Loading...
Loading...
Found 262 Skills
Extract and organize frames from a Bilibili video (bangumi episode, UP upload, or a local file) into scenery shots and per-character image groups, using anime-specific person detection + CCIP character-identity embeddings. Two modes — cluster everyone, or pull out one (or several) named characters via reference folders. Use when the user wants to collect, extract, or organize anime frames/screenshots by character or by scenery from a Bilibili video. Read-only download for personal viewing/analysis; uploads nothing.
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include "train metric learning", "ml-recog", "retrieval embeddings", "triplet loss recognition", "fine-grained matching".
Huawei Cloud Ascend model deployment and testing skill for large language models on Ascend DevServer (910B series). Supports single-machine and dual-machine deployment for LLM, VL (vision-language), Embedding, and Rerank models. Provides model inference testing, deployment log viewing, and status monitoring with automated model matching and deployment script generation. Use this skill when the user wants to: (1) deploy a model on Ascend DevServer, (2) test model inference, (3) view deployment logs or status, (4) list supported models, (5) check deployment prerequisites. Trigger: deploy, test, model list, deployment log, Ascend, DevServer, 910B, ModelArts, LLM, VL, Embedding, Rerank, multimodal, inference, model catalog, 昇腾, 部署模型, 测试模型, 模型列表, 部署日志, 模型部署, 推理测试
Ancient text restoration, attribution, dating, contextualization, and embedding via Aeneas (Latin) / Ithaca (Ancient Greek). Use when asked to "restore", "attribute", "date", "contextualize", "find parallels", "where was it written", "when was it written", "embed", or "analyze" an ancient text, inscription, or epigraphic document, or when the user mentions "Aeneas", or "Ithaca".
Transform a user-supplied photo into an expressive minimal zine poster made only from original source-derived illustration, an artistic proposition, emotional tension, visual metaphor, spacious negative space, art-directed high-chroma color, and unconstrained authorial typography. Let wording, language, amount, placement, type voices, scale, direction, legibility, and image interaction follow expression and aesthetic judgment rather than presets. Preserve source orientation by default with a 3:5 portrait output or 5:3 landscape output. Add source-derived distributed supporting accents and a natural isolated-contour option alongside adaptive paper-edge transitions. Support an exact `单色块模式` trigger for one contiguous saturated color field with all remaining forms in neutral ink. Use for authored abstract or editorial reinterpretations that communicate an emotion or idea without embedding, cropping, tracing, or preserving the original photographic material in the final image.
This skill should be used when building data processing pipelines with CocoIndex, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex is Python-native (supports any Python types), has no DSL, and uses version 1.0.0 or later.
Expert guidance for Satori, the library that converts JSX/HTML and CSS into SVG (the engine behind dynamic Open Graph images and social cards). Use whenever writing or debugging Satori markup e.g. authoring JSX for OG images, choosing CSS that Satori actually supports, fixing layout that renders wrong, embedding fonts, rendering emoji or images, or resolving Satori errors like "Expected length unit" or unsupported property issues. Reach for this any time someone renders HTML/CSS to SVG or PNG with Satori, even if they do not name it.
Import CSV or Excel files into seekdb vector database and manage collections. Supports automatic vectorization of specified columns using embedding functions. When users need to: (1) Read and preview Excel files, (2) Import CSV/Excel data into seekdb, (3) Create vector collections from tabular data, (4) Vectorize specific text columns for semantic search, (5) Batch insert product/document data with embeddings, (6) Delete collections, or (7) Access sample data files (sample_products.csv/xlsx) for testing - IMPORTANT: sample files are located in this skill's example-data/ directory, you MUST read this skill file first to get the correct path.
Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data masking, and document grounding. Supports OpenAI GPT models, Llama, Gemini, Amazon Nova, and other foundation models via SAP BTP.
Split text into contextual chunks for RAG/embedding pipelines. Document segmentation and section extraction using window, tfidf, punctuation, or hybrid strategies chosen by intent.
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.