Results for “distribution”

Use when an admin or partner needs to package an Experience Cloud (Community) site as a reusable Lightning Bolt Solution for distribution — covers the export workflow from Experience Builder, what gets bundled (ExperienceBundle, custom apps, flow categories, theme, layouts, navigation menus) versus what does NOT (data, CMS content, files), choosing Bolt vs managed package vs unlocked package vs cloning a site, sandbox-to-production promotion, multi-org distribution, AppExchange listing as a Bolt, and template versioning via the LightningBolt metadata `versionNumber`. Triggers: 'turn this community into a reusable template', 'package an Experience Cloud site to ship to multiple orgs', 'export Experience Builder template for AppExchange', 'should we use a Bolt or a managed package for this community', 'create an industry-specific community starter', 'how do we version our partner portal template', 'distribute branded Experience site across business units'. NOT for general Experience Cloud site build, content, or member setup (use admin/experience-cloud-site-setup, admin/experience-cloud-cms-content, admin/experience-cloud-member-management). NOT for shipping Apex / LWC / data-model functionality as a product (use devops/managed-package-development, devops/second-generation-managed-packages, devops/unlocked-package-development). NOT for moving a single Experience site between sandbox and prod as a one-off (use devops/experience-cloud-deployment-admin, devops/cicd-for-experience-cloud).

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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

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Expert-level statistics knowledge. Use when working with probability distributions, hypothesis testing, regression, Bayesian statistics, statistical inference, experimental design, ANOVA, or statistical modeling. Also use when the user mentions 'p-value', 'confidence interval', 'hypothesis test', 'regression', 'ANOVA', 'chi-square', 't-test', 'Bayesian', 'maximum likelihood', 'statistical significance', 'correlation', 'normal distribution', or 'sampling distribution'.

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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

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App Store Connect API 4.5.1 (REST) アプリ配布リファレンス。JWT 認証、API キー作成・失効、 Apps, Builds, Bundle IDs, Bundle ID Capabilities, Certificates, Devices, Provisioning Profiles, Users, User Invitations, Merchant IDs, Pass Type IDs、TestFlight (Beta Groups / Testers)、 Sandbox Testers、App Review, Review Submissions, Customer Reviews, Custom Product Pages, Phased Release、In-App Purchases, Subscriptions, Subscription Groups, App Price Points, Promotional Offers, Win-back Offers, Sales Reports、App Asset Library (screenshots / app previews)、 Webhooks / Webhook Events / Deliveries、Xcode Cloud (Workflows, Build Runs, Artifacts)、 Alternative Distribution (EU マーケットプレイス / Web Distribution)、 Game Center (Achievements, Leaderboards, Challenges)、Analytics / 診断レポート。

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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

The English summary is being prepared. Showing the original description.

Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

The English summary is being prepared. Showing the original description.

AEM Cloud Service expert skill for replication / content distribution. Covers migration from CQ Replicator (com.day.cq.replication.Replicator) and Sling Replication Agent (org.apache.sling.replication.agent.api) to the Sling Distribution API (Distributor + SimpleDistributionRequest). Includes agent selection (publish vs preview), async response handling, author cluster coordination, service-user setup, review checklist, troubleshooting, and common pitfalls.

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Build a FlowAI-style team management dashboard with member tables, activity history, summary metrics, and charts. The result is a self-contained HTML prototype with working tabs, light and dark themes, panel zoom, chart tooltips, and CSV export.

• Prototyping a team management dashboard
• Presenting role distribution and online presence
• Reviewing member details and activity across tabs

Turn data into chart images by selecting a suitable format from 26 options. Visualize trends, comparisons, distributions, flows, or relationships, then receive the image URL and the complete settings used to generate it.

• Visualize changes over time
• Compare categories and proportions
• Show distributions and correlations

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Inspect and edit Live2D Cubism model files from the command line. Check referenced assets, adjust motion and expression settings, compare versions, process multiple models, and prepare model files for distribution without opening Live2D Editor.

• Check for missing assets and unused files
• Adjust motion fade times across multiple models
• Compare model versions

Plan how to add external capabilities to an AI agent using MCP, a connection protocol, or Skill Hub distribution. Compare integration methods, design tools and data access, and review context costs, credentials, and third-party security risks.

• Choosing between MCP connections and skill distribution
• Designing an MCP server and its connection method
• Reviewing third-party tools and credential permissions

Create or update reusable skills for AI agents. Plan clear instructions, organize scripts, reference documents, and templates, then validate the skill and package its files for distribution.

• Turn recurring tasks into reusable agent skills
• Simplify an existing SKILL.md and organize references
• Bundle scripts and templates with a skill

Package a local MCP server, which connects an AI host to tools, as a .mcpb file. Include dependencies and runtime support, define installation settings, and validate, test, and sign the bundle for distribution.

• Package a local MCP server for distribution
• Offer installation without Node or Python setup
• Define folder selection and other installation settings

Turn recurring procedures and specialized knowledge into reusable agent skills. It guides the design of SKILL.md, scripts, reference documents, and output assets, then validates the structure and packages the files for distribution.

• Turning a recurring task into an agent skill
• Organizing internal knowledge and reference documents
• Bundling scripts and templates with a skill

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Analyze data distributions, trends, unusual values, and differences between groups using suitable statistical methods. The skill guides interpretation of effect sizes, meaning the magnitude of differences, alongside uncertainty and business impact, while checking for misleading conclusions.

• Summarize business metrics beyond averages
• Assess differences in A/B test results
• Investigate outliers and unusual time-series values

Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.

• Understanding an unfamiliar table or data file
• Checking missing values, duplicates, and suspicious entries
• Choosing measures and grouping columns for analysis

Clarify a founder's business question through six written answers covering the problem, customer, distribution, competitive protection, capital, and founder fit. Turn the answers into a one-page brief with an assessment of readiness for further strategy discussion.

• Clarify assumptions before starting a major initiative
• Organize business questions before fundraising
• Examine the rationale for a strategic pivot

Publish an OpenWork version through Git tags and GitHub Actions without creating version-update commits. Review outstanding fixes, monitor builds and publication, check release assets, and recover failed distribution steps using procedures that protect already published desktop installers.

• Publishing a patch, minor, or major OpenWork release
• Reviewing open fixes before creating a release tag
• Recovering distribution steps for an existing release tag

Build, improve, review, or troubleshoot Agent Zero plugins for local use or community distribution. Follow plugin structure and installation rules, verify behavior in the target runtime, and prepare repository and Plugin Index contributions when requested.

• Building an Agent Zero plugin for local use
• Reviewing an existing plugin's structure and behavior
• Investigating plugins that fail to load or run

Assess whether a company's distributions can support an income-focused investment. Examine dividend history, cash coverage, debt, valuation, and dividend-cut scenarios, then explain its possible portfolio role and calculate income where the supplied information supports it.

• Evaluate whether a high dividend yield is sustainable
• Review dividend coverage and cut risks in an existing holding
• Calculate gross and net dividend income when inputs allow

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.

• Monitoring training loss and accuracy
• Comparing experiments with different training settings
• Inspecting weight and gradient distributions