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Found 171 Skills
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
Integrate multiple plot point analysis results into a comprehensive report, and generate high-quality analysis through deduplication, classification, sorting, and summarization. Suitable for integrating multiple analysis sources and generating unified reports
Run ML model inference (YOLO, YOLOv8, CLIP, SAM, Detectron2, etc.) on FiftyOne datasets. Use when running models, applying detection, classification, segmentation, embeddings, or any model prediction task. Also use for end-to-end workflows that include importing data then running inference.
Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener) into a unified conviction score (0-100), pattern classification, and allocation recommendation. Use when user asks about overall market conviction, portfolio positioning, asset allocation, strategy synthesis, or Druckenmiller-style analysis. Triggers on queries like "What is my conviction level?", "How should I position?", "Run the strategy synthesizer", "Druckenmiller analysis", "総合的な市場判断", "確信度スコア", "ポートフォリオ配分", "ドラッケンミラー分析".
MantaBase T3 Hardware Audit System (Chinese Version). Objectively classify hardware products based on design theories through Brand Blinding, three Auditor (Tool/Toy/Trash) special scoring and Peer Review. Triggers: product link, T3 audit, Tool/Toy/Trash classification, hardware evaluation, VC investment advice
Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification
Resolve merge conflicts systematically with context-aware 3-tier classification and escalation protocol
Universal Cross-session Memory Protocol (Universal Memory Protocol). Enable all AI programming tools to share the same memory system. Applicable to Claude Code / Cursor / Aider / Cline / Codex / Trae / OpenCode. Capabilities: Intelligent Classification / FSRS Decay / Monthly Compression / Multi-layer Retrieval. Triggers: User says "remember"; asks "previous"; sensitive information detected; session ends.
Reddit community moderation via PRAW with LLM-powered report classification: fetch modqueue, classify reports against subreddit rules and author history, and take mod actions (approve, remove, lock). Supports interactive, auto, and dry-run modes.
Analyze inventory health using turnover ratios, ABC classification, safety stock calculations, and stockout vs overstock diagnostics. Use this skill when the user needs to optimize inventory levels, reduce carrying costs, prevent stockouts, or classify products by inventory priority — even if they say 'we have too much stock', 'we keep running out of bestsellers', 'how much safety stock do we need', or 'which products should we focus on'.
Drug regulatory and approval research -- FDA substance registry lookup, drug classification by ATC/EPC/MoA via RxClass, Orange Book generic availability and patent status, DailyMed label parsing (adverse reactions, dosing, contraindications), and clinical trial search. Use when users ask about FDA-approved drugs, drug regulatory status, generic availability, patent expiration, drug class membership, drug labeling, or substance identification.
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.