「experiment」の検索結果

Improve conversion performance on marketing pages and forms through diagnosis, message hierarchy, friction analysis, proof, UX hypotheses, instrumentation and experiments.

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 valid marketing experiments with hypotheses, primary metrics, guardrails, sample/traffic assumptions, implementation QA and decision 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 draft or explicitly apply paid-ad optimizations using evidence, financial constraints, experiments, and capability-gated adapters. Use for campaign optimization, budget reallocation, bid changes, pausing or archiving ads, requests to delete campaigns, search-term or negative-keyword actions, creative rotation, or improving CPA and ROAS.

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。

Create a professional paid-advertising strategy covering objectives, economics, platform selection, campaign architecture, audiences, budget, creative, measurement, experiments, governance, rollout, and reporting. Use for ad plan, media plan, PPC strategy, paid-social strategy, campaign architecture, advertising roadmap, or channel planning.

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 and model paid-media CPA, CPL, CPC, CPM, ROAS, MER, break-even targets, contribution margin, LTV:CAC, impression-share opportunity, budgets, forecasts, and experiment economics. Use for PPC math, ad calculator, break-even analysis, ROAS calculator, CPA calculator, budget forecast, LTV CAC, or MER.

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Operate professional paid advertising across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for account intake, source-grounded audits, strategy, budget and measurement planning, creative production, experiments, reporting, monitoring, and explicitly approved campaign changes. Also trigger on PPC, paid social, retail media, attribution, tracking, landing pages, cross-platform conversion totals, negative keywords or search terms, beta-feature scoring, stale platform claims, API-token or credential setup, campaign deletion, and safe Claude Ads installation or uninstall.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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 evaluating, critiquing, transforming, or generating a Master's Informatics thesis idea, research gap, novelty, research question, experiment, AI/ML/CV/NLP/LLM/Edge-AI method, or when distinguishing R&D research from software implementation using the Frascati Manual 2015.

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在授权测试中核验 Node.js 权限模型（--permission / --experimental-permission，及旧 policy.json）能否被内部 API 绕过。当目标依赖 Node.js Permission Model 做沙箱隔离、需要判断给定 Node 版本下是否存在已知逃逸（inspector 断点、process.binding、fs.statfs/openAsBlob、process.mainModule.require、Module._load、path.resolve 覆盖、Uint8Array 路径、符号链接重命名）时使用。适用目标类型 Node.js 运行时 / 依赖权限模型的服务或 CLI。触发场景包括用户说"这个 Node 权限模型靠谱吗""--permission 能被绕过吗""测下 policy.json 沙箱逃逸""inspector/process.binding 能突破权限吗"。输出：给定 Node 版本下各绕过原语的命中矩阵 + 建议升级版本（含 killed 记录）。

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。

Implements Riverpod (v3.3.x) compile-safe state management for Flutter apps. Use when implementing @riverpod code generation providers, handling AsyncValue loading/error/data states, using ref.watch/ref.read/ref.listen, setting up ProviderScope, writing Notifier/AsyncNotifier classes, using FutureProvider/StreamProvider, applying autoDispose and family modifiers, combining providers, testing providers in isolation, scoping rebuilds with Consumer instead of ConsumerWidget, using ref.watch with select() for field-level precision, migrating from StateNotifierProvider or Riverpod 2.x patterns, handling Notifier resource lifecycle with ref.onDispose, or using experimental Mutation and offline persistence APIs.

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。

Experimental workflow for monitoring explicitly authorized delivery tasks and reporting stalled work. Report-only: findings are produced in the session or as a draft; no scheduled action is taken.

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。

Experimental workflow for babysitting one explicitly authorized pull or merge request. Use to monitor its live review state and checks, fix actionable in-scope findings, and apply separate reply, approval, merge, and soak gates.

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。

Experimental workflow for finding interrupted coding runs and reporting resume candidates. Use after an agent host restart or an interrupted automation; resuming anything needs the user's literal GO.

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。

Experimental workflow for publishing and completing configured software delivery work. Use when the user or an enabled factory policy asks to ship.

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。

Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or drafting proposed host automations for report-only review.

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。

Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability.

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 for any code, documentation, config, dependency, test, review, or maintenance task in the Nauthilus repository that must follow AGENTS.md, POLICY.md, focused reproducer-first debugging, strict DRY/OOP boundaries, English comments, GOEXPERIMENT=runtimesecret test discipline, Makefile-driven validation, prompt-sync checks, and dirty-tree preservation.

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 running the dev Grafana stack and/or the Playwright e2e suite against a *packaged* plugin zip (a release/RC artifact like `hydrolix-hydrolix-datasource-<ver>.zip`) rather than the working-tree `dist/`. Unpacks the package into a timestamped `dist_<timestamp>/dist` sandbox alongside copies of the compose artifacts, so the run is reproducible and the folder stays available to experiment with afterwards. Delegates stack bring-up to `stack-run` (frozen mode: read-only mount + `DEV=false` so the mage-watcher can't rebuild the packaged Go binary), verifies signature/version, runs the suite, and treats cleanup as optional — the sandbox is deleted only with the user's approval. Apply whenever the task is "test this plugin package / zip" or "run e2e against the built artifact".

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 for A/B experiments on OpenAmer configs and skills.

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。

W&B: log ML experiments, sweeps, model registry, dashboards.

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。

Throwaway experiments to validate an idea before build.

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。

Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.

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。

Training Details of the Second RL Stage 4 Experiment 5 Ethics and Safety Statement 6 Conclusion, Limitation, and Future Work 7 Author List A Backgroun

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Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.

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。