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ai-apis
How to use AI APIs like OpenAI, ChatGPT, Elevenlabs, etc. When a user asks you to make an app that requires an AI API, use this skill to understand how to use the API or how to respond to the user.
tiktok
A data pipeline for extracting, cleaning, and processing TikTok Business analytics data, including video performance and audience metrics, to create unified datasets for multiple accounts.
sdd-development
Specification-Driven Development (SDD) methodology for building software where specifications are executable and drive code generation. Use when users want to: (1) Create feature specifications with /speckit.specify, (2) Generate implementation plans with /speckit.plan, (3) Create executable tasks with /speckit.tasks, (4) Follow test-first, library-first development, or (5) Implement constitutional architecture principles. Essential for structured AI-assisted development workflows.
root-cause-tracing
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
long-running-agent
Framework for building AI agents that work effectively across multiple context windows on complex, long-running tasks. Use when building agents for multi-hour/multi-day projects, implementing persistent coding workflows, creating systems that need state management across sessions, or when an agent needs to make incremental progress on large codebases. Provides initializer and coding agent patterns, progress tracking, feature management, and session handoff strategies.
xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
nextjs
Guide for implementing Next.js - a React framework for production with server-side rendering, static generation, and modern web features. Use when building Next.js applications, implementing App Router, working with server components, data fetching, routing, or optimizing performance.
querying-json
Extracts specific fields from JSON files efficiently using jq instead of reading entire files, saving 80-95% context. Use this skill when querying JSON files, filtering/transforming data, or getting specific field(s) from large JSON files
groq-inference
Fast LLM inference with Groq API - chat, vision, audio STT/TTS, tool use. Use when: groq, fast inference, low latency, whisper, PlayAI TTS, Llama, vision API, tool calling, voice agents, real-time AI.
refactor:react
Refactor React and TypeScript code to improve maintainability, readability, and performance. This skill transforms complex React components into clean, well-structured code following modern React 19 patterns. It addresses component bloat, prop drilling, unnecessary re-renders, and improper hook usage. Leverages React 19 features including the React Compiler for automatic memoization, Actions for form handling, useOptimistic for immediate UI feedback, the use() hook for async data, and Server Components for optimal performance.
refactor:nuxtjs
Refactor Nuxt.js/Vue code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, oversized components, deep nesting, SRP violations, data fetching anti-patterns with useFetch/useAsyncData/$fetch, poor composable organization, and mixed business/presentation logic. Applies Nuxt 3 patterns including auto-imports, proper data fetching, single-responsibility composables, TypeScript integration, runtime config, Nitro server routes, Nuxt Layers, middleware patterns, Pinia state management, and performance optimizations.
refactor:rails
Refactor Ruby on Rails code to improve maintainability, readability, and adherence to best practices. This skill transforms messy Rails code into elegant, well-structured solutions following Rails conventions and modern Ruby best practices. It addresses fat controllers and models, extracts business logic into service objects, applies DRY principles, uses concerns for shared behavior, implements query objects for complex database operations, and leverages Rails 8 and Ruby 3.3+ features including pattern matching and Data classes.
ai-playbooks
Imported from https://www.skillhub.club/skills/ruliana-ai-playbooks.
debug:tensorflow
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
debug:scikit-learn
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, convergence warnings from optimizers, cross-validation failures due to class imbalance, data leakage causing suspiciously high scores, or preprocessing errors with ColumnTransformer and feature alignment.
querying-yaml
Extracts specific fields from YAML files efficiently using yq instead of reading entire files, saving 80-95% context. Use this skill when querying YAML files, filtering/transforming configuration data, or getting specific field(s) from large YAML files like docker-compose.yml or GitHub Actions workflows
basic-usage
Use when getting started with llmemory document storage and search - covers installation, initialization, adding documents, vector search, hybrid search, semantic search, BM25 full-text search, document management, and building RAG systems with multi-tenant support
refactor:scikit-learn
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
openai-agents-mcp-integration
Build AI agents with OpenAI Agents SDK + Model Context Protocol (MCP) for tool orchestration. Supports multi-provider backends (OpenAI, Gemini, Groq, OpenRouter) with MCPServerStdio. Use this skill for conversational AI features with external tool access via MCP protocol.
super-mcp-server
Imported from https://www.skillhub.club/skills/berrydev-ai-super-mcp-server.
moai-formats-data
Data format specialist covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation for modern applications
moai-foundation-core
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
exa-rag
Build RAG pipelines with Exa.ai for real-time web retrieval. Use when building retrieval-augmented generation, integrating Exa with LangChain, LlamaIndex, Vercel AI SDK, or implementing AI agents with web search capabilities. Triggers on: RAG pipeline, retrieval augmented generation, Exa LangChain, Exa LlamaIndex, ExaSearchRetriever, ExaSearchResults, Exa MCP, Exa tool calling, Claude tool use, AI agent web search, grounded generation, citation generation, fact checking, hallucination detection, OpenAI compatibility, chat completions.
excel-export-validator
Validates Customer Feedback Analyzer Excel exports with 7 view sheets, 36 columns, and professional formatting. Use when checking Excel files, verifying export quality, debugging Excel generation issues, before releasing new versions, or when modifying Excel export code. Ensures zero errors in customer-facing deliverables.