「drivers」の検索結果

"Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.".

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。

"Apply DuPont Analysis to decompose Return on Equity (ROE) into profitability, efficiency, and leverage components. Use this skill when the user needs to diagnose why ROE is high or low, compare financial performance drivers across companies, or identify which operational lever to pull — even if they say 'why is our ROE declining' or 'how do we improve returns'.".

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 employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.".

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 budget-versus-actual or forecast-versus-actual performance and turn finance data into a management-ready narrative using variance logic, drivers, cash impact, and forward actions. Use when finance, FP&A, or business leaders need commentary, management accounts, or performance explanation. Do not use for bookkeeping entries, tax filing preparation, or legal accounting sign-off.

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 structured 3-scenario financial models (Base / Bull / Bear) for business planning or investment decisions. Sensitize revenue and cost drivers across scenarios, produce P&L summaries, cash flow bridges, and executive narratives. Aligns with FP&A 2.0 and CIMA Management Accounting standards. Use when leadership needs to understand the financial impact range of a decision. Do not use for statutory financial reporting or audit-ready financials.

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 flux commentary for P&L and balance-sheet lines over threshold, comparing current period against prior period and budget with drivers explained from underlying activity. Use for month-end close packages and management 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。

Analyzes malicious and vulnerable Windows kernel drivers (.sys) by parsing the PE for the native subsystem, identifying DriverEntry/IRP dispatch and IOCTL handlers, and flagging BYOVD and kernel-callback abuse. Activates for requests to analyze a Windows driver, examine a .sys sample, or assess a BYOVD/kernel driver threat.

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。

Audit laboratory automation systems -- LIMS architecture, instrument connectivity (SiLA 2, OPC-UA, serial drivers), sample tracking and chain of custody, protocol workflow engines, data acquisition pipelines, and regulatory compliance (21 CFR Part 11 electronic records/signatures.

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。

Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Auto-activate for sqlspec, SQLSpec, SQLFileLoader, drivers, query builders, named SQL, filters, pagination, Arrow, framework extensions, ADK stores, or observers. Not for ORM repositories — use advanced-alchemy.

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。

Research historical news events that moved (or are moving) an asset's price and plot them as data points directly on the chart. Click a marker in the Historic News tab to zoom the chart to that event and read the article. Useful for identifying the fundamental drivers behind price action — earnings, regulation, product launches, macro shocks, geopolitical events.

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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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 Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems.

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。

Baut und betreibt ein anbieterneutrales Self-Care-Kernset für geplante LLM-Aufgaben und Desktop-App-Automationen. Ergänzt deterministisches Policy-Linting, Lock- und Modell-Governance, semantische Prompt-Prüfung, konsistente Mehrflächen-Updates, Mini-Checks und Regeldatei-Hygiene. Nutzen, wenn ein Agent seinen nativen Scheduler entdecken oder eine bestehende Automations-Flotte mit Rollback, Readback und Löschschutz kontinuierlich verbessern soll. Löst aus bei automation self-care, Scheduler-Task-Pflege, Desktop-App-Automations-Wartung, Automations-Flotten-Audit, selbstheilende Zeitpläne, core-set-textautomations, basic-text-automations, textbased-automation-core, textbased-automation-drivers, textbased-governance-automations oder textbased-desktopapp-automations.

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。

Reads a cash forecast (spreadsheet, model export, pasted table or the narrative that accompanies it) and returns a DRAFT assumptions sheet: every assumption the forecast rests on, stated or implied by the figures, with its value as read, its location in the model, the cash lines it drives, its stated source or "not stated", the evidence that would test it, an owner, and one neutral challenge question, so a reviewer can work through the assumptions one by one. Use when the user asks to "list the assumptions behind this cash forecast", "what is this cash flow forecast assuming", "prepare the challenge questions for the treasury review", "pull out the drivers of the 13-week cash forecast" or "build an assumptions register for the liquidity plan". Do not use for actuals against budget by line, use budget-variance-explainer instead; do not use for the case behind a capital request, use capex-request-pack instead. Drafts for human review; never approves, authorises or signs off.

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。

Drafts an architecture or design decision record (ADR) from the discussion notes, design review minutes, chat threads and diagram descriptions the user provides: context, decision drivers, options considered with arguments as stated, the decision as the group worded it, consequences and a review date, with every factual claim tagged evidenced, asserted or UNKNOWN so unsupported claims are marked for the author to substantiate. Use when the user asks to "write an ADR", "document the architecture decision", "record why we chose this design", "turn these design review notes into a decision record" or "draft the design decision log entry". Do not use for a business or governance decision with no technical design, use decision-memo-builder instead; for the change ticket that implements the decision, use change-request-pack. Drafts for human review; never approves, authorises or signs off.

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。

Comprehensive advanced Git patterns guide covering interactive rebase, cherry-pick, bisect for bug hunting, worktrees, reflog for recovery, stash management, submodules vs subtrees, hooks (pre-commit, commit-msg, pre-push), conventional commits, merge strategies, branch naming, git attributes (LFS, diff drivers), blame/log archaeology, patch workflow, sparse checkout, filter-repo, signing commits, and performance optimizations. Use when performing complex Git operations beyond basic add/commit/push.

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 Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems.

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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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。

Extract a thesis's dependency fingerprint — the external variables its bull case actually rests on (key customers, suppliers/inputs, technology transitions, macro drivers, single points of failure). Use when user says "dependency map", "what does [TICKER] depend on", "dependency fingerprint", or "map exposures for [TICKER]".

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。

Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, 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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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 how to recruit CDL drivers — sourcing channels (CDL schools, job boards, military, returning citizens, owner-operator pools), application + screening process, signing bonus structures, recruitment marketing, fleet ambassador programs, and what's working in the current driver market. Reference ATA Workforce Development.

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。