'distribution' 검색 결과

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring performance with concurrent hosts
• Studying latency as conversation history grows
• Comparing throughput and failures under load

Build MCP servers that connect AI assistants to external services and data. This skill guides tool design, input validation, error handling, testing, and distribution, with implementation examples for TypeScript and Python.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an MCP server for an external service
• Improving tool parameters and error messages
• Testing MCP interactions before distribution

Use three adaptive questions to identify where product growth is stuck. The skill recommends a path through new customer segments, geographies, distribution channels, or products, then suggests a low-cost first experiment and a common trap to watch for.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify where product growth is getting stuck
• Choose between new customer segments and new regions
• Compare distribution expansion with a new product

Build or review quality control workflows for single-cell and single-nucleus RNA sequencing data. Select filters from observed data, detect doublets, assign cell types, and create UMAP plots that summarize cell relationships, while recording analysis decisions and outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing quality filters from dataset distributions
• Detecting doublets and checking ambient RNA
• Assigning cell types using references or marker genes

Diagnose macOS app launch and trust failures by inspecting code signatures, app permissions, and Gatekeeper checks. Identify the failure category and get a minimal validation or repair sequence that distinguishes local development issues from distribution requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a macOS app that refuses to launch
• Checking invalid signatures or signing identities
• Diagnosing app permissions and sandbox mismatches

Design a content marketing system that connects posts, videos, and newsletters to sales opportunities. It helps analyze successful content, adapt one core piece for multiple platforms, plan follow-up conversations, and measure which content contributes to deals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a content workflow within a weekly time budget
• Adapt one article or video for multiple platforms
• Connect newsletters and engaged readers to sales conversations

Assess numeric Excel columns using IQR, a measure of the middle half of values, to flag outliers. Summarize counts and limits, create box plots, and examine distribution shape to inform data cleaning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking outliers before data analysis
• Comparing outlier rates across numeric columns
• Reviewing distribution shape with box plots

Clean Excel data and group records with similar numerical features. Produce a combined view of indicator trends, value distributions, category shares, and grouped data, then export the charts as an image and cleaned records as Excel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing trends across multiple indicators
• Checking value distributions and category shares
• Grouping records with similar numerical features

Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarize Excel records using custom categories
• Compare category distributions across groups
• Score and grade text using adjustable rules

Classify existing tests with standard tags for success cases, errors, boundaries, and other behaviors. Get a per-test mapping and distribution summary, or apply tags using supported framework syntax and verify compilation and test discovery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing success-case and error-case test counts
• Adding standard tags to existing tests
• Reviewing the mix of boundary and integration tests

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures

Develop Bayesian models for quantitative social science, from choosing priors to reporting posterior results. Guide MCMC, a sampling method for estimating distributions, check convergence, and assess sensitivity for hierarchical models and small datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing priors for models with small datasets
• Implementing hierarchical models in Stan or PyMC
• Investigating MCMC convergence problems

Build and evaluate Bayesian models with PyMC and ArviZ, from choosing initial assumptions to checking predictions. The workflow covers sampling diagnostics, model comparison, and reports that explain results with uncertainty intervals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose and check prior distributions
• Investigate sampling and convergence problems
• Compare hierarchical or regression models

Run empirical and causal analyses in Python with StatsPAI, from stating assumptions to estimating effects and checking how results change under alternative choices. Produce diagnostic figures, regression tables, and replication bundles for economics, public health, or machine learning studies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating policy or intervention effects
• Comparing public health effect estimates
• Examining how effects differ across groups

Analyze JSON, syslog, or Nginx logs to group similar errors, count occurrences, and identify busy error periods. Filter by severity or time range, then view the report in the terminal or export it as JSON.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Grouping repeated errors to investigate causes
• Finding hours or dates with error spikes
• Analyzing logs within a specific time range

Choose approaches for prime number problems, including primality testing, factorization, and prime distribution. The skill outlines relevant algorithms and theorems, with commands for symbolic calculation and checking mathematical claims.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a primality testing method
• Selecting a factorization approach
• Studying prime distribution and gaps

Work through information theory problems about channel capacity, the maximum information a channel can carry. Organize channel models, calculate mutual information, and choose formulas or iterative methods to examine capacity and input distributions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate capacity for standard channel models
• Organize mutual information calculations
• Explore input distributions that maximize capacity

Turn data into charts suited to comparisons, trends, distributions, relationships, or shares. Add clear titles, units, and accessible colors, then export a static PNG or interactive HTML with hover details and zoom controls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Charting monthly revenue trends
• Comparing totals across regions or categories
• Examining order-size distributions

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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