "agentcore create error", "create failed", "exit code 7", "connection refused local dev". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Read and write Langfuse: browse traces and observations, list prompts and datasets, score traces, add dataset items. Trigger phrases: langfuse, llm observability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Parses error messages, traces execution flow through stack traces, correlates log entries, and applies systematic hypothesis-driven methodology to isolate and resolve bugs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.