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Found 1,299 Skills
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
Apply the skills collection's UPGRADE_NOTES.md after an upgrade. Re-syncs installed tracker and browser-provider descriptors while preserving local edits, reports custom-provider gaps, checks pipeline config and installed artifacts, and summarizes exactly what changed.
Facilitates the first step of a proven customer-interview method: deciding exactly what you're trying to learn, written as numbered goal questions (G1, G2, …) that your hypotheses and interview questions will later be designed to answer. Interviews the user about their business — new idea or established company, B2B or B2C — then drafts a tailored goal-question list, critiques and revises it with them, and preserves the result in a GOALS.md file. Load when the user wants to interview customers, plan customer discovery or customer development, validate a startup idea, or answer unaskable questions like what to charge, how to position, who the ideal customer is, or what to build. Do NOT load for job or hiring interviews, for writing survey questionnaires, for analyzing customer interviews that have already been conducted, or when a goal-question list already exists and the user wants the next step (hypotheses).
Facilitates the third step of a proven ideal-customer (ICP) method: refining classified strengths into keystones — the specific characteristics, behaviors, or circumstances that make a customer NEED an extreme version of a strength, badly enough to drive the purchase alone. Takes a strengths chart (S1, S2, … — file or pasted), walks the strengths one at a time asking who requires an extreme version of each, gates every candidate on naming a real market segment that typifies it (no segment = table stakes, cut), and records survivors in KEYSTONES.md (K1, K2, … with [S] references and example segments). Delivers the strategic verdict when few or none survive: the product isn't compelling yet. Load when the user has classified strengths and asks who needs them, who their target market is, or 'turn our strengths into keystones.' Do NOT load to classify strengths and weaknesses (the previous step), to derive deal-breakers or the anti-market (the next step), or to write the final ideal-customer definition (later).
Grades a specified set of test methods individually and produces a concise table mapping each test (fully-qualified name) to a letter grade (A–F), a score band, and a one-line note — designed to be posted as a PR comment. Use when the caller wants per-test feedback on a curated list of methods (for example, the new or modified tests in a pull request), not a suite-wide audit. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. Input is a list of test methods (or method bodies / file+line spans); output is a compact markdown table plus a short summary. DO NOT USE FOR: full suite audits (use test-quality-auditor agent or test-anti-patterns), writing new tests (use code-testing-generator agent or writing-mstest-tests), fixing failures, or measuring code coverage.
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.
Assesses whether and how an existing mobile product should migrate to React Native. Use when auditing one or more product repositories for migration readiness, including products whose iOS, Android, and other clients live in separate directories or repositories; choosing brownfield, greenfield, or a checkpoint-based path; defining a representative trial; or preparing a baseline and ROI decision before implementation. When product scope or material evidence is unavailable, grills the stakeholder with exactly one question per turn instead of sending a questionnaire.
Use when capturing or analyzing ETTrace profiles for a focused iOS launch or runtime flow, including exact-build dSYM UUID matching, Simulator or device capture, processed per-thread flamegraph JSON, sampled inclusive/exclusive time, unresolved symbols, and comparable verification. Use debugging-instruments or swiftui-performance for generic profiling instead.
New SwiftUI APIs, behaviors, and deprecations introduced in the 2027 OS releases (iOS 27, macOS 27, watchOS 27, tvOS 27, visionOS 27). Use when a SwiftUI view using @State fails to compile with "used before being initialized", "invalid redeclaration of synthesized property", or "extraneous argument label" errors after an SDK update (@State migrated from a property wrapper to a macro in SDK 27; the obvious fix of reordering init assignments is WRONG and produces incorrect runtime behavior; you MUST consult this skill's references before answering); when @ViewBuilder or @ContentBuilder code hits ambiguous overloads in overlay/background or type-check performance regressions after an SDK update; when the user asks what's new in SwiftUI (generally, or for a specific 2027 platform); when adding drag-to-reorder to any container (List, LazyVStack, LazyVGrid, stacks, or custom layouts) via reorderable()/reorderContainer, including the drag-and-drop that integrates with it (dragContainer, dropDestination), or combining items by dropping one onto another; when working with AsyncImage loading and caching (images reloading when scrolling back, the default HTTP cache, a per-request cache policy via AsyncImage(request:)/URLRequest, or applying a custom URLSession with asyncImageURLSession); when adding swipe actions to rows (swipe-to-delete or other swipe actions) in a ScrollView, LazyVStack, LazyVGrid, or stack and not just List, via swipeActions()/swipeActionsContainer(); when working with toolbars, such as controlling which items stay visible versus move into the overflow menu when space is constrained or buttons get cut off (visibilityPriority, ToolbarOverflowMenu), pinning an item so it never overflows (topBarPinnedTrailing), minimizing the navigation bar or toolbar on scroll (toolbarMinimizeBehavior), generating toolbar items with ForEach, or hiding the status bar via the statusBar toolbar placement; when presenting a confirmation dialog or alert from an optional item binding (the sheet(item:) shape) so it shows when the bound value becomes non-nil and passes the unwrapped item into the actions and message closures; when building or migrating a document-based app (including read-only document viewers), reading or writing files through DocumentGroup, optimizing autosave performance for package documents, accessing the document's file URL directly (for example to hand to AVFoundation, PDFKit, Core Image, or any C library that takes a path), reporting progress from a save or load, or migrating from FileDocument / ReferenceFileDocument; or when resolving other SDK 27.0 source incompatibilities and deprecation warnings (for example statusBarHidden on visionOS).
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
Use when the user asks to "plan my site structure", "design the page hierarchy / navigation / URL taxonomy", "fix internal linking", or "find orphan pages"; runs two modes — architecture (hierarchy, nav, URL patterns, hub/spoke clusters, Mermaid site maps) and linking (link graph, authority flow, anchor text, orphan disposition, source/target/anchor plan) — and outputs a structure score /100 plus a handoff summary. Not for external backlinks — use offsite-signal-analyzer; not for XML sitemap or indexation issues — use technical-seo-checker. 网站架构/信息架构/站点地图/内链优化
Import CSV or Excel files into seekdb vector database and manage collections. Supports automatic vectorization of specified columns using embedding functions. When users need to: (1) Read and preview Excel files, (2) Import CSV/Excel data into seekdb, (3) Create vector collections from tabular data, (4) Vectorize specific text columns for semantic search, (5) Batch insert product/document data with embeddings, (6) Delete collections, or (7) Access sample data files (sample_products.csv/xlsx) for testing - IMPORTANT: sample files are located in this skill's example-data/ directory, you MUST read this skill file first to get the correct path.