290 件 ・ 関連度順
概要と使いどころ
Use when defining or auditing the activation event — aha-moment selection, retention correlation, falsifiable definition. Triggers on 'what is our aha moment', 'redefine activation'.
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。
Behavioral activation for depression: Breaking the vicious cycle, activity monitoring, weekly planning, and values-based activities.
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。
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching 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。
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。
Design activation metrics. TRIGGERS - Use when user needs help with activation-metric-design related tasks.
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 poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).
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。
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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。
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
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。
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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。
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
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。
ACTIVATION PREREQUISITE: the request or discovered target must explicitly identify C#, .NET, `.cs`, or `.csproj`; otherwise stay dormant without invoking this skill. USE FOR: locating System.DateTime.Now/UtcNow, System.IO.File/Directory, System.Environment, HttpClient, Console, or Process usage in C#; auditing C# code for hard-to-test framework dependencies; or verifying those C# calls are already abstracted. DO NOT USE FOR: any target lacking the activation prerequisite; generating wrappers (use generate-testability-wrappers); migrating code (use migrate-static-to-wrapper); or general code 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。
[omh] Churn or weak activation among users: turn an observed onboarding, activation, retention, re-engagement, referral, or monetization problem into one consent-safe in-app journey or growth experiment plan with a bounded readout and an explicit decision. Use when the user says: lifecycle-growth, lifecycle growth, lifecycle marketing, lifecycle messaging, in-app journey, in-app message campaign, onboarding journey, onboarding nudge.
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 the user asks to "build a win-back campaign", "re-engage lapsed subscribers", "run a re-permission / re-consent sweep", or "sunset my dead list"; produces a closed-loop reactivation program — a lapsed-cohort definition, a staged offer ladder, a re-consent (re-permission) capture step, and a sunset-confirm / suppression rule. Owns none of the SEND-N sub-item notes: engagement-decay / sunset is email-sequence-designer's and preference-center / frequency options is preference-frequency-manager's — this skill references those notes, it does not re-emit them. Not for the general lifecycle flows (welcome/cart/post-purchase) — use email-sequence-designer; not for the preference-center / opt-down ladder — use preference-frequency-manager; not for the consent record itself — use consent-registry; not for computing EQS or the N1 veto — use email-quality-auditor. 流失召回/重新授权/沉默用户清理
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 retention problems using behavior design (B=MAP). Use when the user mentions "users sign up but dont stick around", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users quit after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.
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 developing WordPress plugins: architecture and hooks, activation/deactivation/uninstall, admin UI and Settings API, data storage, cron/tasks, security (nonces/capabilities/sanitization/escaping), and release packaging.
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 improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-strategy.
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。
Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.
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。
Post-signup user onboarding optimization covering activation metrics, time-to-value reduction, onboarding flow design, empty state optimization, multi-channel coordination, and stalled user recovery.
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。
ACTIVATION PREREQUISITE: the request or discovered target must explicitly identify C#, .NET, `.cs`, or `.csproj`; otherwise stay dormant without invoking this skill. USE FOR: locating System.DateTime.Now/UtcNow, System.IO.File/Directory, System.Environment, HttpClient, Console, or Process usage in C#; auditing C# code for hard-to-test framework dependencies; or verifying those C# calls are already abstracted. DO NOT USE FOR: any target lacking the activation prerequisite; generating wrappers (use generate-testability-wrappers); migrating code (use migrate-static-to-wrapper); or general code 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。
Diagnose where your growth is actually leaking (acquisition, activation, retention, or loops) and produce a growth plan with a named loop, an activation definition, a 90-day experiment portfolio and channel guardrails. Use it when someone says growth stalled, signups don't activate, retention is flat, how do we build a growth loop, should we go PLG, onboarding redesign, churn, referral program, or which channel should we bet on. Draws on 672 insights from 143 Lenny's Podcast guests, led by Elena Verna, Casey Winters and Lauryn Isford.
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 needs to design, improve, or audit a post-signup activation flow to get new users to their first value moment. Activate when activation is lagging, time-to-value feels excessive, or first sessions lack 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。
World-class growth strategy expertise combining Andrew Chen's marketplace and network effects wisdom, Brian Balfour's growth frameworks, Casey Winters' Pinterest/Grubhub playbooks, and the best of Silicon Valley growth thinking. Growth is not marketing. Growth is the systematic application of product, engineering, and data to create compounding user acquisition, activation, and retention. It's a mindset, not a department. Use when "growth strategy, how do we grow, acquisition strategy, retention strategy, viral growth, network effects, growth loops, product-led growth, plg, ltv cac, unit economics, growth model, flywheel, compound growth, channel strategy, referral program, activation rate, magic moment, aha moment, growth experimentation, growth, strategy, acquisition, retention, viral, network-effects, plg, loops, experimentation" mentioned.
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。
Suede-affiliated onboarding and activation strategy for first-run sequencing, empty states, setup checklists, activation milestones, time to value, and retention-linked measurement. Use when users sign up but fail to reach first value or the product needs a new first-session flow. NOT FOR: registration optimization (use suede-signup), lifecycle email sequences (use suede-emails), or monetization prompts (use suede-paywalls).
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。