'quality' 검색 결과

Analyze Neuropixels neural recordings with SpikeInterface, from loading and preprocessing to motion correction and spike sorting, which separates signals by neuron. Calculate quality metrics, review candidate units, and export plots and data for further inspection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect and preprocess SpikeGLX, Open Ephys, or NWB recordings
• Check and correct movement-related drift before spike sorting
• Evaluate and select units using quality metrics

Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan fallback procedures for model provider outages
• Investigate declining answer quality after a deployment
• Prepare emergency controls for token spending spikes

Review CLAUDE.md files, which provide project instructions to Claude Code. Receive a scored quality report covering commands, structure, and outdated guidance, then approve focused edits that keep the instructions concise and useful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing project instructions for Claude Code
• Updating outdated build and test commands
• Reviewing instructions across multiple packages

Review code for security, performance, quality, and maintainability. Get findings organized by severity, with file locations, explanations of impact, and example fixes, plus code metrics and complexity comparisons for refactoring reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a pull request before merging
• Checking code for security risks
• Finding performance improvement opportunities

Evaluate LLM applications using automated scores, human reviews, and model-based judging. Build repeatable checks to compare models or prompts, assess answer quality, and detect declines in performance before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models and prompts
• Checking quality after prompt changes
• Detecting performance declines before deployment

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Analyze threat intelligence feeds to extract indicators of compromise, observable clues of possible attacks, and their campaign context. Assess source quality, standardize formats, remove duplicates, and enrich indicators for distribution to security monitoring systems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing the quality and relevance of threat intelligence feeds
• Standardizing indicators into STIX 2.1
• Removing duplicates and adding attack campaign context

Review websites for security, browser compatibility, and code quality. Identify issues in HTTPS settings, third-party libraries, HTML, and error handling, then apply concrete improvements using checklists and implementation examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing website security settings
• Checking third-party libraries for vulnerabilities
• Replacing deprecated browser APIs

Design visually distinctive websites, landing pages, and portfolios as React components, reusable pieces of a web page. Use industry references to guide typography, color, layout, motion, and relevant 3D visuals that fit the brand.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a brand website with a distinctive visual identity
• Design a landing page around a product
• Create a portfolio with an original layout

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Reviews pull requests for bugs, error handling, code quality, and style consistency. Organizes feedback into an overview, must-fix issues with file locations, improvement suggestions, and positive notes so reviewers can distinguish urgent problems from refinements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bugs that must be fixed before merging
• Reviewing TypeScript types and asynchronous code
• Checking naming, imports, and leftover debug statements

Routes regulatory and quality management requests to one of 15 specialist skills. Helps HealthTech and MedTech teams select the right workflow for audits, medical device regulations, information security, privacy, or AI compliance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a skill for ISO 13485 audit preparation
• Routing questions about EU AI Act risk classification
• Finding the right workflow for GDPR requests

Test marketing prompts against shared cases, score required content, prohibited phrases, and output format, and compare alternatives. Keep version history and diffs, use reusable templates, and check that prompt or model updates preserve output quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare two prompts for ads, email, or social content
• Keep prompt versions and review changes
• Check output quality after prompt or model updates

Review completed AI work by scoring task difficulty and execution quality separately. A fixed scoring matrix, arguments for higher and lower ratings, and saved score history help examine weaknesses and detect repetitive scoring patterns across sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate a completed feature implementation
• Review the quality and coverage of a bug fix
• Reflect on work completed during a session

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Review a development milestone using sprint reports, feature status, bugs, and risks. Summarize completion, quality, and delivery pace, then recommend whether to proceed, proceed with conditions, or stop, with scope choices and action items.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing progress before a milestone deadline
• Identifying blockers and quality risks
• Choosing features to protect or defer

Trace where BigQuery and Cloud Storage data comes from and where it goes. The skill summarizes table or column lineage, meaning data origins and movement, in a readable Markdown report to support data quality investigations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating data origins during a quality issue
• Summarizing data sources and destinations
• Tracing the flow of a specific column

Audit a website or individual page across technical SEO, content quality, performance, images, and AI search readiness. Route work to specialized checks and receive a combined report with prioritized recommendations tailored to the site's business type.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit SEO issues across an entire website
• Investigate problems on a specific page
• Plan SEO improvements for a business type

Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing data ingestion and transformation workflows
• Processing only new or changed records
• Adding data quality checks between processing stages

Download individual YouTube videos with a chosen quality, file format, and destination folder. Save video as MP4, WebM, or MKV, or download only the audio as MP3 using the included Python script.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving a video at a chosen resolution
• Downloading only the audio as MP3
• Choosing a video format and destination folder

Screen listed companies using seven financial measures covering profitability, cash flow, debt payments, and share dilution, which reduces existing shareholders' ownership. Produce comparison tables showing exclusions, exemptions, borderline results, and missing data to guide further research.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening several listed companies before deeper research
• Comparing financial quality within an industry
• Reviewing index constituents or investment themes

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

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