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Plan backend services and APIs, from service responsibilities and interface definitions to authentication, failure handling, monitoring, and rollout. Produces architecture diagrams, API specifications, and documented tradeoffs based on business and operational requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an order management API
• Defining responsibilities across multiple services
• Planning retries and timeouts for external integrations

Create and refine draw.io diagrams by editing XML files, adjusting labels, arrows, and spacing, and using AWS icons. Export transparent PNG images for slides, with guidance for readable Japanese text and consistent layouts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an AWS architecture diagram
• Fix Japanese labels and overlapping arrows
• Export transparent PNG diagrams for slides

Improve resume layouts with consistent fonts, spacing, section order, and clear headings. The skill recommends formatting that supports quick reading by recruiters and text parsing by applicant tracking systems, with guidance on file formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cleaning up a cluttered resume
• Reviewing formatting for applicant tracking systems
• Standardizing fonts, dates, and headings

Build or rewrite resume sections for a target role and career stage. Get tailored summaries, organized skills, achievement-focused experience entries, and guidance on education, optional sections, and the order in which to present them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rewriting a summary for a career change
• Organizing skills for a technical role
• Presenting work experience through achievements

Improve resumes for software engineering, technical product management, data, and DevOps roles. Organize technical skills, connect achievements to business results, and highlight projects in a format suited to applicant tracking systems, software used to process job applications, and recruiters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize technical skills for a target role
• Rewrite experience to show technical and business impact
• Choose projects to highlight during a career change

Compare a job posting with your experience to score how well the requirements match. Identify strengths, missing qualifications, and potential concerns, then plan application priorities, resume adjustments, and points to address in a cover letter.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare a job's requirements with your experience
• Prioritize jobs before preparing applications
• Plan resume adjustments and cover letter points

Design and implement machine learning systems for production use. Cover data preparation, training, prediction services, model comparisons, monitoring, and retraining, with attention to reliability, response time, resource costs, and both technical and business evaluation metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a real-time recommendation service
• Comparing model versions with A/B testing
• Detecting changes in data and model performance

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Analyze business data, test hypotheses, and build predictive models with statistical and machine learning methods. Get support across data exploration, experiment design, visualization, result validation, and planning model deployment and monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyzing customer churn and predicting customers at risk
• Designing an A/B test and evaluating its results
• Forecasting demand for inventory planning

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building scheduled data collection and transformation workflows
• Designing a data warehouse for analysis
• Synchronizing database changes in real time

Connect cognee to model providers, databases, storage services, and development tools. Guides configuration of language models and embeddings, the numerical representations used for search, including the separate settings each requires.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switching model providers or using Ollama locally
• Connecting cognee to Postgres or Neo4j
• Storing cognee data in S3

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Implement Zoom webhooks, HTTP notifications sent when events occur, such as meetings starting or recordings becoming available. Use reference guidance for event selection, subscriptions, signature verification, endpoint validation, delivery handling, and retries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Receiving notifications when cloud recordings are ready
• Choosing meeting and participant events to subscribe to
• Implementing webhook signature verification

Guide server-side Zoom integrations using Zoom Rivet SDK in JavaScript or TypeScript. Consult patterns for authentication, incoming event notifications, typed API calls, multiple modules, and webhook processing on AWS Lambda.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing Zoom authentication and token handling
• Building a Team Chat bot with API integration
• Combining multiple Zoom modules in one server

Helps plan Zoom integrations by comparing APIs, SDKs, app types, authentication, and permissions. Guides the choice between repeatable API automation and MCP, a protocol for connecting AI agents to tools, and identifies relevant implementation skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a Zoom SDK for embedding meetings
• Compare REST API automation with AI agent tooling
• Plan meeting creation, authentication, and event handling

Build custom video calling experiences with Zoom Video SDK. The skill guides session joining, audio and video control, participant event handling, and custom interfaces, with references for web, mobile, and desktop implementations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a video calling app with a custom interface
• Implementing session joining and token authentication
• Updating video displays as participants join or leave

Review a vendor’s agreements across connected contract systems, customer records, email, document storage, and chat. Consolidate signing status, key terms, missing agreements, renewal deadlines, and obligations that may continue after expiration into one report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check agreement coverage before onboarding a vendor
• Review contracts before a vendor renewal
• Identify missing master, data processing, or work agreements

Turn repeated workflows into reusable Picoclaw skills, or update and review existing ones. Organize SKILL.md instructions, scripts, reference documents, and output assets, then check the structure and test representative tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a repeated workflow into a reusable skill
• Updating or reviewing an existing SKILL.md
• Organizing scripts, references, and templates

Set up, debug, and extend PicoClaw using its own commands and configuration structure. Get guidance on model authentication, external tool connections, scheduled tasks, and how conversations are routed and stored.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up models and authentication in PicoClaw
• Configuring and troubleshooting MCP server connections
• Setting up recurring tasks

Deploy FastGPT on your own server using its official installation script and Docker Compose, which runs its services together. Check service health and access, investigate deployment problems, and receive the access URL, initial login details, and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up FastGPT on your own server
• Checking service health after deployment
• Investigating port conflicts or container startup failures