1.5万 件 ・ 関連度順
概要と使いどころ
Customer success across onboarding, adoption, retention, and expansion. Use when designing onboarding playbooks, calculating health scores, building QBR decks, planning renewal strategies, or identifying expansion opportunities.
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 run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
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。
Growth marketing covering experimentation, funnel optimization, acquisition channels, retention, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, or prioritizing channels by CAC and LTV.
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 A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
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。
International market expansion strategy for scaling companies. Use when expanding to new countries, evaluating international markets, planning localization, building regional teams, or assessing regulatory requirements by region.
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。
Customer leadership advisor on CX strategy, retention and expansion, and voice-of-customer programs. Use when defining a CX strategy, scoring CX maturity, planning churn interventions, or designing a VoC program.
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。
Export marketing data to a warehouse or Sheets, PII-redacted, approval-gated. "export contacts to a sheet"
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 a complete explicit instruction sequence from teacher modelling through guided practice to independent work. Use when teaching new skills, procedures, or concepts through direct instruction.
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 self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
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 a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about 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。
Act as experienced opposing counsel to attack, undermine, and expose weaknesses in a legal argument, submission, witness statement, or structured reasoning. Produces a six-part adversarial analysis: 1. A core theory of attack identifying the single most effective way to defeat the argument; 2. A reconstructed version of the opposing argument stripped of rhetoric to expose its fragility; 3. Primary lines of attack grouped by category (legal misstatement, evidential gaps, causation failures, internal inconsistency, over-reliance on assertion, procedural weakness); 4. An "if I were the judge" section showing how a sceptical tribunal would dismantle the argument; 5. Surgical strikes - 3 to 5 high-impact points ready for oral submissions; and 6. An analysis of what the argument is trying to hide. Written in formal, adversarial British English for a legally trained audience.
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。
Expert-level toolkit for modular Godot 4.7+ CLI automation and headless build orchestration. Use when you need to: (1) Build complex scene trees or UI layouts programmatically, (2) Automate expert 3D asset pipelines (glTF -> Collision), (3) Optimize procedural geometry headlessly (CSG -> Static Mesh), or (4) Engineer production-grade CI/CD pipelines. Set GODOT_PATH env var for custom engine location. Keywords: Godot CLI, headless, CI, export, builder, 4.7.
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。
Expert blueprint for Metroidvanias including ability-gated exploration (locks/keys), interconnected world design (backtracking with shortcuts), persistent state tracking (collectibles, boss defeats), room transitions (seamless loading), map systems (grid-based revelation), and ability versatility (combat + traversal). Use for exploration platformers or action-adventure games. Trigger keywords: metroidvania, ability_gating, interconnected_world, backtracking, map_system, persistent_state, room_transition, soft_locks.
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。
Expert patterns for AnimationTree including StateMachine transitions, BlendSpace2D for directional movement, BlendTree for layered animations, root motion, transition conditions, advance expressions, and state machine sub-states. Use for complex character animation systems with movement blending and state management. Trigger keywords: AnimationTree, AnimationNodeStateMachine, BlendSpace2D, BlendSpace1D, BlendTree, transition_request, blend_position, advance_expression, AnimationNodeAdd2, AnimationNodeBlend2, root_motion.
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。
Expert-level toolkit for modular Godot 4.7+ CLI automation and headless build orchestration. Use when you need to: (1) Build complex scene trees or UI layouts programmatically, (2) Automate expert 3D asset pipelines (glTF -> Collision), (3) Optimize procedural geometry headlessly (CSG -> Static Mesh), or (4) Engineer production-grade CI/CD pipelines. Set GODOT_PATH env var for custom engine location. Keywords: Godot CLI, headless, CI, export, builder, 4.7.
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。
Expert blueprint for Metroidvanias including ability-gated exploration (locks/keys), interconnected world design (backtracking with shortcuts), persistent state tracking (collectibles, boss defeats), room transitions (seamless loading), map systems (grid-based revelation), and ability versatility (combat + traversal). Use for exploration platformers or action-adventure games. Trigger keywords: metroidvania, ability_gating, interconnected_world, backtracking, map_system, persistent_state, room_transition, soft_locks.
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。
Expert patterns for AnimationTree including StateMachine transitions, BlendSpace2D for directional movement, BlendTree for layered animations, root motion, transition conditions, advance expressions, and state machine sub-states. Use for complex character animation systems with movement blending and state management. Trigger keywords: AnimationTree, AnimationNodeStateMachine, BlendSpace2D, BlendSpace1D, BlendTree, transition_request, blend_position, advance_expression, AnimationNodeAdd2, AnimationNodeBlend2, root_motion.
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 exporting and distributing Godot games — export presets, platform settings, CI/CD with GitHub Actions
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。
Finds the price buyers will actually pay. Reads the consumer panel's four price answers per buyer into a Van Westendorp price-sensitivity range (too cheap, a bargain, getting expensive, too expensive), sets it against what competitors really charge and against the CFO's margin, and recommends a price, a price ladder (good / better / best) and an opening offer to test. Use when the user says "how much should I charge", "price this", "is $X too expensive", "pricing strategy", "what price", or after the consumer panel.
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。
When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.
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。
Generates infrastructure for exporting app content to social platforms (Instagram Stories, TikTok, Twitter/X) with platform-specific formatting, aspect ratios, and metadata. Use when user wants social media export, share to stories, or platform-specific sharing pipelines.
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。
Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordina
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。
omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existin
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。
Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre
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。