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Found 6,747 Skills
Expert Spring Boot 4 testing specialist that selects the best Spring Boot testing techniques for your situation with Junit 6 and AssertJ.
Create and refine OpenCode agents via guided Q&A. Use proactively for agent creation, performance improvement, or configuration design. Examples: - user: "Create an agent for code reviews" → ask about scope, permissions, tools, model preferences, generate AGENTS.md frontmatter - user: "My agent ignores context" → analyze description clarity, allowed-tools, permissions, suggest improvements - user: "Add a database expert agent" → gather requirements, set convex-database-expert in subagent_type, configure permissions - user: "Make my agent faster" → suggest smaller models, reduce allowed-tools, tighten permissions
Comprehensive TypeScript/JavaScript coding standards focusing on type safety, defensive programming, and code correctness. Use when (1) Writing or reviewing TS/JS code, (2) Fixing type errors or avoiding any/enum/null, (3) Implementing control flow, state management, or error handling, (4) Applying zero-value pattern or immutability, (5) Code review for TypeScript anti-patterns. Covers naming conventions, function design, return values, bounded iteration, input validation. For performance optimization, use accelint-ts-performance skill. For documentation, use accelint-ts-documentation skill.
Generate structured, actionable build reports from Node.js build outputs (TypeScript, ESLint, Webpack, Vite). Groups errors by pattern, prioritizes issues, and suggests documented solutions. Use when analyzing build failures, debugging compilation errors, or reviewing warnings. Supports English and Spanish. | Genera reportes estructurados y accionables de builds Node.js (TypeScript, ESLint, Webpack, Vite). Agrupa errores por patrón, prioriza issues y sugiere soluciones documentadas. Usar para analizar fallos de build, debuggear errores de compilación o revisar warnings.
Use when writing, reviewing, or refactoring React component tests with Testing Library. Load when you see render(), screen, fireEvent, userEvent, waitFor, or *.test.tsx files. Covers query priority (getByRole > getByLabelText > getByText), user-centric testing patterns, async utilities, custom renders with providers, and accessibility-first assertions. Keywords include RTL, Testing Library, screen, getByRole, findBy, queryBy, userEvent, waitFor, toBeInTheDocument, testing-library/react, testing-library/user-event, jest-dom.
Review AI API key leakage patterns and redaction strategies. Use for identifying exposed keys for OpenAI, Anthropic, Gemini, and 10+ other providers. Use proactively when code integrates AI providers or when environment variables/keys are present. Examples: - user: "Check for leaked OpenAI keys" → scan for `sk-` patterns and client-side exposure - user: "Is my Gemini integration secure?" → audit vertex AI config and key redaction - user: "Review AI provider logging" → ensure secrets are redacted from logs - user: "Scan for Anthropic secrets" → check for `ant-` keys in code and configs - user: "Audit Vertex AI integration" → verify proper IAM roles and service account usage
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
SEO methodologies, keyword research techniques, and optimization strategies for search engine visibility
Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
Queries the UniBind database for experimentally validated transcription factor (TF) binding sites. Use when retrieving direct TF-DNA interaction datasets, downloading binding site coordinates (BED/FASTA) for local analysis, or listing available datasets by species, cell line, or TF name. Don't use to query specific intervals, locations, genes, motif models or expression data.
Generate, edit, and compose images using Gemini Nano Banana models via portable Python scripts. Handles authentication via API Key or Vertex AI environment variables. Available parameters: prompt, model, aspect-ratio, safety-filter-level. Always confirm parameters with the user or explicitly state defaults before running.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.