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Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

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• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

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.

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• 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

Process calibrated one-dimensional NMR signals into spectra, candidate peaks, and signed integration results. Supports manual phase adjustment, baseline correction, and recorded processing settings so results can be reviewed and reproduced.

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• Convert raw one-dimensional NMR signals into spectra
• Check peak positions against reference signals
• Review phase and baseline corrections

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

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• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Use Binance Onchain Pay APIs to prepare cryptocurrency purchases and transfers to external wallets. Retrieve payment options, exchange rates and fees, create payment links, and check order status with automatically signed requests.

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• Prepare a crypto purchase with delivery to an external wallet
• Check payment methods and estimated fees
• Create a payment link for a merchant checkout

Trace uploaded files through extraction, conversion, parsing, and reconstruction of saved objects in a CTF sandbox, an isolated competition environment. Preserve original and derived files, distinguish visible validation from backend behavior, and document the smallest reproducible processing chain.

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• Trace an uploaded file to its backend effect
• Investigate archive extraction and preview generation
• Compare visible validation with backend file handling

Integrate PayPal into online checkout flows with one-time payments, subscriptions, refunds, and payment notifications. Includes client-side buttons, server-side payment handling, and sandbox testing to check the purchase flow before using live credentials.

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• Adding PayPal to an online checkout
• Setting up subscriptions and recurring billing
• Implementing refunds and payment notifications

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

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• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Design and improve Unity games that process large numbers of entities. Learn ECS, an approach that separates data from behavior, and apply Jobs and Burst patterns to organize parallel processing and address CPU performance bottlenecks.

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• Designing Unity games with thousands of entities
• Converting object-oriented game logic to ECS
• Addressing CPU performance bottlenecks

Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.

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• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

Plan voice AI systems by comparing direct speech processing with separate transcription, language generation, and speech synthesis stages. Covers response delays, detecting when people speak, interruptions, and noise handling to guide conversational interface design.

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• Compare architectures for a voice AI system
• Review delays across conversation processing stages
• Design handling for interruptions during speech

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

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• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths

Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

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• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Store conversations, agent activity, and feedback in cognee sessions, then carry them into long-term memory. This skill explains improve(), its processing stages, result statuses, and settings for diagnosing skipped work or controlling automatic runs.

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• Saving conversations and agent activity by session
• Moving session content into long-term memory
• Investigating skipped or blocked processing stages

Investigate and fix agent behavior in TradingAgentsCN by checking active database prompt templates, prompt assembly code, and output processing. Apply targeted changes and verify them with focused tests, template checks, and workflow logs.

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• Investigating prompt changes that do not affect runtime behavior
• Synchronizing database templates with prompt assembly code
• Tracing incorrect dates or price references in position analysis

Process AI model responses through a specified Google Model Armor template before sending them to users. Supply response text directly, pipe output from another command, or provide a JSON request for outbound safety processing.

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• Check model responses before showing them to users
• Pipe model command output into safety processing
• Process response text with a specified template

Process large email, document, and transcript archives by routing items to AI models according to their importance. Rule-based privacy checks run before model calls, while extracted information becomes linked knowledge pages with progress checkpoints.

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• Extract useful information from large email archives
• Organize decisions and advice from meeting transcripts
• Route reports to suitable models by importance

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.

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

Write Workflow scripts that divide research, reviews, or migrations among agents. The reference explains parallel and staged processing, independent verification, failure handling, budgets, and resuming runs so the script has a clear execution structure.

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• Design a review workflow with independent verification
• Divide broad research among agents and combine findings
• Plan staged processing for a large migration

Add or troubleshoot Table v9 features such as sorting, filtering, pagination, selection, merged cells, and column layout. Guidance helps check feature registration, dependencies, and row-processing settings, with references selected for the task.

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• Adding sorting, filtering, and pagination
• Investigating missing table APIs or state
• Configuring row selection, cell selection, or merged cells

Build table data processing with TanStack Table v9 while keeping data and column definitions stable. The skill guides type inference, which determines types automatically, row identity, optional features, and checks for missing APIs.

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• Set up the foundation for table data processing
• Troubleshoot column type inference
• Investigate missing APIs or optional features