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Found 163 Skills
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention
Use when selecting, installing, configuring, smoke-testing, documenting, or troubleshooting MCP servers for academic search, arXiv, Semantic Scholar, OpenAlex, Crossref, PubMed, Zotero, Overleaf, Google Scholar, paper metadata, or scholarly source tooling.
USE FOR AI-grounded answers via OpenAI-compatible /chat/completions. Two modes: single-search (fast) or deep research (enable_research=true, thorough multi-search). Streaming/blocking. Citations.
Use the official MinerU (mineru.net) parsing API to convert a URL (HTML pages like WeChat articles, or direct PDF/Office/image links) into clean Markdown + structured outputs. Use when web_fetch/browser can’t access or extracts messy content, and you want higher-fidelity parsing (layout/table/formula/OCR).
USE FOR getting AI-generated POI text descriptions. Requires POI IDs obtained from web-search (with result_filter=locations). Returns markdown descriptions grounded in web search context. Max 20 IDs per request.
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.
Write Related Work sections that compare and contrast prior work with your approach. Organize by theme, cite broadly, and explain how your work differs. Use when writing or improving the Related Work section of a paper.
Systematic retrieval expert covering all areas of Chinese law. ## Core Features - Supports user identity recognition (ordinary person/law student/lawyer/judge/prosecutor) - Provides differentiated services based on different identities - Complete legal source retrieval (laws/administrative regulations/judicial interpretations/guiding cases/typical cases) - Original legal article citation and cross-reference sorting ## Core Trigger Conditions (Trigger if any is met) **High Priority (Must Trigger)**: - Explicit request to find legal articles/regulations/judicial interpretations/regulatory documents - Request to determine legality/illegality ("Is it illegal?""Is it legal?""Am I liable?") - Request to find compensation standards/compensation amounts/liability determination/procedural requirements - Asking "Based on which law?""What does the law stipulate?""What is the legal basis?" **Medium Priority (Trigger based on context)**: - "What to do?""How to defend rights?""Can I sue?" - "What procedures are needed?""What conditions are required?" - "What else can I claim?""Where can I file a complaint?" ## Application Scenarios - Labor disputes: illegal termination, economic compensation, work-related injuries, social security, job transfer, etc. - Contract disputes: deposit, liquidated damages, breach of contract liability, sales contracts, etc. - Tort liability: traffic accidents, personal injury, medical accidents, environmental pollution, etc. - Marriage and family: divorce property, child custody, estate inheritance, etc. - Administrative/criminal/corporate finance, etc. ## Non-Triggering Scenarios - Only asking about legal concepts/terminology explanations (not retrieval-related) - Only requesting lawyer/legal service recommendations - Only discussing legal news/case stories (not involving specific regulations) - Only asking about legal examination/study questions **Note**: Even if the user does not explicitly request a "retrieval report", this skill will be triggered as long as the issue involves searching, organizing, interpreting, or applying legal norms.
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.