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Found 13,656 Skills
Drive iOS Simulator and Android emulator/device screens for AI agents. Use when asked to automate a simulator or emulator, tap/swipe/type on a device, describe UI, take a screenshot, or interact with a mobile app.
Use when explicitly invoked or when a concrete latency constraint requires minimizing wall-clock agent time without reducing accuracy.
Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).
Code comment hygiene for AI coding agents: remove generic AI-slop comments, keep the valuable ones, never touch the code.
Use only when the user explicitly types `/orchestrate <goal>` to decompose a large task, spawn a tree of parallel cloud-agent workers/subplanners/verifiers via the Cursor SDK, and collect structured handoffs; do not invoke autonomously.
Scan how you actually work with your coding agent and surface what to encode next. Point it at ONE run's artifacts to find what would have prevented a specific failure (the reactive loop — 'that went wrong, what should change in the AI layer?'), or at a window of session logs to find recurring patterns worth building (the proactive scan). Agent-agnostic. Outputs a shape-only HTML report. Use to evolve your system from real usage.
Primes the agent with deep codebase understanding by analyzing structure, documentation, and key files. Use when starting work on a codebase, at the beginning of a session, or when you need a fast orientation before planning or implementing. Optionally pulls external task context from Jira issues and Confluence pages first.
Primes the agent with focused understanding of the frontend portion of the codebase — components, routing, state management, and styling — without loading unrelated backend code. Use at the start of a session when the work is scoped to UI or client-side features. Optionally pulls external task context from Jira issues and Confluence pages first.
Discover agent-native CLIs for professional software. Access the live catalog to find tools for creative workflows, productivity, AI, and more.
Submit feedback on an Expo skill—or Expo itself—and control bundled anonymous usage telemetry (off by default / opt-in). Submit feedback with: npx --yes submit-expo-feedback@latest "ACTIONABLE_FEEDBACK". Optionally add either or both: --category "CATEGORY" and --subject "SUBJECT". Replace the uppercase placeholders before running. Use when a skill was useful, confusing, broken, missing context, or worth improving; when Expo, Expo CLI, EAS CLI, docs, or MCP worked well or fell short; when an AI agent repeatedly failed, got stuck, or needed the user to take over an Expo task (report it as an eval candidate); or when the user explicitly asks to enable or disable telemetry (tracking), check its status, or understand what it collects.
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.
Apply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.