「diagnosis」の検索結果

Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.

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。

Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

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 for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.

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。

Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

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 asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery 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。

Run NV-Reason-CT inference on user-provided 3D NIfTI chest or abdominal CT volumes for engineering and research workflows. Not for diagnosis, treatment, or clinical reporting.

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。

How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.

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 a concrete ./holohub command fails, hangs, regresses, or returns wrong output and needs reproducible diagnosis and verification.

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。

Start or patch Dynamo router modes and run router endpoint smoke checks. Use for round-robin, KV-aware, least-loaded, or device-aware routing setup; use recipe-runner for recipe deployment and troubleshoot for failure diagnosis.

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。

账号诊断/起号体检：读取已完善的画像 Profile + 近期内容数据，诊断垂直度、定位清晰度、限流降权信号、流量池阶段，给出病因→证据→处方式的起号意见与发布建议。当用户说账号诊断/起号体检/为什么没流量/是不是被限流了/账号定位诊断/起号建议/怎么起号时使用

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。

Turns an answer into a one-page visual HTML explainer: the model writes a short Markdown draft and the bundled CLI builds one page. Use it proactively and liberally, without being asked, whenever a page would help the reader more than plain text, even if plain text would also work: any explanation of how something works or how parts relate (flow, request path, architecture, code or folder structure, state machine, lifecycle, history); any comparison, trade-off or decision with pros and cons; any diagnosis, review or investigation, especially with several causes or findings to rank; any answer with a table, a numbered or ranked list, steps, branches or several sections; or when the user says "I don't get it / draw it / explain visually / 讲讲原理 / 没看懂 / 画个图". When in doubt, use it: a page is quick and cheap to make. Also for explainer videos ("make a video", "做个视频") and for changing its settings. Skip only for small talk, a trivial one-line answer, or when the user asks for plain text.

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 your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: "agent not working", "wrong answer", "agent error", "tool call failing", "debug agent", "check logs", "read traces", "broken", "500 error", "424 error", "model access denied", "command not found", "stuck in DELETING", "maxVms exceeded", "cold start diagnosis", "cold start slow", "agentcore create error", "create failed", "exit code 7", "connection refused local dev". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.

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 preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: "production checklist", "harden agent", "production ready", "secure agent", "inbound auth", "going live", "cold start optimization", "session lifecycle", "StopRuntimeSession", "quota", "throttling", "maxVms", "rate limit", "security audit of outbound API calls", "gateway target audit for production", "restrict who can call", "lock down endpoint", "only our app can call". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.

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 deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK/IAM/quota error diagnosis, version management, rollback, and canary deployments. Triggers on: "deploy my agent", "agentcore deploy", "deploy failed", "CDK error", "rollback", "canary deploy", "pin version", "redeploy", "deploy stuck". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.

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。

Troubleshoots failing applications by discovering and analyzing CloudWatch log groups to identify error patterns, root causes, and actionable solutions. Use when an application is experiencing failures and log-based diagnosis is needed.

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。

Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0->5.0->8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.

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。

Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).

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。

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

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。

Authors, validates, and troubleshoots AWS CloudFormation templates. Covers template authoring with secure defaults, local validation with either cfn-lint or cloudformation-validate, cfn-guard security and compliance checks as a recommended default, account-aware CloudFormation service pre-deployment validation, CloudFormation Express mode for faster deployments, and root-cause diagnosis of failed stacks using CloudFormation events and CloudTrail correlation. Also covers author-time template intelligence with the CloudFormation Language Server and published cloudformation-validate libraries.

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。

Guided journey from a stalled, plateaued business to one with an honest diagnosis, a working operating rhythm, and offers repriced to real value. Orchestrates eight skills phase by phase - good-strategy-bad-strategy, traction-eos, high-output-management, team-topologies, drive-motivation, lean-analytics, negotiation, monetizing-innovation - asking the user questions at every decision point and recording results in the project docs/ folder (STRATEGY.md, OPERATIONS.md, METRICS.md, IMPROVE-BUSINESS-PLAN.md) so the journey resumes across sessions. Use when the user wants to fix a business that has plateaued, diagnose why growth stalled, tighten strategy and execution, re-motivate a team, or says 'revenue is flat and I do not know why'. Starting from scratch with no customers: use create-business. Once the fundamentals work and the goal is expansion: use grow-business. When the product itself drags the business down: use improve-app. For one framework in isolation, invoke that skill directly.

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。

This workflow, known as RIPER-5, is designed exclusively for handling complex software engineering tasks such as system design, architectural refactoring, bug diagnosis, performance optimization, and multi-component integration.

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 the user asks about Ni Haisha / 倪海厦 TCM course material, especially Shang Han Lun / 伤寒论, Jingui / 金匮要略, Zhongjing Xinfa / 仲景心法, clinical cases / 临床案例 / 倪师医案, Bagang Bianzheng / 八纲辨证, Fuyang Forum / 扶阳论坛, Yijinjing / 易筋经, Liang Dong dialogue / 梁冬对话倪师, Stanford lecture / 斯坦福大学演讲, Tianji / 天纪 / 易经 / 阳宅 / 紫微斗数, Huangdi Neijing / 黄帝内经, Shennong Bencao / 神农本草, acupuncture / 针灸, meridians, acupoints, acupuncture depth safety / 危险进针深度 / 气胸风险, six-channel pattern identification, symptom-to-formula routing, formula comparison, lesson review, board/PPT screenshot evidence, or course-derived study notes. This skill is for educational distillation and study support only, not medical diagnosis, prescriptions, dosage, individualized treatment, or self-administered acupuncture.

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。

Designs a translational blueprint for moving a biomedical finding toward diagnosis, prognosis, treatment response prediction, patient stratification, or therapeutic development, with explicit translational milestones, validation thresholds, and feasibility-sensitive route framing.

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。

Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.

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。