'raci' 검색 결과

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

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• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

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.

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• Set up distributed tracing in KubeSphere
• Configure automatic application instrumentation
• Query traces and service connections through the API

Add OpenTelemetry tracing to MATLAB code. Use when the user asks to "add tracing", "instrument with spans", "add OpenTelemetry", "trace my code", "add observability" (when about tracing), or mentions "spans", "distributed tracing", or "OTel tracing" in the context of MATLAB functions. Covers span creation, parent-child context propagation, error handling, attributes, events, and semantic conventions.

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Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit

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Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

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• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup

Use this skill when building, reviewing, or refreshing a RACI (Responsible / Accountable / Consulted / Informed) matrix for a Salesforce project so that every Salesforce-specific decision — data model change, automation tier choice, security model, integration boundary, deployment, license/edition — has exactly one accountable owner and a documented escalation path that downstream agents can route to. Trigger keywords: RACI matrix salesforce project, stakeholder authority salesforce, escalation path salesforce decisions, who approves data model change, salesforce decision rights, REFUSAL_NEEDS_HUMAN_REVIEW routing. NOT for the change advisory board operating model itself (use admin/change-management-and-deployment). NOT for end-user training rollout plans (use admin/change-management-and-training). NOT for the technical mechanics of permission set assignment authority (use admin/permission-set-architecture). NOT for pure stakeholder requirements elicitation (use admin/requirements-gathering-for-sf). More trigger keywords: who approves a sandbox refresh, who signs off a data load, release update activation owner, delegated admin authority vs RACI, multi-org decision rights, escalation time-box.

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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

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

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• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session

Turn descriptions and existing notes into a standard operating procedure, a document explaining how work gets done. Capture process flow, responsibilities, detailed steps, exceptions, and metrics so teams can use it for handoffs or audits.

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• Documenting a process known only to one person
• Clarifying who performs and owns each step
• Preparing procedures for handoffs or audits

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

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• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Expert blueprint for racing games including vehicle physics (VehicleBody3D, suspension, friction), checkpoint systems (prevent shortcuts), rubber-banding AI (keep races competitive), drifting mechanics (reduce friction, boost on exit), camera feel (FOV increase with speed, motion blur), and UI (speedometer, lap timer, minimap). Use for arcade racers, kart racing, or realistic sims. Trigger keywords: racing_game, vehicle_physics, checkpoint_system, rubber_banding, drifting_mechanics, camera_feel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert blueprint for racing games including vehicle physics (VehicleBody3D, suspension, friction), checkpoint systems (prevent shortcuts), rubber-banding AI (keep races competitive), drifting mechanics (reduce friction, boost on exit), camera feel (FOV increase with speed, motion blur), and UI (speedometer, lap timer, minimap). Use for arcade racers, kart racing, or realistic sims. Trigger keywords: racing_game, vehicle_physics, checkpoint_system, rubber_banding, drifting_mechanics, camera_feel.

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Instrument applications with OpenTelemetry for distributed tracing, including auto and manual instrumentation, context propagation, sampling strategies, and integration with Jaeger or Tempo. Use when debugging latency issues in distributed systems, understanding request flow across microservices, correlating traces with logs and metrics for root cause analysis, measuring end-to-end latency, or migrating from legacy tracing systems to OpenTelemetry.

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Revisa una migración de base de datos antes de desplegarla para asegurar que sea segura — reversible, no destructiva y compatible hacia atrás — siguiendo las convenciones de base de datos del proyecto. Úsala siempre que crees, revises o apruebes una migración de esquema o de datos (p. ej. "revisa esta migración", "¿es seguro desplegar esta migración?").

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when controls have no clear owner, or "security" owns everything, and the team asks "who owns each control", "build a RACI for our controls", "assign control owners", "nobody knows who runs the access review", "set a review cadence per control" or "the auditor asked who is accountable". Assigns one accountable role and the operators to every applicable control, checks workload and segregation of duties, sets a review cadence from how each control runs, and writes a RACI table plus a one-page brief per owner role.

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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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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.

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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

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

Build and debug asynchronous Rust applications with Tokio, a runtime for scheduling work while operations wait. Apply patterns for task management, channels, error handling, cancellation, and concurrency limits when developing network services and other concurrent systems.

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• Build asynchronous network services
• Manage tasks and channel communication
• Design error handling and cancellation

Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.

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• Capture an execution trace of SGLang inference
• Check basic accuracy before profiling
• Record input processing and token generation separately

Create reusable procedures for responding to security incidents, from detection and containment to recovery. Define decision points, responsibilities, escalation rules, and workflows for security automation platforms so teams can follow a consistent response process.

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• Establishing an incident response program
• Documenting phishing or ransomware response procedures
• Designing automated security response workflows

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times