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Found 2,616 Skills
Alibaba Cloud Tablestore Agent Storage Skill. Use for building and managing Tablestore-based knowledge bases with the `tablestore-agent-storage` Python SDK. Capabilities: - Install and configure the `tablestore-agent-storage` SDK - Create, describe and list knowledge bases (with subspace and custom metadata support) - Upload local files or import OSS documents into a knowledge base - Query document status and list documents - Perform hybrid retrieval (dense vector + full-text) with metadata filtering - Set up local directory sync scripts and scheduled tasks for automatic knowledge base updates Triggers: "知识库", "tablestore", "ots", "表格存储", "agent storage", "knowledge base", "向量检索", "文档上传", "文档导入", "知识库同步", "tablestore-agent-storage", "AgentStorageClient"
Test coverage analysis, identify gaps, and generate missing tests to achieve over 80% coverage. Trigger conditions: user requests for test coverage analysis, test coverage improvement, and test writing.
[Pragmatic DDD Architecture] Guide for Server and Client components in Next.js App Router. Use when creating any .tsx file under presentation/components/, pages, or layouts — also when deciding whether to add "use client" to an existing component, passing data from a Server Component to a Client Component, composing Server content inside a Client slot, handling the VO serialization boundary, creating Compound Components, separating logic for Mobile/Desktop screens, or styling with `cva` and `cn`. Covers: Server vs Client decision, async Server Component patterns, creating getSession callbacks for Use Cases, Client Component restrictions, toBranded() boundary pattern, children slot composition, and props interface rules. Depends on 'use-cases' and 'server-actions'.
Use the Paragraph REST API or TypeScript SDK to manage posts, publications, subscribers, and coins on paragraph.com. No installation required — just HTTP requests or `npm install @paragraph-com/sdk`. Trigger when the user asks to integrate with, build on, or call the Paragraph API.
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Manage models, datasets, columns, and relationships and query workspace storage with SQL using the Cargo CLI. Use when the user wants to inspect or modify data models, create or update columns, list datasets, set model relationships, understand the schema, or run SQL against storage.
Read-only Storage Analysis Assistant for macOS / Windows (auto-detects system). Scans the entire disk usage to identify space hogs, categorizes each item into three levels: 🟢 Auto-cleanable / 🟡 Manual judgment required / 🔴 Clean with caution, and provides actionable disposal plans. Generates an interactive HTML report with beautiful formatting, collapsible sections, and one-click copy commands. Also supports starting a local service to delete files directly via the web (move to trash / delete immediately). The entire scanning process is read-only. Must be used in the following scenarios: When users mention "storage analysis", "disk full", "C drive/hard disk full", "insufficient space", "clean up space", "disk cleanup", "space occupied", "what's taking up space", "help me check storage", "check computer storage/space", "storage space", "computer space insufficient", "memory full/insufficient" (in Chinese colloquial, "memory" often refers to storage), "storage analysis", "disk cleanup", "clear cache", "disk cleanup"; or when users complain about insufficient computer space, want to know what's taking up hard disk space, or need cleanup suggestions. Note: If users explicitly refer to RAM (e.g., "which process is using memory", "high memory usage", want to see Activity Monitor), that's RAM, not storage, and does not belong to this skill.
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.
Kubernetes storage management for PVCs, storage classes, and persistent volumes. Use when provisioning storage, managing volumes, or troubleshooting storage issues.
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Comprehensive toolkit for validating, linting, testing, and automating Terragrunt configurations, HCL files, and Stacks. Use this skill when working with Terragrunt files (.hcl, terragrunt.hcl, terragrunt.stack.hcl), validating infrastructure-as-code, debugging Terragrunt configurations, performing dry-run testing with terragrunt plan, working with Terragrunt Stacks, or working with custom providers and modules.
TDD and coverage standards for Engram. Trigger: When implementing behavior changes in any package.