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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

Plan large sets of search-focused pages using templates and data, such as location, comparison, and integration pages. Define unique content, URL patterns, internal links, launch checks, and monitoring metrics for a structured publishing plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning service pages for multiple locations
• Designing product comparison or integration page templates
• Expanding a directory using available data

Build or update Figma screens from code or descriptions using an existing design system, a shared set of components and visual rules. Assemble sections with reusable components, colors, spacing, and styles, then check their appearance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recreate an application page in Figma
• Build a landing page with shared components
• Update a Figma screen to match code

Write, revise, and review beads user documentation using consistent terminology, explanations, diagrams, and navigation. Follow project rules for generated pages and release alignment, with checks for links, freshness, and documentation consistency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Write user guides with consistent beads terminology
• Simplify confusing explanations with diagrams and examples
• Reorganize pages, navigation, links, and redirects

Prioritize product or business assumptions using an impact and risk matrix. Sort them into implementation, deferral, rejection, or testing, then propose experiments for assumptions that need validation, using actual behavior and clear success metrics and thresholds.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding which assumptions to test first
• Comparing assumptions by impact and risk
• Defining experiments and success thresholds

Develop applications with the Claude API using examples from Anthropic's official cookbooks. Explore document summarization, image analysis, document retrieval, external tool calls, and prompt caching through practical Python code and implementation guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning basic Claude API integration
• Building an assistant that searches documents
• Extracting text and information from images

Evaluate Google Cloud workloads against built-in best practices or custom organizational rules using Workload Manager. Use public client libraries or the REST API to run checks, review violations and scanned resources, and export results to BigQuery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate workloads against Google Cloud best practices
• Inspect rules for SAP and SQL Server workloads
• Run evaluations using custom organizational rules

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Assess how changes, failures, or paused updates to a BigQuery table or view affect downstream assets. Trace data lineage, the relationships showing where data flows, and summarize dependent tables, dashboards, and processes with direct and indirect impacts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess downstream effects of a table failure
• Check dependencies before changing or deleting a view
• Evaluate the impact of pausing data updates

Find content losing search impressions and clicks using Google Search Console. Rotate through pages based on refresh history, update dated copy, statistics, and internal links, and open a pull request for human review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find articles losing search impressions and clicks
• Refresh outdated copy and statistics in small weekly batches
• Fix internal links to renamed or retired pages

Investigate a codebase, reproduce bugs, implement requested changes, and verify the results using Leon’s file, search, and shell tools. Follow project instructions, preserve existing user changes, and report checks and unresolved issues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reproduce and fix a software bug
• Implement a feature using existing project patterns
• Check a UI against supplied references

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Build Vue 3 applications using JavaScript and JSDoc comments that describe data types. Create components and reusable logic, configure Vite, routing, and state management, and check annotations and behavior with ESLint and Vitest.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build Vue 3 interfaces using JavaScript
• Add JSDoc type hints to components and shared logic
• Migrate Vue 2 Options API code to Composition API

Handle order refunds by checking the supplied refund policy first, calling refund-order with an eligible order’s ID, and using the provided confirmation template to respond to the customer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether an order qualifies for a refund
• Process an eligible refund using an order ID
• Respond using the supplied refund confirmation template

Diagnose paid advertising performance using account evidence, financial constraints, and experiments. Draft ranked changes to budgets, bids, or ad delivery, and apply supported operations only after approval and state checks, retaining a way to reverse them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing budget allocation and bids
• Planning ad pauses and creative rotation
• Assessing search terms before adding negative keywords

Generate talking-person videos from a portrait or an existing video and an audio track, aligning lip movements, expressions, and body motion with speech. It supports long-form generation and can create speech from text using Kokoro-82M.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a talking-person video from a portrait and recorded speech
• Generate a video using existing footage and a replacement audio track
• Create a talking-person video from text using speech synthesis

Run repeated code reviews and fixes using an installed reviewer chosen by the user. The workflow tracks findings across rounds, checks that edits stay within scope, and reports whether blocking issues were resolved or further decisions are needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Repeat reviews and fixes for a module
• Improve code using a specific installed reviewer
• Keep automated edits within an agreed file scope

Assess customer acquisition channels using acquisition costs, lifetime customer value, payback time, retention, and growth capacity. Compare channels and receive recommendations to increase spending, improve performance, pause activity, or continue with a spending cap.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether a paid advertising channel deserves more budget
• Compare content, outbound sales, and partner referrals
• Choose whether to improve or pause an underperforming channel

Check article and file availability through the NCBI PMC Open Access service using supported article identifiers. Receive a concise metadata summary, with the option to save the original XML response when you need the full service output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check Open Access availability using a PMCID
• Look up availability using a supported DOI
• Save the original XML response for later use

Create original generative art with p5.js, using code to produce patterns and motion. Receive a written artistic concept and an interactive HTML viewer with adjustable parameters, reproducible random variations, seed navigation, and PNG downloads.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create art from particle motion and flow fields
• Explore patterns by adjusting sliders
• Reproduce a favorite variation with the same seed

Apply related edits across several files using a unified diff, a file describing code changes. Check the patch with Git before applying it, and rebuild rejected patches using the current file contents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Applying one related change across three or more files
• Handling repetitive edits or whitespace-sensitive changes
• Reusing a patch from a PR or git diff

Review existing tests using 19 research-backed test smell categories, meaning patterns that can weaken test quality. Get findings ranked by demonstrated risk, with precise code evidence and fixes suited to the test framework in use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Request a test review using formal academic categories
• Prioritize test problems by their demonstrated risk
• Find causes of flaky tests or misleading passes

Turns a copy-edit report into conflict markers that display original and suggested text together in the source file. Review each proposed change using VS Code or Positron's merge interface, optionally limiting edits to one Quarto section.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing proofreading suggestions individually
• Applying style edits selectively
• Reviewing changes within one Quarto section

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.