'pip' 검색 결과

Design machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an end-to-end machine learning pipeline
• Building reproducible training workflows
• Comparing models before production deployment

Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Design CI/CD pipelines that move applications through development, staging, and production. Define approval and security checks, gradual release strategies, health checks, and rollback procedures, and investigate gates or deployments that fail to progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing CI/CD pipelines across multiple environments
• Introducing canary or blue-green releases
• Investigating stalled approvals and release gates

Prepare a sales forecast narrative from CRM records or an uploaded pipeline export. Organize deal amounts and counts by forecast category, explain changes since the previous review, and highlight closing risks and support needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for a quarterly sales forecast meeting
• Reviewing deal outlooks in a manager one-on-one
• Building a forecast narrative from a pipeline export

Review revenue plans through six questions covering pipeline coverage, win rates, customer revenue retention, sales rep ramp time, discounts, and lead sources. Produce a structured assessment of gaps and three concrete actions before committing targets or expanding sales.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review assumptions before committing a quarterly revenue target
• Find pipeline stages where win rates are weakening
• Separate retention, contraction, and expansion in customer revenue

Retry commands that fail with network errors through proxychains4, a tool that routes connections through a proxy. The skill also guides proxy configuration and troubleshooting when downloads, package installations, or Git operations cannot connect.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrying a failed Git clone through a proxy
• Installing packages when pip or npm cannot connect
• Troubleshooting download timeouts and DNS failures

Find repeated tasks in recorded activity or starred examples, then describe their steps, exceptions, and human decisions. Assess a small automation candidate using captured evidence, available tools, permissions, and clear completion criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding recurring tasks in activity records
• Reviewing starred examples for workflow patterns
• Assessing a small automation candidate

Review recorded activity over a chosen period to understand sustained work and app switching. Get one evidence-based pattern and one small experiment for the next day, with uncertainty stated and no invented productivity scores.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing sustained work over a chosen period
• Understanding app and window switching patterns
• Choosing a small experiment for tomorrow

Prepare a project handoff using recent work records and checks of current materials. Give the recipient a verified status, accessible artifact locations, blockers, and concrete next steps, while distinguishing delivered work from drafts and intentions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Handing a project over to another person
• Preparing context before resuming paused work
• Collecting artifact locations and supporting links

Turn recorded or starred procedures into reusable guides based on captured evidence. Organize prerequisites, steps, expected results, branches, and final checks, while clearly marking missing details and removing private information from examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning recorded demonstrations into procedure guides
• Documenting starred work for future reuse
• Preparing instructions for colleagues

Analyze a single discovery interview to understand actual behavior, needs, workarounds, and open questions. Receive a concise account tied to sources, separating participant actions from hypothetical intentions, interviewer suggestions, and interpretation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarize actual behavior from an interview
• Identify needs and current workarounds
• Prepare follow-up research questions

Build a daily or weekly worklog from recorded activity and checked outcomes. Separate plans, observed work, and verified completion, then summarize useful results and unfinished tasks with source links and clearly marked gaps or estimates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a daily work report
• Reviewing weekly outcomes and unfinished tasks
• Summarizing work time using recorded active minutes

Review a specific meeting’s notes and transcript to summarize decisions, commitments, named owners, and stated deadlines. Keep proposals and completed work separate, flag recording gaps, and draft a concise follow-up message when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing decisions and commitments after a meeting
• Checking explicitly stated owners and deadlines
• Distinguishing agenda proposals from agreed work

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Manage prospects and clients for GEO services, which help sites prepare for AI search visibility. Keep contact details, audit history, notes, and monthly deal values in a local JSON file, and summarize sales stages and potential revenue.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register prospects and review their sales stages
• Keep audit history and dated interaction notes together
• Summarize won contracts and potential monthly revenue

Set up Python projects and standalone scripts with uv for dependencies, ruff for code checks and formatting, and ty for type checking. Configure tests and project files, or migrate existing workflows when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting a Python project or reusable package
• Writing a standalone script with external dependencies
• Configuring formatting, type checks, and tests

Inspect CircleCI runs, identify failing steps, and validate configuration locally using the CircleCI CLI, its command-line tool. It also guides targeted reruns and pipeline launches, reporting results, permission gaps, and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed CircleCI runs
• Validating configuration before committing changes
• Rerunning a specific workflow or job

Turn articles, notes, or code history into talk preparation materials, including angles, titles, scripts, slide drafts, and revision sheets. Coordinate the stages with user checkpoints, or run an individual stage again.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a talk from articles or notes
• Build an experience report supported by Git history
• Develop a script after choosing an angle and title

Set up Python projects and manage packages, isolated environments, and Python versions with uv. Includes migration guidance and lockfiles that record dependency versions, plus workflows for consistent installations in CI and Docker.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new Python project
• Migrating from pip or Poetry to uv
• Keeping dependency versions consistent across environments

Set up Python projects and manage packages, isolated environments, and Python versions with uv. Includes migration guidance and lockfiles that record dependency versions, plus workflows for consistent installations in CI and Docker.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new Python project
• Migrating from pip or Poetry to uv
• Keeping dependency versions consistent across environments

Provides GitHub Actions and GitLab CI templates for automating tests, builds, and releases. It adapts configurations to project types and covers caching, parallel jobs, credential handling, and controls for production deployments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up CI/CD for a frontend or backend project
• Automating tests and releases for a library
• Running checks only for changed parts of a monorepo

Design or revise Marin experiment pipelines by connecting reusable tasks and their outputs in a dependency graph. Organize shared implementations separately from experiment settings, then check that the workflow connects the intended inputs and stages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an experiment workflow entrypoint
• Connecting pipeline stages and outputs
• Separating reusable code from experiment settings

Build and evaluate machine learning models in Python with scikit-learn. Covers classification, numerical prediction, grouping similar data, preprocessing, model comparison, and pipelines that combine data preparation and prediction into a repeatable workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build classification or numerical prediction models
• Group customers with similar characteristics
• Compare models using cross-validation

Design and automate machine learning workflows from data preparation through training, validation, deployment, and monitoring. Provides guidance and templates for reproducible experiments, quality checks, gradual model rollouts, and restoring a previous model when problems occur.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a new machine learning pipeline
• Creating reproducible training workflows
• Adding quality checks before model deployment