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Found 1,644 Skills
Router skill for LLMQuant investor-lens workflows. Use when the user wants an investor-style reasoning overlay grounded in LLMQuant Data evidence.
Router skill for LLMQuant commodities workflows. Use when the user needs commodity spot, futures curve, inventory, roll yield, or macro linkage analysis.
Router skill for LLMQuant hedge-fund and PM strategy workflows. Use when the user needs equity long/short, long-biased, event-driven, macro, quant, or multi-strategy playbooks.
Router skill for LLMQuant risk workflows. Use when the user needs fear scoring, VIX regime, hedge design, or research health checks.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
OWASP Top 10 for LLM Applications - prevention, detection, and remediation for LLM and GenAI security. Use when building or reviewing LLM apps - prompt injection, information disclosure, training/supply chain, poisoning, output handling, excessive agency, system prompt leakage, vectors/embeddings, misinformation, unbounded consumption.
Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve. Supports multiple datasets (random, sharegpt, sonnet, HF), backends (openai, openai-chat, vllm-pooling, embeddings), throughput/latency testing with request-rate control, and result saving. Use when benchmarking LLM serving performance, measuring TTFT/TPOT, or load testing inference APIs.
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
Generate llms.txt and llms-full.txt files for a website to improve AI discoverability. Use when the user asks to create llms.txt, generate llms.txt, fix llms.txt, make site AI-readable, or mentions llms.txt generation.
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel boundaries, representative kernel flows, or Perfetto time ranges.
LLM Tuning Patterns
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.