Total 50,523 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
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.
Router skill for LLMQuant event workflows. Use when the user needs earnings event briefs, M&A tracking, regulatory risk, catalysts, event calendars, or cross-asset event impact.
Router skill for LLMQuant equity derivatives workflows. Use when the user needs single-stock derivative, convertible, warrant, structured payoff, or hybrid security analysis.
Router skill for LLMQuant crypto workflows. Use when the user needs crypto market regime analysis, token research, perpetual funding, basis, leverage, liquidity, or cross-asset crypto context.
Router skill for LLMQuant rates and FX workflows. Use when the user needs yield curve, duration, central-bank divergence, FX carry, real-rate, dollar, or cross-currency analysis.
Search Twitter for trending promotional posts related to coding/AI agent tools, generate reply drafts with the pikiclaw GitHub card, and push the results to Feishu Doc along with bot notifications. Does NOT auto-post to Twitter.
Used for finetuning NV-Generate-CTMR MR-brain diffusion UNet from a NIfTI datalist. Not for clinical or production data approval.
Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
Source-first, self-loop resistant guardrails for Capy GitHub dialogue responders before any write-capable PR, issue, or review action.
Trigger this skill when building applications with Gemma or for general knowledge inquiries related to Gemma models (e.g. prompt structure, capabilities). Covers model selection, development workflows, and deployment best practices.
Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.
Standardized directory structure and artifact management for agentic research. Ensures consistent data flow across all agent platforms (Gemini, Claude, Antigravity).