'activation' 검색 결과

Improve the experience after signup so new users reach their first useful result sooner. The skill reviews setup steps, checklists, empty screens, and follow-up messages, then proposes prioritized changes and metrics for measuring activation and retention.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find where new users drop out after signup
• Redesign initial setup and onboarding checklists
• Write guidance for empty screens and tooltips

Analyze CLR activation logs to explain how .NET Framework selects and loads its runtime, the software that executes managed code. Diagnose loading failures, unexpected version choices, installation dialogs, and conflicting activation paths within a process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures to load the .NET Framework runtime
• Explaining why an unexpected CLR version was selected
• Tracing .NET 3.5 installation dialogs or their absence

Investigate why a PyTorch model behaves a certain way by changing its internal values with pyvene. The skill guides experiments that swap or remove activations, train interventions, compare outputs, and save or share intervention configurations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locating model components that affect an output
• Swapping activations to test behavioral hypotheses
• Training interventions to investigate causal structure

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions

Guides experiments that change internal values in PyTorch models to test what causes their behavior. Uses pyvene to swap or remove activations, locate influential components, train interventions, and save experiments for reuse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locating model components that affect an answer
• Comparing outputs after swapping internal activations
• Training interventions to investigate causal structure

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions

Improve the experience after signup so new users reach a useful result sooner. Review setup steps, checklists, empty screens, and email triggers, then produce a prioritized audit or a redesigned flow with a measurement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate drop-off during initial setup
• Design a first-session checklist
• Write guidance for screens with no data

Improve the experience after signup by identifying where new users drop off and helping them reach their first useful result. Develop setup flows, checklists, guidance, email triggers, and prioritized experiments based on activation metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find where new users drop off after signup
• Design setup checklists and empty-state guidance
• Coordinate onboarding emails with in-app actions

Create and maintain Claude Code skills with clear file structure and activation rules. Get guidance on hooks, which run at specific workflow events, plus troubleshooting, edit restrictions, and splitting detailed instructions into reference files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a new Claude Code skill
• Tuning skill activation conditions
• Investigating missing or unwanted activation

Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Improve user onboarding flows to increase activation and retention. Use when asked to design welcome screens, setup wizards, product tours, onboarding checklists, or activation sequences. Trigger phrases: "onboarding flow", "user activation", "welcome screen", "product tour", "onboarding checklist", "first-run experience", "setup wizard", "aha moment", "time to value", "user onboarding", "activation rate".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnoses .NET Framework CLR activation issues using CLR activation logs (CLRLoad logs) produced by mscoree.dll. Use when: the shim picks the wrong runtime, fails to load any runtime, shows unexpected .NET 3.5 Feature-on-Demand (FOD) dialogs, unexpectedly does NOT show FOD dialogs, loads both v2 and v4 into the same process causing failures, or any time someone is wondering "what is happening with .NET Framework activation?"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For ongoing email sequences, see emails.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and reviews the three lifecycle events of a WordPress plugin — activation (one-shot setup, dbDelta, add_option seeding, cron schedule, cap seeding), deactivation (reversible cleanup, cron clear via wp_unschedule_hook, never delete user data), and uninstall.php (standalone file, WP_UNINSTALL_PLUGIN guard, no autoloader, full data removal). Multisite-aware patterns using the $network_wide / $network_deactivating callback args, plus the recommendation against register_uninstall_hook in favor of uninstall.php. Use when scaffolding a plugin or debugging ghost cron events / orphan options. Triggers on register_activation_hook, register_deactivation_hook, uninstall.php, WP_UNINSTALL_PLUGIN, dbDelta, wp_unschedule_hook, switch_to_blog.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnoses .NET Framework CLR activation issues using CLR activation logs (CLRLoad logs) produced by mscoree.dll. Use when: the shim picks the wrong runtime, fails to load any runtime, shows unexpected .NET 3.5 Feature-on-Demand (FOD) dialogs, unexpectedly does NOT show FOD dialogs, loads both v2 and v4 into the same process causing failures, or any time someone is wondering "what is happening with .NET Framework activation?"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when publishing, unpublishing, replicating, or activating/deactivating content in AEM 6.5 LTS. Covers Quick Publish, Manage Publication, tree activation, bulk content activation, package replication, workflow-based approval, scheduled activation/deactivation (on/off time), dispatcher flush, and the programmatic Replication Java API for AEM publish instances.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when publishing, unpublishing, replicating, or activating/deactivating content in AEM 6.5 LTS. Covers Quick Publish, Manage Publication, tree activation, bulk content activation, package replication, workflow-based approval, scheduled activation/deactivation (on/off time), dispatcher flush, and the programmatic Replication Java API for AEM publish instances.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement or review a managed runtime provider for NemoClaw. Check its required interfaces, activation requirements, saved provider identity, and lifecycle behavior, then report validation evidence and any remaining qualification requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a new managed runtime provider
• Review provider activation requirements
• Check saved state across lifecycle operations

Compress large language model weights to four bits with AWQ, a method that uses model activation patterns to limit quality loss. Follow workflows for quantization, loading compressed models, and serving them with vLLM using less GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large language models with limited GPU memory
• Quantizing a model with domain-specific text
• Serving an AWQ model with vLLM

Audit a collection of agent skills for valid settings, clear activation conditions, complete workflows, and required files. Get scores for each target platform, prioritized fixes, and a separate record of whether activation was actually tested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking skill quality before publication
• Reviewing an imported skill collection
• Investigating excessive or missing automatic activation