「termine」の検索結果

"Apply auction theory to compare the four canonical auction formats and assess revenue equivalence. Use this skill when the user needs to choose an auction format, evaluate bidding strategies, or determine when revenue equivalence breaks down due to risk aversion, asymmetry, or correlated values.".

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。

"Calculate Economic Order Quantity to minimize total inventory cost (ordering + holding). Use this skill when the user needs to determine optimal order size, balance ordering frequency against storage costs, or set reorder points — even if they say 'how much to order', 'optimal batch size', or 'inventory cost minimization'.".

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。

"Calculate safety stock levels to buffer against demand and lead time uncertainty. Use this skill when the user needs to set inventory buffers, determine service level trade-offs, or optimize safety stock across SKUs — even if they say 'how much buffer inventory', 'stockout prevention', or 'service level calculation'.".

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。

"Solve the newsvendor problem for single-period ordering decisions under uncertain demand. Use this skill when the user needs to determine optimal order quantity for perishable goods, seasonal products, or one-time purchase decisions — even if they say 'how much to order for this season', 'perishable inventory', or 'single-period ordering'.".

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。

"Perform break-even analysis to determine the sales volume or revenue needed to cover all costs. Use this skill when the user needs to calculate break-even point, assess margin of safety, evaluate operating leverage, or decide pricing and volume trade-offs — even if they say 'how many units do we need to sell', 'when will we be profitable', or 'what happens if we lower the price'.".

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。

"Implement PageRank algorithm to compute web page importance scores using the random surfer model. Use this skill when the user needs to rank pages by link authority, build a simplified search ranking system, or understand how link structure determines page importance — even if they say 'which pages are most important', 'link analysis', or 'page authority score'.".

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 bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say 'should we bundle these products', 'bundle pricing', or 'package deal pricing'.".

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。

"Conduct Van Westendorp Price Sensitivity Meter analysis to identify acceptable price ranges. Use this skill when the user needs to determine price boundaries for a new product, find the optimal and indifference price points, or survey-based pricing research — even if they say 'what should we charge', 'price sensitivity survey', or 'acceptable price range'.".

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 and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.".

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。

"Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet spec', 'process capability', or 'Cpk calculation'.".

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 and exploit SMTP Open Relays. This skill teaches how to test mail servers to determine if they process email delivery regardless of the sender or recipient domain, enabling attackers to spoof internal addresses and bypass basic anti-phishing controls.

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。

Expands one indicator into the full scope of an incident: pivoting across data sources to find related hosts, accounts, and infrastructure, building a timeline, and bounding what is and is not affected. Activates for requests to scope an incident, pivot from a single IOC, or determine the blast radius of a detection.

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 ScriptableObject-based systems including data containers, event channels, and runtime sets. Use when creating game data (weapons, enemies, items, abilities), cross-system events, or live object tracking. Triggers on requests like "create weapon data", "add an event system", "make a runtime set", "decouple these systems", "create item database", "add a damage event", "track active enemies", "create a config SO", "add game settings data", or any task involving ScriptableObject data containers, event-driven communication, or runtime object tracking. Reads ProjectConfig.yaml -> architecture_pattern to determine SO-first vs DI-first context.

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 task needs the judgment of a Crushing/Grinding Machine Operator — recognizing that classifier cut point, not mill residence time alone, determines what reports to finished product in a closed-circuit grinding operation, monitoring circulating load as a distinct diagnostic variable, matching grinding media size to the target size reduction, or verifying product size via actual sieve/laser analysis rather than mill parameters.

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。

Guide for annotating ENCODE peaks with genomic features using ChIPseeker and GREAT. Use when users need to assign peaks to genes, determine genomic feature distribution (promoter, intron, intergenic), or perform gene ontology enrichment of peak-associated genes. Trigger on: peak annotation, ChIPseeker, GREAT, peak to gene, genomic feature, promoter enrichment, gene ontology, peak distribution, TSS distance, nearest gene.

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。

Smart code scanner that detects Spring Boot patterns and routes to appropriate skills. Use when editing Java or Kotlin files in Spring Boot projects, working with pom.xml/build.gradle containing spring-boot-starter, or when context suggests Spring Boot development. Detects annotations (@RestController, @Entity, @EnableWebSecurity, @SpringBootTest) to determine relevant skills and provides contextual guidance. Uses progressive automation - auto-invokes for low-risk patterns (web-api, data, DDD), confirms before loading high-risk skills (security, testing, verify).

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。

Per-type rich result eligibility checker. Given a URL or page type, determine WHICH Google rich results the page is eligible for, validate existing JSON-LD against Google's required/recommended properties per type, and produce a gap report. Use when user says "rich results", "rich snippets", "eligible rich results", "structured data types", or "schema eligibility".

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 an existing mathematical, logical, algorithmic, or technical proof from its stated conclusion backward as a bounded AND/OR graph of exact obligations. Use to reconstruct conclusion dependencies, determine whether a proof-supplied route establishes the conclusion, or expose hidden assumptions, unmet side conditions, cycles, overly strong steps, or silent route switches. For a source-mapped Lamport rendering use convert-lamport, and for forward hierarchy, construct, and scope conformance use forward-lamport before this skill. This does not decide full theorem-level entailment by searching for a different proof; do not turn the audit into a linear reversed proof, exhaustive proof search, or replacement proof.

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 an ASC 606 / IFRS 15 revenue recognition engine for SaaS — the five-step model (identify contract, identify performance obligations, determine transaction price, allocate to performance obligations, recognize as POs are satisfied) applied to subscription contracts.

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 statistically rigorous experiments to test data science hypotheses. Use when the user says "design an A/B test", "run an experiment", "test this hypothesis", "is this difference significant", "how many samples do I need", "randomized controlled trial", "causal inference", "uplift test", "holdout group", "significance test", "power calculation", "avoid p-hacking", or needs to determine whether an observed effect is real before making a product or model decision.

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。

Migrates Android applications from the old, legacy Google Mobile Ads (GMA) SDK (com.google.android.gms:play-services-ads) to the new GMA Next-Gen SDK (com.google.android.libraries.ads.mobile.sdk:ads-mobile-sdk). Provides comprehensive mapping tables for imports, classes, and method signatures to help determine migration steps. Use when migrating an existing Android codebase from the old, legacy GMA SDK to GMA Next-Gen SDK.

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 to determine whether a Salesforce Commerce project should use B2B Commerce (account-gated, BuyerGroup-based, contract pricing) or D2C Commerce (consumer storefront, guest checkout, individual-based). Trigger keywords: B2B vs B2C, commerce platform selection, buyer journey, account-based purchasing, consumer storefront, licensing decision. NOT for implementation mechanics — use admin/b2b-commerce-store-setup or admin/b2c-commerce-store-setup for configuration work. NOT for Salesforce B2C Commerce (SFCC/Business Manager) 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。

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.

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。

Determine which LLM model to use for each agent spawn based on configuration, session directives, and task type

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。