'trace' 검색 결과

对 profiling 数据（kernel_details.csv / trace_view.json / step_trace_time.csv）做统一分析： 5 层递进管道（analyze_trace.py）+ baseline/优化后 kernel diff（compare_traces.py），输出 瓶颈诊断、算子执行序、可融合候选与 P0-P2 方向建议。输入统一来自 profiling-collect。 只要用户有 profiling 产出并问"为什么慢/瓶颈在哪/优化前后差多少/往哪个方向"，都用本技能；采集走 profiling-collect，方案选档走 dit-perf-opt，实现/并行/基准走 pattern-dev/dit-parallel-opt/benchmark-dev——本技能只做诊断与方向。 由 model-auto-optimization 的 S1（融合分析）/S3（并行）阶段调用，亦由 dev-workflow 的分析阶段指引加载。 融合机会候选只做**识别**；候选的**边界与收益判定**（能不能融成一个单元、融多大、值不值）交 `fusion-scope-analyze`（本技能的辅助技能）——本技能不判融合边界。

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Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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Use when working with Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.

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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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Compile your local stellar-build usage traces into sharper skills. Use when the user says "optimize my skills", "optimize-skills", "improve my skills from usage", "learn from my traces", "stellar-loop optimize", or wants the learning loop to refine installed skills based on how they've actually been used. DSPy-style prompt optimization over the local trace store at ~/.stellar-build/traces — fully local, reversible.

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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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Caveman Cloud의 비용, 응답 시간, 오류를 읽기 전용으로 조사합니다. 집계와 개별 실행 기록을 대조해 지출 내역과 개선 여지를 근거와 함께 정리합니다.

• LLM 비용을 높이는 처리 파악
• 응답 지연과 오류 조사
• 실측 비용과 검증된 절감액 정리

이름이 있는 브라우저 세션을 열고 초기 화면과 각 작업의 이력을 저장합니다. 사이트 탐색 과정을 남겨 후속 정보 추출, 양식 입력, 테스트에 사용할 세션을 준비합니다.

• 작업 이력을 남기며 사이트 탐색
• 초기 화면과 페이지 구조 저장
• 정보 추출과 양식 입력 준비

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Review papers, drafts, and research ideas with evidence-linked feedback focused on improvement. Audit low-stakes assessment rubrics, trace supporting evidence, and check rater agreement and uncertainty without ranking people or recommending consequential decisions.

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• Prepare evidence-linked feedback on a paper draft
• Review strengths and limitations of a research idea
• Check assessment criteria and evidence traceability

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

Trace how requests become rendered pages in a CTF sandbox. Compare display restrictions with server enforcement, inspect data passed to the browser, and preserve a minimal example that demonstrates where rendering behavior diverges.

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• Trace routes through server rendering
• Inspect page data passed to the browser
• Compare display restrictions with handler enforcement

Trace SSH keys, tokens, and other credentials in Linux CTF environments. Connect each recovered artifact to the service that accepts it, then document the resulting privileges or movement to another host.

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• Trace where recovered SSH keys and tokens are accepted
• Document privilege gains through sockets or sudo
• Explain movement between Linux challenge hosts

Investigate Windows secrets protected by DPAPI, a system for encrypting sensitive data, in a CTF sandbox. Trace the required keys and account context through decryption, then verify which service accepts the recovered secret.

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• Inspect DPAPI-protected data and masterkeys
• Trace credentials stored by browsers or Windows Vault
• Identify the account or machine context needed for decryption

Trace OAuth and OIDC sign-in flows in a CTF sandbox, from redirects through token exchange to application acceptance. Show where tokens become a recognized identity or permission, and preserve the smallest reproducible flow.

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• Trace sign-in redirects and callback parameters
• Inspect state, nonce, and PKCE validation
• Check how tokens become application identity or permissions

Trace queued jobs from submission to their eventual effects in a CTF sandbox. Compare request-time and worker-time settings, follow retries and failed-message handling, and document the smallest sequence that explains a worker-specific result.

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• Trace a queued payload to its eventual downstream effect
• Compare request-time settings with worker runtime state
• Investigate retries and dead-letter queue handling

Analyze running iOS apps in a CTF sandbox, a controlled challenge environment. Trace methods, inspect secure storage, and examine request signing and certificate checks to identify the state needed to reproduce an accepted request.

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• Trace Objective-C or Swift methods
• Inspect Keychain and local app storage
• Examine certificate checks and request signing

Trace movement between Windows hosts in a CTF sandbox using credentials and Kerberos tickets, which carry authentication information. Connect recovered artifacts to the service that accepted them and document the resulting access or privileges.

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• Trace movement between Windows hosts
• Identify services that accept recovered credentials
• Connect Kerberos tickets to resulting access

Trace uploaded files through extraction, conversion, parsing, and reconstruction of saved objects in a CTF sandbox, an isolated competition environment. Preserve original and derived files, distinguish visible validation from backend behavior, and document the smallest reproducible processing chain.

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• Trace an uploaded file to its backend effect
• Investigate archive extraction and preview generation
• Compare visible validation with backend file handling

Inspect firmware in a CTF sandbox, map its partitions, and trace startup and update steps. Connect extracted files, credentials, and configuration to device behavior, preserving evidence for a minimal reproducible path.

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• Map firmware partitions and extracted filesystems
• Trace startup and update verification steps
• Identify where embedded credentials are used

Investigate memory dumps, disk records, and captured network traffic while preserving evidence. The skill guides timeline reconstruction and examination of system traces to identify suspicious activity and produce findings that others can review.

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• Preserve evidence before starting an incident investigation
• Inspect processes and connections in memory dumps
• Build timelines from event logs and execution traces

Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

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• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Reconstruct Windows folder browsing activity from Shellbag registry records using SBECmd and ShellBags Explorer. Investigate traces of deleted directories, disconnected USB drives, and network shares while accounting for limits in what the records and timestamps show.

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• Reconstructing a user's folder browsing history
• Investigating browsing traces on disconnected USB drives
• Checking access traces for deleted folders