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Found 567 Skills
Transforms vague or simple user prompts into high-quality, structured, and high-performance AI instructions using systematic optimization techniques like XML tagging, few-shot examples, and Chain-of-Thought. Use this skill when you need to improve the reliability, accuracy, or formatting of an AI's output.
Use Gemini to find existing solutions before building from scratch. Leverages Google Search grounding to discover code examples, libraries, and best practices to avoid reinventing the wheel.
Analyze propositions from multiple expert perspectives. Dynamically generates 4-6 relevant expert roles, then performs validation, comprehensive analysis, or debate-style examination. Use when user wants to examine ideas critically, find blindspots, or explore different viewpoints on a topic.
Diagnose MCP validation dependencies (Brave Search, Chrome DevTools) and output exact setup snippets.
This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).
Search Sourcegraph-indexed codebases for patterns, examples, and system understanding. Triggers on implementation questions, debugging, or "how does X work" queries.
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Generate code using nx generators. USE WHEN scaffolding code or transforming existing code - for example creating libraries or applications, or anything else that is boilerplate code or automates repetitive tasks. ALWAYS use this first when generating code with Nx instead of calling MCP tools or running nx generate immediately.
Performs structural code search and refactoring by matching code structure instead of exact text. Use when editing code structure with text matching ambiguity, handling "old_string not unique" problems, or performing formatting-independent pattern matching across function signatures, method calls, and class structures
PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and reference templates/snippets (vector/hybrid/image) plus agent-oriented examples (RAG/memory/text2sql).
Complete energy optimization API reference - Power Profiler workflows, timer/network/location/background APIs, iOS 26 BGContinuedProcessingTask, MetricKit monitoring, with all WWDC code examples
Use when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file paths, complete code examples, and verification steps assuming engineer has minimal domain knowledge