Skills
AI agent Skills için açık kayıt: önerilen modeller, örnek prompt'lar ve uyumlu araçlar içeren yapılandırılmış prompt'lar ve iş akışları.
Skills
48
Kategoriler
9
Uyumlu araçlar
9
Katkıda bulunanlar
1
Analyzes LLM usage and reduces inference cost without sacrificing quality. Covers prompt compression, context trimming, caching (prompt and semantic), model routing by task difficulty, batching, structured output to cut retries, and token accounting. Produces a concrete plan with estimated savings and quality guardrails.
Designs routing layers that dispatch requests across multiple LLMs based on task type, difficulty, latency, cost, and reliability. Covers classifier-based and heuristic routing, fallbacks and retries across providers, quality scoring, and A/B evaluation of routing policies. Helps teams get frontier quality where it matters and cheap models everywhere else.
Designs and scaffolds ergonomic command-line tools with subcommands, flags, config files, shell completions, and helpful error output. Covers argument parsing conventions, exit codes, stdin/stdout piping, colored output, progress indicators, and cross-platform packaging. Produces maintainable CLIs that follow POSIX conventions and feel great to use.
Plans and executes safe dependency upgrades across a project. Analyzes current versions, reads changelogs and release notes for breaking changes, sequences upgrades to minimize risk, applies required code migrations, and verifies with builds and tests. Works across npm, pip, Maven, Cargo, and Go modules.
Designs multi-agent systems where a coordinator delegates sub-tasks to specialist agents, verifies intermediate results, and synthesizes a final answer. Covers agent role definition, routing and delegation strategy, shared memory and message passing, verification loops, cost and latency budgeting, and failure handling across frameworks like LangGraph, CrewAI, or a custom orchestrator.
Writes safe, reversible database schema migrations and the corresponding rollback scripts. Plans zero-downtime changes using expand-and-contract patterns, handles data backfills and index creation without locking, and generates migrations for tools like Alembic, Flyway, Prisma, Knex, and Rails ActiveRecord with clear up/down steps.
Adds clear, accurate inline comments and API doc blocks to existing code without changing behavior. Generates docstrings and structured comments (JSDoc, Google/NumPy style, Javadoc, Rustdoc) that explain intent, parameters, return values, side effects, and edge cases, while avoiding noisy comments that merely restate the code.
Designs GraphQL schemas from domain descriptions or existing data models. Produces typed SDL with queries, mutations, subscriptions, input types, enums, and interfaces, following naming conventions, pagination patterns (Relay-style connections), and error-handling best practices. Can also generate resolvers scaffolding and map schemas to existing REST or SQL backends.
Generates mobile application prototypes and implementations for iOS and Android. Creates SwiftUI views, Jetpack Compose layouts, and React Native components from descriptions or wireframe images. Handles navigation patterns, state management, and platform-specific design guidelines.
Interacts with GitHub via the gh CLI for managing issues, pull requests, CI runs, releases, and repository settings. Automates common GitHub workflows including PR creation with proper descriptions, issue triage, release drafting, and CI debugging.
Designs and scaffolds AI agent architectures including tool definitions, system prompts, memory strategies, and orchestration logic. Supports multi-agent workflows, ReAct patterns, function calling schemas, and MCP server configurations. Helps structure agents that are reliable, observable, and easy to debug.
Generates type-safe API client SDKs from OpenAPI specs, API documentation, or example requests. Produces clean, well-typed client code with error handling, retry logic, pagination helpers, and authentication setup. Supports TypeScript, Python, Go, and Rust.
Helps architect scalable distributed systems by analyzing requirements and producing high-level architecture diagrams, component breakdowns, data flow descriptions, and technology recommendations. Covers load balancing, caching strategies, database selection, message queues, and failure handling patterns.
Analyzes and resolves git merge conflicts by understanding the intent of both sides. Examines the conflict markers, surrounding context, and commit history to produce a correct merged result that preserves both changes without breaking functionality.
Designs and implements comprehensive error handling for APIs. Covers error response formats (RFC 7807 Problem Details), HTTP status code selection, error logging strategies, retry logic, and client-friendly error messages with proper i18n support.
Generates complete OpenAPI 3.1 specifications from API descriptions, existing code, or route definitions. Includes request/response schemas, authentication, error responses, examples, and server configurations. Produces valid YAML ready for Swagger UI.
Generates production-ready React components with TypeScript, proper props interfaces, accessibility attributes, responsive design, and test files. Follows modern patterns including Server Components, Suspense boundaries, and composition over inheritance.
Explains complex code in plain language at the requested level of detail. Breaks down algorithms, design patterns, and architecture decisions. Adapts explanation depth from high-level overview to line-by-line walkthrough based on audience.
Generates TypeScript type definitions from various sources — JSON data, API responses, database schemas, or plain descriptions. Produces strict types with proper generics, utility types, discriminated unions, and JSDoc comments.
Guides systematic code refactoring while preserving exact behavior. Identifies code smells, suggests appropriate refactoring patterns, and executes transformations incrementally with verification at each step. Follows Chesterton's Fence principle — understands why code exists before changing it.
Designs, optimizes, and iterates on prompts for LLM applications. Covers system prompt design, few-shot examples, chain-of-thought reasoning, output formatting, and prompt testing strategies. Helps build reliable AI-powered features.
Skills ve MCP sunucuları
aralarındaki fark nedir?Skills“ne yapılacağı”
Bir Skill; talimatları, örnek prompt'u ve önerilen modelleri paketleyerek agent'ın görevi tutarlı şekilde yapmasını sağlar. Skills yeni bağlantılar değil, bilgi ekler.
MCP sunucuları“nasıl bağlanılacağı”
Bir MCP sunucusu, gerçek sistemlere (veritabanları, API'ler ve dosyalar) bir taşıma yöntemi üzerinden bağlanarak agent'a yeni yetenekler kazandırır. MCP görev talimatları değil, bağlantılar ve eylemler ekler.
Kısa kural: modelin bir görevi iyi yapması gerekiyorsa Skill, bir araca veya sisteme erişmesi gerekiyorsa MCP sunucusu kullanın. Birlikte çalışabilirler; Skill, MCP sunucusunun sunduğu araçlardan yararlanabilir.
Faydalı bir Skill mi geliştirdiniz?
Bir SKILL.md gönderin; açık kaynaklıdır ve topluluk tarafından sürdürülür.