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Found 1,288 Skills
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
LLM inference via paid API: OpenAI-compatible chat completions proxied through x402 providers. Supports Kimi K2.5, MiniMax M2.5. Uses x_payment tool for automatic USDC micropayments ($0.001-$0.003/call). Use when: (1) generating text with a specific model, (2) running chat completions through a pay-per-request LLM endpoint, (3) comparing outputs across models.
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
Compress documents for LLM token efficiency while preserving semantic content. Use when asked to compress, compact, shrink, or optimize a document, CLAUDE.md, system prompt, skill file, or any text for fewer tokens. Also use when the user mentions token count, token budget, context window limits, or wants to make prompts shorter for cost savings.
Develop, debug, and optimize SGLang LLM serving engine. Use when the user mentions SGLang, sglang, srt, sgl-kernel, LLM serving, model inference, KV cache, attention backend, FlashInfer, MLA, MoE routing, speculative decoding, disaggregated serving, TP/PP/EP, radix cache, continuous batching, chunked prefill, CUDA graph, model loading, quantization FP8/GPTQ/AWQ, JIT kernel, triton kernel SGLang, or asks about serving LLMs with SGLang.
Benchmark any agent skill to measure whether it actually improves performance. Use when the user wants to evaluate, test, or compare a skill against baseline, or when they mention "benchmark", "eval", "skill performance", or "does this skill help". Runs isolated eval sessions with and without the skill, grades outputs via layered grading (deterministic checks + LLM-as-judge), analyzes behavioral signals, and generates a comparison report with a USE / DON'T USE verdict.
Research tool for visually exploring BLS Occupational Outlook Handbook data with an interactive treemap, LLM-powered scoring pipeline, and data scraping/parsing utilities.
Overrides default LLM truncation behavior. Enforces complete HTML generation with zero placeholder patterns. Every landing page must be delivered as a complete, production-ready file. No shortcuts, no skeletons, no "add more as needed" patterns.
Transform code, issues, or context into a detailed prompt/context for another LLM to fix or implement. Use when preparing comprehensive context for external LLM assistance, bug fixes, improvements, or feature implementations. Provides detailed context without implementation suggestions, letting the receiving LLM decide how to implement solutions. Focuses on "what" (problem, requirements, current state) not "how" (implementation approach).
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
Framework for collective skill evolution in multi-user LLM agent ecosystems — automatically distills session experience into reusable SKILL.md files and shares them across agent clusters.
Expert skill for building AI systems with Weft, a Rust-based programming language where LLMs, humans, APIs, and infrastructure are first-class primitives with typed connections and durable execution.