「measurement」の検索結果

Structures earnout and contingent payment mechanisms with milestone definitions, measurement periods, and payout scenarios. Use when modeling earnouts, designing milestone-based payments, or valuing contingent consideration.

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Structures operating partner involvement with portfolio company assignment, initiative tracking, and impact measurement. Use when coordinating operating partners, tracking operational initiatives, or measuring portfolio support impact.

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。

Constructs ROIC decomposition with invested capital measurement, operating return analysis, and value creation vs destruction assessment. Use when analyzing ROIC, building capital return frameworks, or assessing value creation.

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。

Evaluates alternative trading systems with fill rate analysis, information leakage assessment, and venue toxicity measurement. Use when analyzing dark pools, evaluating ATS venues, or assessing execution venue quality.

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。

Content strategy, copywriting frameworks (AIDA/PAS/BAB/4Ps/FAB), editorial calendar management, platform-specific content, A/B testing, campaign planning, audience targeting, and content performance measurement.

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用户需要从外部可观测信号推断内部隐藏状态时; 当需要"从表象看本质"时; 当直接观测不可行, 必须通过间接指标推断时。 不适用于: 可以直接观测内部的场景、纯数据查询、不需要推断的场景。

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Used when the user asks to "create a PowerPoint", "generate PPTX slides", "make a PowerPoint presentation", "build a .pptx deck", "create editable slides", "PptxGenJS", "editable presentation", "corporate deck", "slide deck for sharing", or when the output format is PPTX for any presentation task. Also triggers on "export to PowerPoint", "pitch deck PPTX", "offline presentation", or when the context implies an editable/corporate format. Generates OOXML-compliant .pptx files with PptxGenJS, precise layout, text measurement, and overlap validation.

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Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.

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Evaluate and compare levitation mechanisms for a given application through a structured trade study. Covers magnetic (passive diamagnetic, active feedback, superconducting), acoustic (standing wave, phased array), aerodynamic (hovercraft, air bearings, Coanda effect), and electrostatic (Coulomb suspension, ion traps) mechanisms. Use when selecting the most appropriate levitation approach for transport, sample handling, display, bearings, or precision measurement applications.

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Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.

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xUnit, Moq/NSubstitute, FluentAssertions, and coverlet patterns for any .NET project. Covers test structure ([Fact]/[Theory]/[InlineData]), mocking discipline, fluent assertions, integration test setup with WebApplicationFactory, and coverage measurement. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with xUnit [Fact] and [Theory]. - Mock dependencies with Moq or NSubstitute without overusing mocks. - Write expressive assertions with FluentAssertions. - Measure coverage with coverlet and enforce a minimum threshold. Do NOT use this skill for: - ASP.NET Core-specific integration tests (WebApplicationFactory, HttpClient — those are in aspnet-core-plugin:aspnet-conventions). - EF Core in-memory or SQL Server LocalDB test patterns (aspnet-core-plugin:efcore-patterns). - C# language idioms — see csharp-foundation:csharp-conventions.

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Use when a user needs to create or improve a deterministic, engine-agnostic fuzz harness for raw or structured target inputs. Not for coverage measurement: use fuzzing-coverage-analysis.

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Apply one independently reviewable numeric Proto UI package-budget increase for an already accepted capability, using canonical cost evidence and existing development authorization. Use for standalone ceiling transactions, not feature implementation, measurement-policy changes, spending limits, publication or release.

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Add a real achievement, measurement, skill, or project update to the master resume. Use when the user wants to record new factual experience.

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。

Invoke only when the user explicitly asks to review code through the "single level of abstraction / layered error handling" lens — a function does only its own layer's business logic while errors are handled above or below. The agent reports detections, raw-count measurements, and move directions. Apply only when the user explicitly requests this lens.

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Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Run an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.

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。

Performance profiling principles. Measurement, analysis, and optimization techniques.

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Use when a feature launch needs experiment design, test sequencing, rollout guardrails, and measurement logic before exposure to users.

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Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.

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Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.

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"Apply rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.".

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"Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.".

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"Calculate network centrality metrics to identify important nodes in graphs. Use this skill when the user needs to find key influencers, critical infrastructure nodes, or central actors in a network — even if they say 'who is most important in this network', 'key nodes', or 'network influence measurement'.".

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。