「models」の検索結果

"Build three-statement financial models (Income Statement, Balance Sheet, Cash Flow) with revenue forecasting, assumption management, and scenario analysis. Use this skill when the user needs to project financials, build a fundraising model, create financial projections for a business plan, or evaluate M&A targets — even if they say 'build a financial model', 'project our revenue', 'how much money will we make next year', or 'model this acquisition'.".

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 Discounted Cash Flow (DCF) valuation models to estimate intrinsic value. Use this skill when the user needs to value a company, evaluate an investment, estimate fair share price, or build financial projections — even if they say 'what is this company worth', 'should we acquire them', or 'build me a valuation 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 and design pricing strategies including cost-plus, value-based, competitive, penetration, and skimming approaches with psychological pricing techniques. Use this skill when the user needs to set or change prices, evaluate pricing models, understand price elasticity, or apply psychological pricing — even if they say 'how much should we charge', 'are we priced right', or 'our margins are too low'.".

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。

"Model viral spread dynamics using SIR/SIS/SEIR compartmental models. Use this skill when the user needs to predict content spread patterns, estimate viral thresholds, or model information cascades in social networks — even if they say 'will this go viral', 'epidemic model for content', or 'spread prediction'.".

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。

"Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.".

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 credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval 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。

"Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series 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。

Evaluate LLM agents and tool-using workflows—task success, tool accuracy, latency/cost, safety, and regression suites. Use when shipping agent features, comparing prompts/models, or debugging agent 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。

"Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA 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。

"Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.".

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 exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.".

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。

Design data models with Pydantic schemas, comprehensive validation rules, type annotations, and relationship mappings.

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 a durable feature spec before touching code. Reads the codebase map, reads related models/migrations to ask technical questions (not generic ones), then produces a structured spec doc in docs/specs/. Designed to pipeline with /implementation --issue N. Use before any feature implementation.

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。

Design component architecture, REST API contracts, and data models (Pydantic schemas) before implementation. Use when planning how to build a feature, defining endpoints, or modeling data. Trigger on "design the architecture", "design the API", "data model", "how should I structure this feature".

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。

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill 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。

Build customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes.

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。

Use when loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.

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 before repeatedly placing GLB models in PlayCanvas to record each asset's uniform scale, grounding offset, pivot compensation, and yaw once.

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 composing PlayCanvas entity hierarchies from models, colliders, rigid bodies, and scripts, so each object has one semantic root and clear lifecycle.

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 before loading, placing, scaling, or animating GLB models in PlayCanvas to measure bounds, grounding, clips, joints, and hierarchy with the bundled offline inspector.

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。

AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models. Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.

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。

Test Model Context Protocol (MCP) servers and tool-calling systems for security vulnerabilities including tool injection, parameter manipulation, privilege escalation, and data exfiltration through AI agent tool interfaces. Use this skill when assessing MCP server implementations, AI agent tool integrations, or any system that exposes tools to language models. Covers tool confusion attacks, cross-tool exploitation, and MCP server hardening assessment.

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。