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Found 1,622 Skills
Update the llms.txt file in the root folder to reflect changes in documentation or specifications following the llms.txt specification at https://llmstxt.org/
Optimize programmatic SEO pages for visibility and citation in AI-generated answers from ChatGPT, Perplexity, Google AI Overviews, and other LLM-powered search. Use when optimizing for LLM citation, implementing llms.txt, configuring AI crawler access, structuring content for AI extraction, or when the user asks about generative engine optimization (GEO), AI search visibility, or getting cited by AI.
Enterprise LLM Fine-Tuning with LoRA, QLoRA, and PEFT techniques
LLM app development with RAG, prompt engineering, vector databases, and AI agents
LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
Apply when implementing fulfillment, invoice, or tracking logic for VTEX marketplace seller connectors. Covers the External Seller fulfillment protocol: fulfillment simulation (checkout and indexation), order placement with reservation id, order dispatch (authorize fulfillment), OMS invoice and tracking APIs, and partial invoicing. Use for seller-side services that must answer within the simulation SLA and integrate with VTEX marketplace order management.
Detects common LLM coding agent artifacts by spawning 4 parallel subagents
Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
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.
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.
Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation