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Found 6,510 Skills
Routes PubNub questions to the correct documentation source, MCP tool, and specialist skill. Classifies intent (chat vs non-chat, conceptual vs implementation, runtime testing vs analytics) and points the agent to the right next step. Use when a user mentions PubNub for the first time, asks "where do I start", "which docs", "what should I use", or any time the appropriate next skill is unclear.
Input template configuration for Elastic integrations. Covers agent stream templates (agent/stream/*.yml.hbs) for all non-CEL input types: HTTPJSON, AWS S3, CloudWatch, Azure Blob, Azure EventHub, GCS, GCP Pub/Sub, TCP, UDP, HTTP Endpoint, Filestream, Logfile, Journald, Winlog, and WebSocket. For CEL input programs, use the cel-programs skill instead.
Fix the visual diffs that have been reviewed and rejected on a Meticulous test run, following their review comments if given. Use when a user has reviewed the results of a test run and is handing off to an agent to implement the fixes.
Stop coding agents from shipping generic UI. Use UIZZE's 800,000+ real web and iOS screens to build product-specific interfaces, define a design contract, cover required states, and run a hard finish gate. Use for web or iOS UI design, implementation, redesign, critique, and pre-ship review in Codex, Claude Code, Cursor, Copilot, and other coding agents.
Guide AI agents through TypeScript coding best practices including type safety, error handling, code organization, and architecture patterns. This skill should be used when generating TypeScript code, reviewing TypeScript files, creating new TypeScript modules, refactoring JavaScript to TypeScript, or when the user asks about TypeScript patterns, types, or coding standards. Keywords: typescript, types, coding standards, best practices, type safety, generics, architecture, refactoring.
Expert Python developer specializing in Python 3.11+ features, type annotations, and async programming patterns. This agent excels at building high-performance applications with FastAPI, leveraging modern Python syntax, and implementing comprehensive type safety across complex systems.
General-purpose agent for researching complex questions and executing multi-step tasks. Versatile problem-solver that combines research capabilities, analytical thinking, and systematic task execution. Use for complex research projects, multi-step workflows, cross-domain analysis, and tasks requiring multiple tools and approaches.
LangGraph parallel execution patterns. Use when implementing fan-out/fan-in workflows, map-reduce over tasks, or running independent agents concurrently.
Comprehensive tool for finding and analyzing DeFi yield opportunities across protocols, chains, and asset types. Use when users want to earn yield on crypto assets (staking, lending, liquidity farming, aggregators). Supports exhaustive parallel research using librarian agents, direct web searches, and GitHub repository analysis.
Comprehensive research and synthesis agent specializing in multi-source information gathering, critical analysis, and integrated knowledge synthesis. Excels at complex research projects requiring systematic investigation across domains, evidence evaluation, and coherent narrative construction.
Interactive collaborative analysis with documented discussions, inline exploration, and evolving understanding. Serial execution with no agent delegation.
Show status of all features in .dev/. Scans feature folders using parallel agents, generates a status report, and offers to archive completed features.