'traces' 검색 결과

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.

Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

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Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

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Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).

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Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.

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.

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Maakt regelrecht-scenario's vindbaar en hun norm-ketens leesbaar/geassert. Aan de invoerkant — vertaal een platte lijst leaf-parameters naar benoemde casus-assen + een herbruikbare persona-bibliotheek, met een reverse-lookup casus-matrix. Aan de uitvoerkant — maak de norm-keten zichtbaar via keten-checkpoints (assert elke knoop op het kritieke pad, niet alleen de endpoint) en golden-trace-snapshots + branch-coverage. Gebruik dit bij het schrijven of opschonen van scenario-/feature-files, het terugvinden of ontwerpen van een specifieke casus in een grote variabelenlijst, het reviewen van engine-traces, of het regressie-bestendig maken van cross-law-ketens zodat een ketenfout niet stil achter een correcte endpoint verdwijnt. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Haakt aan op regelrecht-stelselanalyse (engine-tests / meta-check) en regelrecht-audit-products (testcase-scenario's).

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Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

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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.

Diagnose, fix, or validate SwiftUI animation and scroll behavior by temporarily instrumenting views with MotionEyes, capturing console traces over time, and comparing motion data to user intent or test expectations. Use when users report bugs such as wrong direction, premature fade, timing or easing mismatch, unexpected movement, missing movement, incorrect relative movement between views, scroll jumps, scroll restoration drift, content-offset desynchronization, or when they want to turn MotionEyes traces into focused assertions. Prefer XcodeBuildMCP log capture first and fallback to CLI log streaming; remove only agent-added MotionEyes instrumentation after validation.

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Ergogen (エルゴノミック自作キーボード設計ツール) リファレンス。 YAML / JSON 設定で points, outlines, cases, pcbs を宣言し SVG / DXF / JSCAD / KiCad を出力。 Anchors, Zones, Adjustments, Preprocessing, Footprints, Traces。 キーレイアウト、ケース設計、PCB 生成。

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Design a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).

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.

Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.

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Recommend, create, preview, and attach Coval trace-based metrics from OpenTelemetry spans. Use when a user has Coval traces and wants custom trace metrics, trace-aware LLM judge metrics with include_traces, latency, token usage, provider failures, tool behavior, vertical-specific workflow signals, or production monitoring based on trace attributes.

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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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Configure an AI agent to send OpenTelemetry traces to Coval. Use when a user wants to add Coval tracing, instrument an agent for simulations or conversation monitoring, make traces show up in Coval, handle SIP/PSTN/WebSocket trace correlation, or replace the one-command wizard with a security-reviewable manual setup.

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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.

INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.

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OpenClaw의 품질 검증 시나리오를 모의 환경이나 실제 서비스에서 실행하고, 실패 원인 수정과 보고서 확인, 여러 모델의 응답 스타일 비교를 지원합니다.

• 모의 환경이나 실제 서비스에서 품질 검증
• 실패 시나리오 원인 조사와 재검증
• 여러 모델의 캐릭터 표현 비교

저장소에서 외부 요청이나 사용자 입력으로 악용할 수 있는 취약점을 찾습니다. 입력 경로와 재현 절차, 영향을 확인해 버그 바운티 보고서 작성을 지원합니다.

• 외부 요청으로 도달 가능한 취약점 조사
• 정적 분석 결과 검토
• 최소 재현 절차와 영향 정리

Playwright로 브라우저 조작을 재현하는 테스트를 만들고 화면별 작업 정리와 자동 실행을 지원합니다. 간헐적 실패를 조사하고 이미지, 영상, 실행 기록을 저장합니다.

• 로그인과 검색 동작을 확인할 때
• 화면별 테스트 작업을 정리할 때
• 간헐적으로 실패하는 테스트를 조사할 때

상시 실행되는 AI 에이전트의 시작·중지, 로그 모니터링, 권한 관리, 단계적 업데이트와 롤백 절차를 정리합니다. 장애 대응과 성공률·비용 평가를 지원합니다.

• 상시 실행 에이전트의 운영 설계하기
• 실패가 급증할 때 장애 대응하기
• 업데이트와 롤백 절차 마련하기

운영 환경의 동작과 장애 원인을 추적하도록 로그, 지표, 서비스 간 처리 경로와 알림을 설계합니다. 실제 출력과 알림까지 검증합니다.

• 운영 기능의 모니터링 정보 구성
• 서비스 간 로그 연결
• 알림과 대응 절차 검토