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Found 1,644 Skills
Reassign fulfillment orders from one location to another for warehouse overflow or regional routing.
Router skill for LLMQuant ETFs workflows. Use when the user needs ETF holdings, overlap, concentration, issuer snapshot, or theme exposure analysis.
List available large language models and send chat completion requests programmatically. Use this skill when you need to call an LLM within a snippet, including model comparison, visual understanding, batch inference, and model performance testing.
Router skill for LLMQuant macro workflows. Use when the user needs macro dashboards, Fed or central-bank previews, inflation and growth context, liquidity, or macro-to-portfolio impact analysis.
Step-by-step guide for adding support for a new LLM in Dust. Use when adding a new model, or updating a previous one.
Router skill for LLMQuant Data primitive workflows. Use when the user needs SEC filings, 13F holders, macro snapshots, or source-grounded macro briefs.
Router skill for LLMQuant credit workflows. Use when the user needs issuer credit review, spread regime analysis, high-yield stress monitoring, default risk, debt maturity, or covenant context.
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
Expert skill for integrating local Large Language Models using llama.cpp and Ollama. Covers secure model loading, inference optimization, prompt handling, and protection against LLM-specific vulnerabilities including prompt injection, model theft, and denial of service attacks.
The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources → wiki → schema). Also use when discussing knowledge management strategy, wiki structure decisions, or how to organize distilled knowledge. This is the "theory" skill — other skills handle specific operations (ingesting, querying, linting).
BullMQ queue system reference for Redis-backed job queues, workers, flows, and schedulers. Use when: (1) creating queues and workers with BullMQ, (2) adding jobs (delayed, prioritized, repeatable, deduplicated), (3) setting up FlowProducer parent-child job hierarchies, (4) configuring retry strategies, rate limiting, or concurrency, (5) implementing job schedulers with cron/interval patterns, (6) preparing BullMQ for production (graceful shutdown, Redis config, monitoring), or (7) debugging stalled jobs or connection issues
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.