'trace' 검색 결과

Trace mailbox abuse in a CTF sandbox, a controlled security challenge environment. The skill connects OAuth permissions, forwarding rules, delegated access, and message logs to concrete mail effects, documenting the smallest reproducible sequence and supporting evidence.

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• Trace how OAuth consent enables mailbox access
• Investigate forwarding and inbox rule effects
• Verify shared mailbox and delegated access

Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.

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• Investigating slow requests across services
• Understanding service dependencies
• Tracing how errors spread

Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.

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• Share a trace with teammates
• Open an Arize view from IDs in logs
• Link directly to a dataset or evaluator

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

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• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Record operations across multiple AI agents and connect events into a traceable history. Search records by tracking ID, time window, or responsible component to support audits and investigations, with local storage available when the vector database fails.

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• Trace task dispatch and execution across agents
• Search audit records by time or responsible component
• Keep recording events during vector database failures

Inspect Claude Code execution traces in Braintrust, including tool calls and token usage. The guide explains event hooks, troubleshooting missing records, and matching separate parent and sub-agent traces using timestamps and metadata.

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• Investigating missing execution traces
• Reviewing tool calls and token usage
• Matching parent and sub-agent execution records

Use when asked 质量追溯体系怎么建, 产品追溯方案, 批次追溯, 一物一码, 客户要求可追溯性, 召回演练, 正向追溯和反向追溯, or plan product and batch traceability in a Chinese factory. Produces a traceability plan in Chinese: the trace unit and coding rule, the data to capture at each process step (material lots, work orders, equipment, people, inspection results), forward and backward trace paths, record retention, a mock recall drill with a target time, gaps against the current system, and a phased rollout, with industry rules (food, medical devices, automotive) marked to confirm.

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Use when you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.

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Evolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.

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Grafana Tempo provider for obz. Covers trace search, trace retrieval by ID, and extension commands for tag discovery. This skill should be used when the user mentions "Tempo", "Grafana Tempo", "TraceQL", "obz trace -p tempo", or needs to search traces from a Tempo backend.

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Agent trace 诊断器：当用户提供 trace、日志 JSON、agent 执行记录、工具调用序列，或说“看下这个日志/trace，定位根因”“只说明问题不要改文件”时使用；用中文输出核心根因、可视化执行流程图、trace 证据链、可能文件/行和修复建议，默认只读。不用于没有日志证据的新功能设计、普通代码解释或直接修复代码。

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ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.

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Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not AI observability traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.

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Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.

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Troubleshoot Coval OpenTelemetry trace ingestion, missing trace UI, sparse traces, bad simulation or conversation correlation, auth/org errors, oversized payloads, duplicate spans, and production debugging with Trace Search.

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Honeycomb의 처리 기록으로 Paperclip 실행, 화면 표시, CI의 지연을 조사합니다. 오래 걸리는 구간을 정리하고 계측 설정과 누락된 계측 추가도 다룹니다.

• 에이전트 실행 전후의 지연 조사하기
• 작업 페이지 표시 지연 조사하기
• 전체 CI를 지연시키는 작업 찾기

작업에 기록된 실행 구간을 모아 에이전트 사이의 처리 흐름을 트리로 정리합니다. 소요 시간과 대기 시간, 오류 및 처리가 느린 지점을 확인할 수 있습니다.

• 느린 작업의 원인 조사
• 에이전트 사이의 협업 확인
• 실행 기록의 오류 위치 확인

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

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• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task

Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.

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• Recall past preferences and decisions
• Find exact commands in archived conversations
• Import and search documents or Agent Skills

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

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

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• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Run smoke tests, or basic operational checks, for Mastra projects locally or in staging and production. Verify Studio screens, agents, tools, workflows, traces, and memory, then report results, blockers, and skipped checks.

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• Checking a local Mastra project before release
• Testing Studio and server deployments in the cloud
• Running targeted checks for agents or traces

Investigate errors, crashes, and unexpected behavior by reproducing failures, tracing execution, and comparing logs. Tests one explanation at a time, then provides a verified fix, supporting evidence, and tests or safeguards to prevent recurrence.

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• Investigate error messages and execution traces
• Find the cause of an application crash
• Compare logs to locate a failure