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帮助调整AgentDB的数据压缩、搜索索引、缓存和批量操作，并测量性能，以改善内存占用与搜索速度，权衡速度、精度和内存需求。

• 减少AgentDB内存占用
• 兼顾精度与搜索速度
• 规划数百万向量的配置

Represent dependency trees, taxonomies, and other hierarchies inside a Poincare ball, a geometric model for hierarchical distances. Use tree depth for radius and text embeddings for direction, then measure distances to examine the encoded structure.

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• Analyze subtrees in module dependencies
• Examine structural patterns in class hierarchies
• Organize concepts by taxonomic relationships

Search document collections by meaning using embeddings, numerical representations of text. Compare text similarity, route queries among a few predefined patterns, and use a compressed search index when memory is limited.

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• Finding documents with similar meaning
• Comparing the similarity of two pieces of text
• Reducing memory use for large vector collections

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

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• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Build vector search that considers both semantic similarity and patterns in how items connect. Use graph features to explore structure in embeddings, numerical representations of content, and compare retrieval results with conventional similarity search.

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• Investigate structure missed by cosine similarity
• Include contextual roles in RAG retrieval
• Compare graph-based and cosine search results

Build searches that find similar meanings using vector databases, which store numerical representations of data. Choose search indexes, combine semantic and keyword search, and tune retrieval quality and response time for search, recommendations, or AI answer systems.

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• Build search that finds documents with similar meanings
• Improve document retrieval for AI answers
• Design a recommendation engine

Build AI applications that answer questions using retrieved documents. Covers vector databases, text embeddings that represent meaning as numbers, search strategies, and result reranking to connect language models with external knowledge.

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• Building Q&A systems for internal documents
• Adding domain knowledge to chatbots
• Implementing natural language document search

Design and improve RAG systems, which retrieve documents to support language model answers. Explore meaningful document splitting, vector storage, combined keyword and semantic search, and relevance ranking while checking common causes of poor retrieval.

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• Design a system that answers questions using documents
• Review document splitting when search misses relevant content
• Combine keyword search with search based on meaning

Turn text into embeddings, numeric representations of meaning, through 9Router. Choose a model and process individual texts or batches to prepare data for meaning-based search, similarity comparisons, or RAG systems that retrieve reference material.

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• Preparing text vectors for meaning-based search
• Generating embeddings for text similarity comparisons
• Batch-processing reference passages for RAG

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

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• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

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• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Uses a completed ZKsync Era investigation with no reportable findings as a defense study. It explains 25 tested attack vectors, the safeguards that blocked them, and criteria for deciding whether to continue reviewing a large bridge protocol.

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• Learning defensive design from a completed ZKsync Era investigation
• Reviewing why 25 tested attack vectors failed
• Assessing whether to continue investigating a large bridge

Generate images, edit photos and vector artwork, and assemble narration and supplied clips into videos. Inspect visual details, timing, motion, and audio, then deliver finished files for viewing or download.

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• Generate an image from a written brief
• Edit photos and layered artwork
• Create vector artwork

Set up WizTelemetry Events to collect and store Kubernetes events in KubeSphere. Configure index retention policies and use the query API to filter warnings, inspect resource events, generate statistics, or export matching records.

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• Setting up Kubernetes event collection and storage
• Finding warnings for a specific resource or time range
• Generating event statistics and time-based counts

Set up KubeSphere’s WizTelemetry Data Pipeline to collect, transform, and route logs, audit data, events, and notifications. Configure delivery to OpenSearch, deploy agents across selected clusters, and check their operating status.

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• Setting up a shared collection pipeline for observability data
• Configuring delivery to OpenSearch
• Deploying agents across multiple clusters

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

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• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.

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• Choose services for a Cloudflare application
• Select storage for databases and files
• Build AI agents and semantic search

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

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• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Work through matrix eigenvalue problems, from the characteristic polynomial, whose roots give the eigenvalues, to finding eigenvectors. Follow a structured process to check the results and examine algebraic and geometric multiplicities.

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• Finding a matrix's eigenvalues and eigenvectors
• Computing a characteristic polynomial
• Checking eigenvalue and eigenvector results

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration

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Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration host character (who, where, tone, voice and music come from the recipe choices) walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.

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Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.

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Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

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