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Found 2,493 Skills
Install UploadThing file upload components from the Elements registry. Use when user needs file uploads, drag-and-drop dropzones, avatar uploads, file cards, image grids, paste-to-upload, or upload progress indicators. Triggers on "upload", "file upload", "dropzone", "uploadthing", "drag and drop upload", "image grid", "paste upload", "avatar upload".
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Guide for using Netlify Blobs object storage. Use when storing files, images, documents, or simple key-value data without a full database. Covers getStore(), CRUD operations, metadata, listing, deploy-scoped vs site-scoped stores, and local development.
Use when the user wants to review a pull request, understand what a PR changes, assess risk of merging, or check for missing test coverage. Examples: "Review this PR", "What does PR #42 change?", "Is this PR safe to merge?"
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
Use this skill when working with Mastra - the TypeScript AI framework for building agents, workflows, tools, and AI-powered applications. Triggers on creating agents, defining workflows, configuring memory, RAG pipelines, MCP client/server setup, voice integration, evals/scorers, deployment, and Mastra CLI commands. Also triggers on "mastra dev", "mastra build", "mastra init", Mastra Studio, or any Mastra package imports.
Go testing patterns for production-grade code: subtests, test helpers, fixtures, golden files, httptest, testcontainers, property-based testing, and fuzz testing. Covers mocking strategies, test isolation, coverage analysis, and test design philosophy. Use when writing tests, improving coverage, reviewing test quality, setting up test infrastructure, or choosing a testing approach. Trigger examples: "add tests", "improve coverage", "write tests for this", "test helpers", "mock this dependency", "integration test", "fuzz test". Do NOT use for performance benchmarking methodology (use go-performance-review), security testing (use go-security-audit), or table-driven test patterns specifically (use go-test-table-driven).
Implement Syncfusion React Kanban component for flexible task management and workflow visualization. Use this when building kanban boards, workflow stages, or card-based task management systems. This skill covers drag-and-drop card operations, swimlane organization, WIP limit validation, custom card templates, event handling, and API data binding for React applications.
Apply the Modigliani-Miller theorem to analyze capital structure decisions and identify when financing choices affect firm value. Use this skill when the user needs to evaluate debt-equity tradeoffs, assess the impact of leverage on firm value, understand tax shield benefits, or when they ask 'does capital structure matter', 'should we take on more debt', or 'what is the optimal leverage ratio'.
Summarizes descriptive concepts for max pain options theory, covered-call style crypto ETFs, crypto arbitrage families and risks, and bull/bear flag chart patterns—always as non-prescriptive education. Use when the user asks about max pain, premium income ETFs, arbitrage, funding rates, flash loans, or bull/bear flags in crypto trading context.
Points to Michał Zalewski’s (lcamtuf) canonical American Fuzzy Lop (AFL) documentation at lcamtuf.coredump.cx/afl—coverage-guided fuzzing concepts, afl-fuzz usage, and historical technical notes for C/C++ targets. Use when the user cites AFL classic, lcamtuf’s AFL page, or needs the original upstream reference—not as a substitute for current AFL++ docs or authorized fuzzing policy.
Persistent key-value memory storage for agents. Store and recall information across conversations and sessions. Use when you need the agent to remember facts, preferences, or data between interactions.