1.6K skills · sorted by relevance
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.
Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.
Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.
Build AI features for Dart and Flutter applications with the Genkit Dart SDK. Provides code and documentation for conversational agents, reusable processing flows, prompt files, model integrations, interactive interfaces, and debugging through recorded execution traces.
Generate Python code to clean, transform, analyze, visualize, and model BigQuery data with BigQuery DataFrames. The skill keeps processing in the cloud and uses familiar table operations, with guidance for previews, schema checks, and machine learning.
Helps developers send conversion and event data to Google Ads, Google Analytics, and Floodlight through the Data Manager API. It covers destination setup, data formatting, validation, migration from other APIs, and checking processing results.
Add formatting, arithmetic, text processing, and translated labels to json-render specifications. This skill explains ready-made directives, or value-processing rules, how to combine them, and how to define custom rules for application-specific transformations.
Manage LangBot bots and processing workflows through MCP, a standard for connecting agents to tools. Configure authenticated access, inspect resources, update supported settings, and investigate execution histories, logs, and model request diagnostics.
Design Redis caching, leaderboards, notifications, and message processing. Helps choose data structures, write Lua scripts for operations that run together without interference, and plan expiration, memory use, and persistence settings.
Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.
Turn plain-language requirements and optional API documentation into OpenShell sandbox network policies in YAML. Define allowed destinations, programs, and API operations, with optional middleware settings for inspecting or transforming traffic and controlling what happens when processing fails.
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.
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.
Build and debug Java applications with Spring Boot 3.x. This skill guides application design, API implementation, database query tuning, authentication, and testing, including WebFlux applications that process requests without blocking.
Build Rails 7+ applications with partial page updates, real-time connections, and background jobs. The skill implements Hotwire, Action Cable, and Sidekiq features, improves Active Record database queries, and writes RSpec tests.
Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.
Create print-friendly slides explaining system architecture or a pipeline, a sequence of processing steps. The layout combines a cream background, blueprint grids, process boxes, rust-red callouts, and dashed feedback loops.
Build a new agent skill from a structured template. Copy the directory and edit SKILL.md to document its purpose, inputs, outputs, processing steps, precautions, and examples, replacing placeholders with concrete content.
Screen documents and external URLs before they enter a processing workflow. Check blocked domains and document permissions, then produce a pass or reject decision, a risk report, and a tracking ID for later review.
Continue Python tasks when sandbox execution fails by saving code as a script and running it through run_shell. The workflow covers dependencies, execution checks, and output verification for PDFs, image processing, data analysis, and file operations.
Continue Python tasks when the sandbox cannot start by switching to run_shell. The workflow covers short scripts, saved script files, library checks, longer timeouts, and output verification for tasks such as PDF generation and data processing.
Hand complex document generation or data processing to shell_agent when direct tools repeatedly fail. Define the required output, let the tool choose libraries and recover from errors, then verify the resulting files and content.
Recover failed Python tasks by saving the code as a script and running it through the system shell. The workflow covers spreadsheet generation, data processing, output verification, and troubleshooting missing packages, paths, or permissions.
Validate spreadsheet sources before running Python scripts to transform data, update multiple sheets, or apply formatting. Check required data first, try backup sources when needed, and report errors clearly if processing cannot proceed.