'tracing' 검색 결과

Site reliability specialist for Prometheus metrics, distributed tracing, alerting strategies, and SLO designUse when "observability, monitoring, prometheus, grafana, alerting, slo, sli, metrics, tracing, logging, on-call, incident, observability, prometheus, grafana, tracing, jaeger, alerting, slo, sli, metrics, logging, sre, ml-memory" mentioned.

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

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Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.

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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.

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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

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INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.

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실제 파일과 행 번호를 확인해 CodeTour 형식의 단계별 코드 안내를 만듭니다. 새 담당자의 학습, 설계 이해, PR 검토, 장애 경로와 보안 경계 확인에 활용합니다.

• 새 유지보수 담당자를 위한 코드 안내
• 설계와 기능의 실행 경로 이해
• PR 변경 사항을 순서대로 검토

Debug Node.js from the terminal by pausing execution, inspecting variables, and tracing function calls. Use interactive commands or scripts to investigate failing tests, collect runtime state, and capture CPU profiles or memory snapshots.

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• Inspecting intermediate state in failing Node.js tests
• Tracing function calls and unexpected variable values
• Investigating stalled asynchronous processing

Investigate software errors by tracing code, reviewing recent changes, and reproducing symptoms. Test a specific cause before making a minimal fix, then verify the result with regression tests and a structured debug report.

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• Investigating errors and stack traces
• Tracing bugs introduced by recent changes
• Verifying fixes and documenting regression tests

Turn a project's claude-mem history into weekly narrative chapters. Each chapter builds on the previous week's handoff, tracing ongoing work, decisions, and unresolved questions, with an index and chronologically ordered Markdown files.

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• Reviewing a project's development history week by week
• Tracing issues opened, continued, and resolved across weeks
• Creating indexed weekly chapters from a long project timeline

Inspect stored Muse records when regular product tools do not reveal enough detail. Use read-only database queries to investigate missing records, trace execution history, and check relationships or inconsistencies across different product areas.

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• Investigating missing or orphaned records
• Reconstructing execution history
• Tracing relationships across product areas

Investigate errors and unexpected behavior by finding related code, following function calls, and checking source files. GitNexus helps trace execution paths, while the diagnosis identifies the repository and reports how current its code index is.

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• Finding where an error originates
• Investigating an endpoint that returns 500
• Tracing calls between two functions

Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.

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• Auditing AI agent integrations in GitHub Actions
• Tracing issue and PR text into agent prompts
• Reviewing sandbox settings and tool permissions

Solve sandboxed CTF security challenges by reconstructing encoding and encryption steps, extracting data hidden in media, and tracing mobile app signing or storage behavior. Preserve exact parameters and evidence to reproduce decoded content or signed requests.

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• Reconstructing encoding and encryption chains in CTF challenges
• Extracting data hidden in images and other media
• Tracing APK or IPA request-signing logic

Organize CTF investigations by mapping the supplied targets, tracing one minimal path, and selecting the relevant specialist workflow. Findings are grounded in observed behavior and reproducible evidence, with a clean baseline used for verification.

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• Choosing an analysis path for a CTF challenge
• Tracing a minimal path through challenge infrastructure
• Packaging reproducible evidence for a solution

Analyze Android apps in a CTF sandbox using static inspection and Frida hooks, which observe or alter running code. Trace request signing, local storage, and Java-to-native boundaries to reproduce a request accepted by the server.

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• Tracing request-signing inputs and outputs
• Inspecting Java-to-native calls with runtime hooks
• Examining app settings, databases, and token storage

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

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• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services

Investigate incorrect, missing, or unexpectedly changed OpenViking memories by tracing memory files back to archived change records and, when needed, source messages. Return an evidence trail, identify gaps, and suggest next steps without modifying data.

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• Investigate incorrect memory content
• Check unexpected memory paths or owners
• Trace missing or deleted memories

Explore unfamiliar code with Codebase Memory to map architecture, trace function calls and dependencies, and assess change impact. Findings are checked against source files, with index freshness and coverage considered before drawing conclusions.

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• Understanding an unfamiliar codebase
• Tracing function callers and dependencies
• Assessing the impact of code changes

Investigate why a bug, performance change, or experimental result occurred. Compare competing explanations using supporting and contradicting evidence, rank likely causes, and identify the next check that would best resolve uncertainty.

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• Investigating bugs and regressions
• Explaining latency and resource usage
• Tracing unexpected experimental results

Unwrap layers of obfuscated PowerShell malware to recover hidden commands, additional malicious code, and server addresses. Combines script structure analysis with execution tracing and decoding tools to support incident investigation and detection work.

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• Investigating an obfuscated PowerShell script
• Recovering hidden commands and additional malicious code
• Extracting suspicious URLs, IP addresses, and file paths

Investigate ransomware payment addresses using public blockchain records. Trace transfers, group addresses likely controlled by the same entity, identify links to exchanges or mixing services, and organize findings into a report for incident responders and investigators.

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• Investigating a payment address found in a ransom note
• Tracing funds after a ransom payment
• Comparing related wallets across ransomware incidents

Examine Outlook PST and OST archives to extract messages, headers, attachments, and recoverable deleted items. Use the extracted records to trace email routing, reconstruct communication patterns, and support incident investigations or legal evidence review.

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• Investigating suspicious emails in Outlook archives
• Tracing message routes through email headers
• Extracting attachments for further analysis

Audit Kubernetes access rules to find excessive permissions and service account token abuse paths. Trace which users or workloads could gain cluster administrator access, then produce prioritized findings and recommendations for reducing permissions.

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• Reviewing Kubernetes privilege escalation paths
• Checking permissions after an RBAC change
• Tracing workloads with overprivileged service accounts