「operator」の検索結果

Convert Kai workflow data, CSV exports, audit folders, SDR package outputs, and marketing reports into dashboard-ready specs or lightweight static dashboards. Use when "data dashboard", "operator dashboard", "operator room", "HTML operators room", "sales dashboard", "SDR dashboard", "turn this data into a dashboard", "dashboard handoff", "visualize Kai data", or any request to package sourced marketing, sales, audit, or SDR data for a dashboard or presentation surface.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze Ascend NPU schedule, operator dispatch, operator launch, and Host Bound profiling issues in Ascend profiling data. Use when need to diagnose device Free time, framework/operator dispatch latency, launch latency, PYTORCH_API/CANN_API launch gaps, aclrtSynchronizeStream stalls, task queue behavior, CPU scheduling interference, GC/lock pauses, CPU affinity, or schedule-side optimization actions.

日本語の概要は準備中です。原文の説明を表示しています。

Compile, profile, diagnose, optimize, and compare Ascend NPU operators across CANN versions and A2/A3/A5 for Ascend C, CATLASS, Triton-Ascend, TileLang-Ascend, PyPTO, and SHMEM/MC2. Use when an operator already has a runnable implementation and the user asks for msOpProf collection, bottleneck analysis, source-level tuning, or a reproducible before/after performance report. Do not use as the primary workflow for operator creation, migration, or unresolved correctness failures.

日本語の概要は準備中です。原文の説明を表示しています。

Architectures and techniques for neural operators on discontinuous PDE solutions (shocks, contact discontinuities, steep gradients). Covers local-global spectral design (ShockFNO), reflection padding for non-periodic BCs, resolution scaling for shock width, and frequency-band error diagnostics. Use for low-viscosity Burgers, compressible Euler, Riemann problems, or any PDE where standard FNO produces Gibbs oscillations.

日本語の概要は準備中です。原文の説明を表示しています。

Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.

日本語の概要は準備中です。原文の説明を表示しています。

Learn and apply how the operator works — operator preference, working style, communication preference, "remember my preference", "I prefer", "don't do that again", "that's not how I work". Read at session start, updated at session end.

日本語の概要は準備中です。原文の説明を表示しています。

運用者が設定したTeamサーバーの更新を、指定担当のNight Watchに引き継ぎ、既存の更新機構による検証・復旧・完了確認まで進めるスキル。

• Teamを公式mainへ更新したいとき
• 失敗・延期した更新の続行調整
• データベース移行と復旧の調整

サービスの健全性や遅延、処理量、リソース不足を把握するために、運用上の疑問から指標を選び、GrafanaやSigNoz向けの監視ダッシュボードを作ります。

• Kafkaの監視画面を作りたいとき
• Elasticsearchの状態や性能の監視
• API gatewayの遅延やエラーの監視

Retrieve tracked military aircraft positions (OpenSky + Wingbits) with callsign, type, operator, altitude, and activity clusters, filterable by bounding box, operator, and aircraft type. Use when the user asks about military air activity in a region.

日本語の概要は準備中です。原文の説明を表示しています。

Write, calibrate, and debug the prefill-vs-decode logprob (KL) consistency tests in sglang -- the two independent conditions a zero requires (every operator batch-invariant, and the two paths computing the same function), which helper separates them, how to pick a threshold once they hold, and how to localize a divergence to a single operator. Use when adding a KL test to a model, picking or defending a kl_div threshold, or investigating a KL number that is too high.

日本語の概要は準備中です。原文の説明を表示しています。

Walk the operator through wiring the first NanoClaw agent to a DM channel — resolve the operator's channel identity, select or create the agent, and trigger a welcome DM via the normal delivery path. Use after channel credentials are configured and the service is running.

日本語の概要は準備中です。原文の説明を表示しています。

Post-call handling for a voice session — turn the transcript into a brain page, post the summary to the operator's messaging surface, archive the audio. The pipeline is the contract; the firing paths are operator-wired (see "Two firing paths" below for what ships today).

日本語の概要は準備中です。原文の説明を表示しています。

Anti-forensics and evidence destruction techniques for red team operators conducting authorized engagements. Covers log clearing on Windows (wevtutil, Clear-EventLog, ETW provider patching) and Linux (journal truncation, utmp/wtmp binary editing, syslog manipulation), timestamp manipulation via Timestomp and SetMACE to defeat timeline analysis, filesystem-level anti-forensics including NTFS Alternate Data Streams for payload hiding and secure deletion with sdelete/shred, memory artifact removal to counter live forensics, disk artifact manipulation targeting MFT entries and USN journal records, network forensics evasion through encrypted C2 channels and DNS-over-HTTPS tunneling, and anti-VM/sandbox detection to avoid dynamic analysis environments. Tools: Timestomp, wevtutil, sdelete, shred, MimiPenguin, Invoke-Phant0m. Aligns to MITRE ATT&CK T1070 (Indicator Removal), T1027 (Obfuscated Files or Information), T1497 (Virtualization/Sandbox Evasion). Each technique includes the forensic artifact it targets, the destruction or manipulation method, and the defender perspective so operators understand detection gaps they must account for.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

日本語の概要は準備中です。原文の説明を表示しています。

Problem-solving strategies for operator theory in functional analysis

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Dictated voice note about project work or status: terse voice and mobile-style requests - turn short spoken-style asks into clarify, plan, status, handoff, or confirmation actions. Use when the user says: voice-operator, voice operator, voice-first, voice command, mobile command, short command, dictated command, dictated request.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Shell command or test-suite execution: policy overlay for terminal commands - add cwd, environment, safety, and result-evidence gates after preferring native shell tools for ordinary CLI, package-manager, and test runs. Use when the user says: command-operator, command operator, terminal command, terminal task, shell command, shell task, cli command, command execution.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Audio, video, or screenshot to process: user-sent media - audio, video, YouTube links, screenshots, receipts, OCR, meeting recordings, transcripts, timestamps, and clip summaries, gated for source, permission, and hallucination risk. Use when the user says: media-input-operator, media input operator, media input, audio transcription, audio transcript, transcribe audio, transcribe this audio, meeting recording.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Live rate, score, or local time a work task needs: policy overlay for live lookups - add provider, freshness, units, and source-quality gates after preferring native live-data tools for ordinary weather, finance, sports, maps, and time-zone requests. Use when the user says: live-info-operator, live info operator, live information, real time information, real-time information, weather today, current weather, weather forecast.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Email, Slack, or Jira action to perform: external app actions - email, Slack, Discord, Notion, Linear, Jira, CRM, and similar providers, scoped with auth, payload, confirmation, and result-evidence gates. Use when the user says: connector-operator, connector operator, external app action, external connector action, saas action, api action, send email, email customer.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Browser login, click, or form task: policy overlay for browser tasks - add auth, confirmation, and observed-trace gates after preferring the native browser for ordinary URL, click, login, and form actions. Use when the user says: browser-operator, browser operator, browser task, browser operation, browser automation, browser session, webpage operation, web page operation.

日本語の概要は準備中です。原文の説明を表示しています。

Provides verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Also provides structured workflows for IAM role management and baseline policy generation from application source code or a Terraform plan JSON. Covers condition operator safety (ForAnyValue/ForAllValues with Null checks), bucket policy deny patterns (VPC endpoint restrictions, org paths), confused deputy protection, and service role creation for AWS services (Glue, CloudTrail, Lambda, ECS, etc.) with aws:SourceAccount/aws:SourceArn trust conditions. Applies when creating IAM roles, writing IAM or bucket policies, generating policies from application source code or a Terraform plan JSON, working with STS, Organizations, or condition operators, or any task needing a service or execution role. Does not cover non-IAM authorization like Cognito user-pool policies or app-level RBAC.

日本語の概要は準備中です。原文の説明を表示しています。