Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Convert images and text prompts to 3D models (.glb) using the 3D AI Studio API. Supports TRELLIS.2, Hunyuan Rapid, and Hunyuan Pro models.
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Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required.
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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
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Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
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Songwriting craft and Suno AI music prompts.
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2,592 curated AI system prompts from Claude, ChatGPT, Cursor, v0, Devin, Grok, and more.
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Evolve prompts/regex/SQL/code with Imbue's evolution loop.
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Persistent AI companion with long-term memory, project context awareness, and multi-session continuity across any LLM.
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Build comparable company analysis in Excel — operating metrics, valuation multiples, statistical benchmarking vs peer sets. Pairs with excel-author. Use for public-company valuation, IPO pricing, sector benchmarking, or outlier detection.
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Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The 'don't do the work yourself' rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
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Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
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HuggingFace hf CLI: search/download/upload models, datasets.
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Karpathy's LLM Wiki: build/query interlinked markdown KB.
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Analyze prompts and offer depth / token-budget options BEFORE answering. Use this skill when the user wants to control token usage, tune response depth, choose between short and long answers, or optimize their prompt. Triggers on: "tokens", "token budget", "depth", "consumption", "short vs long answer", "how many tokens", "save tokens", "answer at 50%", "give me the short version", "I want to control how much you use", "tune your response", "presupuesto de tokens", "profundidad", "responde al 50%", "dame la versión corta", or any equivalent phrasing in English or Spanish. If the user wants to control length, detail or depth -- even without mentioning tokens explicitly -- this skill applies.
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批量调用 Dreamifly 生成并下载图片或视频。Use when the user asks to batch-generate images, edit images with references, create text-to-video or image-to-video, queue prompts, validate or estimate generation cost, retry failed media jobs, or summarize Dreamifly outputs.
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Batch-generate Dreamifly images/videos from prompt queues. Use when the user asks to batch-generate images, edit images with references, create text-to-video or image-to-video, validate or estimate Dreamifly job cost, retry failed media jobs, or summarize Dreamifly outputs.
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