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Found 285 Skills
Intelligent music genre search assistant with 5947 genres sourced from RateYourMusic. It supports quick lookup, smart recommendations, and hierarchical exploration. USE THIS SKILL when users mention: - Explicit commands: /genre, /music-style, "query music genre", "recommend music style" - Creation needs: "I want to make a song/music in XX style", "help me choose a music style", "what style is suitable for XX scenario", "recommend some music styles with XX characteristics" (e.g., suitable for late night, energetic, ethereal, dark) - Exploration: "What subgenres does XX style have", "What's under Ambient", "Show me the branches of Rock" - Suno music generation: Trigger this skill before using the suno-music-creator skill when the genre needs to be determined, or when the user says "generate with Suno" but hasn't specified a genre
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions.
Primary tool for all code navigation and reading in supported languages (Rust, Python, TypeScript, JavaScript, Go). Use instead of Read, Grep, and Glob for finding symbols, reading function implementations, tracing callers, discovering tests, and understanding execution paths. Provides tree-sitter-backed indexing that returns exact source code — full function bodies, call sites with line numbers, test locations — without loading entire files into context. Use for: finding functions by name or pattern, reading specific implementations, answering 'what calls X', 'where does this error come from', 'how does X work', tracing from entrypoint to outcome, and any codebase exploration. Use Read only for config files, markdown, and unsupported languages.
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Fast, low-cost exploration of Robonet trading resources. Browse 8 data tools to explore available trading pairs, technical indicators, Allora ML topics, existing strategies, and backtest results. All tools execute in <1 second with minimal cost (free to $0.001). Use this skill first before building or testing strategies to understand what resources are available.
Specialized feature development agents. Use for deep codebase exploration and architecture design during feature development.
Token-efficient codebase exploration using RepoPrompt CLI. Use when user says "use rp to..." or "use repoprompt to..." followed by explore, find, understand, search, or similar actions.
Multi-repository codebase exploration for library internals, architecture understanding, and implementation comparisons.
Teaches learners to extract transferable design lessons from real-world codebases through critical evaluation and systematic exploration. Use when a learner wants to study existing code to learn patterns, architecture, or design decisions—not just understand what it does. Guides through navigation, pattern recognition, critical evaluation (deliberate choice vs. compromise), and lesson extraction. Triggers on phrases like "learn from this codebase", "study how X is implemented", "understand design patterns in Y", or when a learner wants to improve by reading real code.
Create algorithmic art using p5.js with seed-based randomness and interactive parameter exploration. Use this when users request to create art with code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art instead of copying existing artists' works to avoid copyright infringement.
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.