「ppo」の検索結果

Eliciting and documenting a sales process before it is built in Salesforce: stage sequencing, entry/exit criteria per stage, win/loss analysis requirements, and stage transition rules. Trigger keywords: sales process design, stage discovery, entry criteria, exit criteria, stage gate, win/loss categorisation, sales methodology, pipeline stages, opportunity stage mapping, stage ladder, probability ladder, forecast category mapping, required fields per stage, stage swim lane, discovery workshop, handoff brief. NOT for configuring OpportunityStage, Sales Processes or Path in Setup — use admin/opportunity-management. NOT for cross-functional swim-lane maps — use admin/process-flow-as-is-to-be.

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。

Set up, configure or troubleshoot Salesforce Collaborative Forecasts: forecast types, forecast categories, rollup methods, quota management, forecast hierarchy, manager adjustments, pipeline inspection integration. Trigger keywords: forecast type, forecast category, cumulative rollup, individual rollup, quota, forecast adjustment, forecast hierarchy, opportunity splits forecasting, pipeline forecast. NOT for configuring the opportunity stages, sales processes or split types themselves — use admin/opportunity-management. NOT for territory-based forecasting setup — use admin/enterprise-territory-management. NOT for Classic/Customizable Forecasting.

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。

Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.

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。

Build SaaS customer support — tiered support models, SLAs, macros, escalation, and support-driven product feedback.

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 a support investigation as a suggest-and-interpret loop, never executing anything yourself — orient to the product's support pack and follow the signal with judgment. Use when someone asks for help triaging, investigating, or resolving an operational or support issue on a deployment. Not for feature-development cards (see investigate-and-fix and implement-this).

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。

Maintain a support docs pack — dedup, length budgets, and no splintering — and land changes as a reviewed pull request. Use when a support thread, or a hand-off from Support assist, surfaces a new resolution, a correction to an existing one, or a deployment quirk worth recording. Not for ordinary code changes.

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。

Configures the JetFormBuilder action events system — the named events (DEFAULT.PROCESS, DEFAULT.REQUIRED, GATEWAY.SUCCESS, GATEWAY.FAILED, BAD.REQUEST, ON.DYNAMIC_STATE, never) that decide WHEN a Form Action runs. Covers declaring supported / unsupported / required events on a custom action, the validation precedence inside Base_Event::is_valid_action(), the multi-event subscription model the action editor exposes, and how to register a brand-new event type via the 'jet-form-builder/event-types' filter using Base_Event, Base_Action_Event, or Base_Gateway_Event. Use when an action needs to run only on payment callbacks, only on validation failure, only on a specific conditional state, after another action's hidden flow, or when the plugin needs to define its own event class (e.g. for inbound webhooks). Triggers on mentions of "JFB events", "action events", "GATEWAY.SUCCESS", "DEFAULT.PROCESS", "supported_events", "unsupported_events", "get_required_events", "Base_Event", "Base_Gatewa...

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。

Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.

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。

電子カルテに組み込む臨床判断支援を設計し、薬の相互作用や用量の検証、臨床スコアの計算、警告の重要度に応じた画面制御とテストを実装するスキル。

• 薬の相互作用とアレルギー確認の実装
• 患者情報に応じた用量検証
• NEWS2などの臨床スコアの実装

Prioritize support requests, identify owners, route internally, and prepare safe customer drafts

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。

Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Supports aggregate or synthetic research documentation and traceability, excluding patient care and live clinical 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。

Authoring playbook for building agents that triage and reply to customer messages — support tickets, email inquiries, chat questions, refund requests, or product issues. Use this when the user wants an agent that handles inbound customer questions, drafts replies, escalates hard cases, summarizes tickets, or follows a support playbook.

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。

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

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。

Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to 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。

Onchain OS onboarding & guide hub — the single entry for first-time, 'what is this / how do I use it', OKX.AI, and customer-support intents; classifies the intent and routes to the right sub-flow via its Intent Routing table. Covers: (1) Onchain OS onboarding + welcome banner — 'what is onchainos', 'what is onchain os', 'what can it do', 'what can onchainos do', 'what does onchainos do', 'how do I use this', 'how do I play', 'how to use onchainos', 'how to play onchainos', 'how does onchainos work', 'how do I start', 'getting started', 'tutorial', 'onboarding', 'first time', 'I just installed', 'now what', 'what do I do now', 'where do I start', 'who are you', 'what are you', 'introduce onchainos', 'tell me about onchainos', 'I'm new'; (2) OKX.AI intro & role-registration routing (the Agent economic system — roles User / ASP / Evaluator) — 'what is OKX.AI', 'OKX.AI 是什么', 'how to use OKX.AI', 'OKX.AI 快速开始', and any spelling / spacing / casing / typo variant (OKXAI, okx ai, okx-ai, lowercase okx.ai, 啥是okxai); (3) customer support / Help Center — 'contact support', 'talk to a human', 'customer service', 'file a complaint', 'give feedback', 'report a bug / system error', 'help center', 'FAQ', 'user guide', 'something is broken'. NOT for: direct on-chain actions (swap / wallet / balance / token) or Agent task lifecycle (publish / accept / deliver / dispute) — those have their own skills.

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。

Connect fundamentals, sentiment and risk to supporting and opposing evidence. Use when the user asks to stock opportunities with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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。

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

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 is doing hands-on NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — those belong to other skills.

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。

Synthesize qualitative and quantitative user research into structured insights and opportunity areas. Use when analyzing interview notes, survey responses, support tickets, or behavioral data to identify themes, build personas, or prioritize opportunities.

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。

Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

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。

Collect Apify debug evidence for support tickets and troubleshooting. Use when an Actor run has failed, is stuck, or produced empty output and you need to gather run metadata, logs, dataset samples, and environment info before opening a support ticket. Trigger with "apify debug", "apify support bundle", "collect apify logs", "apify diagnostic", "apify run failed why".

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。

Collect Apollo.io debug evidence for support. Use when preparing support tickets, documenting issues, or gathering diagnostic information for Apollo problems. Trigger with phrases like "apollo debug", "apollo support bundle", "collect apollo diagnostics", "apollo troubleshooting info".

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。

Collect Anthropic Claude API debug evidence for support and troubleshooting. Use when encountering persistent API issues, preparing support tickets, or collecting diagnostic information including request IDs and rate limit headers. Trigger with phrases like "anthropic debug", "claude debug bundle", "collect anthropic logs", "anthropic diagnostic", "claude support ticket".

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。

Collect Anima SDK debug evidence for support tickets and troubleshooting. Use when filing Anima support requests, debugging code generation issues, or collecting diagnostic data for the Anima team. Trigger with: "anima debug bundle", "anima support ticket", "anima diagnostics".

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。