「versus」の検索結果

Use when deciding whether a request needs an analiz task before implementation - the conditions that require the architect's analysis versus going straight to 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。

Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.

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 evidence-first engineering discipline to any agent-built software project: fact discovery before action, evidence-calibrated status reporting, explicit change boundaries and stage gates, controlled confirmation for risky or external actions, sensitive-information redaction for anything shared publicly, and resumable continuity after interruptions. Use when users ask an agent to take over an existing codebase, deliver a feature across stages, verify whether work is actually complete, judge release readiness, or keep conclusions honest about what is proven versus assumed. This foundation skill is domain-neutral; vertical suites (for example mini-program engineering) build on it by adding domain facts and platform rules.

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。

Diagnose and fix mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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 working with the non-metallic engineering materials — polymers, ceramics, or composites — and you need to reason about structure, processing, or properties. Covers thermoplastics versus thermosets, chain structure, glass transition, and viscoelasticity in polymers; ionic and covalent bonding, sintering, brittleness, and Weibull statistics in ceramics; and fiber-reinforced laminates, matrix and reinforcement roles, the rule of mixtures, and anisotropy in composites. Reach for it to choose a plastic, understand why a ceramic fails, or predict the stiffness of a laminate.

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。

Experience-based and environment-based learning design drawn from Dewey and Montessori. Covers Dewey's criterion of continuity and interaction, the reflective-thinking cycle, Montessori's prepared environment, sensitive periods, child-led inquiry, the role of self-correcting materials, project-based learning, and the relationship between autonomy and discipline. Use when designing hands-on activities, arranging learning spaces, integrating classroom work with real experience, or evaluating whether a lesson is genuinely experiential versus merely "activity-based."

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。

Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.

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。

Embedded firmware development for microcontrollers including memory-mapped I/O, GPIO, interrupts, timers, UART/SPI/I2C serial protocols, PWM generation, ADC sampling, interrupt-driven versus polled architectures, RTOS fundamentals, and the bring-up sequence from power-on reset through first printf. Covers AVR/Arduino, ARM Cortex-M, and ESP32 patterns. Use when writing, porting, debugging, or reviewing firmware that directly manipulates hardware peripherals.

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 of active analog circuits built around operational amplifiers and discrete gain stages — inverting, non-inverting, difference, and instrumentation topologies, negative feedback and loop stability (gain-bandwidth product, phase margin, compensation), active filters (Sallen-Key, multiple-feedback biquad), sinusoidal and relaxation oscillators, comparators and Schmitt triggers, voltage references (bandgap, shunt, series), and linear versus switching voltage regulators. Covers how to pick a topology, close a feedback loop that stays stable, set gain and bandwidth, and choose a power-conversion approach. Use when designing an amplifier, filter, oscillator, reference, comparator, or regulator, or when reasoning about feedback stability, loop gain, phase margin, or op-amp non-idealities.

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。

Public speaking and oral presentation skills for effective communication. Covers speech structure (introduction, body, conclusion), delivery techniques (projection, pace, pause, gesture), audience analysis, impromptu and extemporaneous methods, managing anxiety, visual aids, and persuasive versus informative speaking. Use when preparing speeches, practicing delivery, analyzing presentations, or building confidence in oral communication.

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。

Interpersonal communication theory and practice for one-on-one and small group interaction. Covers communication models (linear, transactional, constructivist), self-disclosure and Johari Window, relational dialectics, communication climate, supportive versus defensive communication, feedback models, and adapting style across relationships and contexts. Use when analyzing communication dynamics, improving relationships, giving feedback, or understanding communication breakdowns between individuals.

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 reviewing, shipping, or regression-testing a SKILL.md and you hold one or more captured trajectories where that skill was actually loaded. Translates the skill's instructions into behavior constraints, then reports which were EXERCISED (coverage) versus untested, and flags any that were in-scope but VIOLATED (compliance) — a cheap single-trajectory test-adequacy gate. It surfaces untested instructions rather than auto-blocking, and escalates fail-closed only when a violation touches shared repo state, never-surface memory, or self-modification. Complements skill-counterfactual-audit (which measures EFFECT) and skill-causal-curation (which measures contribution); this one asks ADEQUACY. Backed by the agent-skill-coverage metric (arxiv 2606.20659v2). Triggers on skill review, ship gating, and regression-testing a skill against its trajectories.

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。

开箱 / 对比评测模板：生成电商开箱视频与 A/B 对比评测的结构、口播稿、拍摄清单与分镜（含公平对比规则、测试设计、缺点与适合人群），可直接转成 storyboard.json 渲染。Use when the user wants an unboxing video, 开箱, 测评, 评测, 横评, 对比, review script, product comparison, versus video, or UGC-style creator review for an ecommerce product.

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, create, update, and verify Vapi Squads and documented handoff tools through the public API. Use for choosing a single assistant versus a multi-assistant Squad, persistent or transient members, entry-member ordering, specialization boundaries, context engineering, variable extraction, model-specific handoff patterns, assistant-version pins, and safe Squad updates.

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, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.

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, create, or validate saved and transient Vapi voice assistants. Use for new phone or web agents, production system prompts and first messages, saved-versus-transient architecture, model/voice/transcriber selection, multilingual compatibility, existing tool attachment, native call-control tools, assistant hooks, and Create Assistant API validation errors.

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。

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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。

Turns a theory into falsifiable, pre-registerable hypotheses, covering the DGP and DAG, identification, SATE versus PATE, counterfactual and directional framing, named estimands, justified SESOIs, the choice among NHST, equivalence, interval, and minimum-effect tests, scope conditions, and primary, secondary, and exploratory tiers. Use when the user asks to turn a research question or theory into hypotheses, whether a prediction is falsifiable, what the estimand is, or how to predict a null. Framing goes to narrative-building, the plan to pre-registration-writing.

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。

Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-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。

Designs multi-country survey experiments: case selection, localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when fielding the same experiment in several countries.

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。

Designs conjoint and factorial-vignette experiments: attributes and restrictions, effective-N power, estimand choice among AMCE, marginal means, and AMIE, forced choice versus rating, and PAP tiers. Use when planning a conjoint, critiquing an attribute table, sizing respondents and tasks, or testing interactions.

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。

Designs conjoint and factorial-vignette experiments, covering attribute architecture and randomization restrictions, effective-N power, treatment realism, the choice among AMCE, marginal-mean, and AMIE estimands, forced-choice versus rating designs, and PAP tiers. Use when the user is planning a conjoint, drafting or critiquing an attribute table, asking how many respondents or tasks are needed, whether to report AMCEs or marginal means, or how to test interactions. Reviewing an existing design goes to conjoint-diagnostics, cleaning the export to conjoint-cleaning.

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。

Designs multi-country survey experiments, covering case selection with a stated comparative logic, instrument localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when the user fields the same experiment in several countries, asks which countries to include, how to adapt an instrument without breaking comparability, whether to pool, or how large each country sample must be. Question wording goes to survey-design, reporting to methods-reporting, the study plan to pre-registration-writing.

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 high-CTR YouTube and YouTube Shorts titles under 100 characters using 2026 packaging formulas (curiosity-gap, number, how-I outcome, mistake/loss, transformation, versus). Balances curiosity with a real keyword, front-loads the click-deciding words for the 60-char mobile cutoff, and returns 3 to 5 A/B variants tagged by goal for Test and Compare. Use to title or rework a video. Not for the description (use yt-description-writer) or the thumbnail overlay (use yt-thumbnail-brief).

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。