'common' 검색 결과

Build targeted company and contact lists with Common Room's Prospector. Refine criteria through conversation, distinguish new companies from existing accounts, and prioritize existing accounts using available activity signals and scores.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a new target company list by industry, size, and location
• Prioritize existing accounts using activity signals
• Find companies with attributes similar to a reference company

You are a member of a Commonly workspace — a shared space where humans and AI agents from any origin collaborate in pods (chat rooms with memory). Use this whenever you are connected to Commonly via the commonly_* MCP tools: to read what's happening, post, remember things across sessions, react, DM other agents, and work the task board. Load it the moment you see any commonly_* tool available.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Research a contact in Common Room using an email, social handle, or name. Bring together available background, activity, website visits, and company context, then suggest conversation starters supported by actual engagement signals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Researching a contact before a sales conversation
• Reviewing recent activity and website visits
• Tracking changes in a contact's engagement

Research sales accounts using Common Room data to summarize company details, buying signals, ownership, and key contacts. Answer specific questions directly or prepare a broader briefing with evidence-based next steps, clearly identifying missing information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a company briefing before a sales conversation
• Check buying signals and recent account activity
• Find account owners and key contacts

Prepare for customer meetings with a concise brief built from Common Room data. Combine company activity, attendee profiles, and previous conversations into specific talking points, likely discussion topics, and a recommended meeting outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for a first sales conversation
• Reviewing usage and risks before a renewal meeting
• Checking attendee roles and previous conversations

Review JavaScript module usage and convert CommonJS code to ES modules using import/export. Inspect source code and browser execution, explain how unused code can be removed during builds, and describe how to verify changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting require() calls to import statements
• Migrating module.exports to ES module exports
• Reviewing how browsers load modules

Draft personalized sales emails, call scripts, and LinkedIn messages using Common Room activity data and recent public news. Each draft connects a specific prospect signal to the outreach, with notes explaining the chosen angle and any assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting outreach based on a prospect’s recent activity
• Preparing email, phone, and LinkedIn outreach together
• Finding the right contact at a target company

Use this skill when producing a polished, Commonly-branded deliverable (.docx brief / memo, .xlsx data matrix, .pptx deck) and you do not have specific brand guidance from the user. Trigger on: 'write me a brief', 'one-pager', 'memo', 'data sheet', 'status matrix', 'short deck', 'summary deck', 'closing slide', 'final deliverable'. Routes to `officecli merge` with one of three pre-built starter templates that already carry the Commonly palette, fonts, and structure — so you populate content with one merge call instead of 30 individual `officecli set` commands. DO NOT use for fundraising decks (use `officecli-pitch-deck`), academic papers (use `officecli-academic-paper`), or financial models (use `officecli-financial-model`).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Find and download public cancer imaging data from NCI Imaging Data Commons. Select CT, MR, PET, or pathology datasets using metadata, view images in a browser, and check licenses and citations before using the data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find cancer images by disease, modality, or anatomical site
• Download selected DICOM image series
• View radiology and pathology images in a browser

Prepare for the next seven days of customer and prospect calls with one chronological briefing. Gather company and attendee research, highlight urgent account signals and notable news, and suggest meeting objectives using Common Room as the primary source.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for a week of customer calls
• Reviewing companies and attendees before meetings
• Spotting renewal risks and expansion opportunities

Implement shared authentication for Azure Communication Services in Java, including automatic renewal of access tokens, the credentials used to access services. It also guides handling identifiers for ACS users, phone numbers, and Microsoft Teams users.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up authentication for Chat or Calling clients
• Renew access tokens for long-running clients
• Handle ACS, phone number, and Teams user identifiers

Review Python code for common mistakes in error handling, asynchronous operations, type annotations, and tests. Use a practical checklist and correction examples to guide code reviews, investigate bugs, and improve existing implementations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing Python code before a merge
• Investigating bugs caused by common coding mistakes
• Refactoring legacy Python code

Create or improve a company-specific data analysis skill by gathering analysts’ knowledge and examining database structures. Document business terms, metric formulas, table relationships, and common query patterns in reusable reference files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a data analysis skill for a company
• Document metric formulas and standard exclusions
• Add knowledge about a new business domain

Investigate code errors through a structured process using reproduction steps, logs, and testable explanations. The workflow guides fault isolation, fixes, and follow-up checks, with common error patterns for Python, JavaScript/TypeScript, and Bash/Zsh.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating an error using logs and reproduction steps
• Finding why code broke after a recent change
• Troubleshooting common shell script errors

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill whenever the user is doing hands-on DOCA programming on a BlueField DPU or ConnectX NIC and needs the foundation primitives every per-library context rests on — walking the doca_ctx lifecycle, discovering doca_dev / doca_devinfo and gating on doca_*_cap_* before trusting a feature, wiring doca_mmap / doca_buf_inventory / doca_buf for zero-copy I/O across libraries, driving doca_pe for completions, or DOCA Log's two-tier (--sdk-log-level vs app-side) model. Trigger even when the user does not say "DOCA Common" — typical implicit phrasings include "my tasks submit but nothing completes", "DOCA_ERROR_BAD_STATE from doca_ctx_start", "--sdk-log-level does nothing for my DOCA_LOG_DBG lines", "share a buf between doca_dma and doca_rdma", or "crashes far from the offending line". Refuse and route elsewhere for per-library questions in isolation (load doca-flow / doca-rdma / doca-eth alongside), installing DOCA (doca-setup), or doc lookup (doca-public-knowledge-map).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review session corrections, trigger misses and recurring agent mistakes to propose targeted skill-library changes. Use for retrospective, rework or routing-gap analysis before registry edits; load +common/common-learning-log for redacted evidence.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

의료 앱의 환자·직원·재무 기밀 정보를 분류하고, 의료기관별 접근 제어와 감사 기록을 설계하며 로그·주소·브라우저 저장소의 정보 유출을 점검합니다.

• 환자 기록을 다루는 코드를 점검합니다
• 의료기관 간 접근 제어를 설계합니다
• 기밀 정보의 조회와 변경을 기록합니다