Generate a dense vector-embedding prompt for precise RAG semantic search.
npx skills add https://github.com/anthropics/skills --skill template-skill
Please help me install this Agent Skill. Skill: template-skill Source: https://github.com/anthropics/skills/tree/main/template-skill Command: npx skills add https://github.com/anthropics/skills --skill template-skill
This Skill provides a foundational structure, eliminating the need to start from scratch when developing new AI automation capabilities. It streamlines the initial setup, allowing you to focus directly on core logic and functionality.
SKILL.md format, ensuring consistency across all your custom AI Skills.High-intent search queries and answers about installing and using this skill.
Create a custom AI skill by using a pre-defined directory layout and SKILL.md format with YAML frontmatter. This template-based approach eliminates setup overhead, letting you focus on core automation logic and functionality instead of building from scratch.
Generate vector-embedding-dense prompts that produce action-oriented search instructions for RAG workflows. The prompt structure—three declarative sentences without filler—yields embedding-ready queries for semantic search across knowledge bases, documents, and skill units.
Yes. This template provides the standardized SKILL.md structure and essential YAML frontmatter needed to quickly establish file organization and documentation for automating any specific business process without starting from scratch.
Use a three-sentence declarative format with no filler to structure your semantic search query. This constraint produces embedding-ready prompts optimized for precise retrieval across document sections, fact extraction, and knowledge base lookups in RAG systems.
The SKILL.md template includes YAML frontmatter with name and description fields, plus a structured section for automation instructions. This foundation accelerates development by providing a consistent format across all custom AI skills.
The template generates English-only output optimized for semantic search across diverse domains and use cases. The three-sentence structure applies uniformly to section lookup, fact extraction, and knowledge base retrieval regardless of subject matter.
dgriffin831Normalize whitespace, strip HTML tags, and handle Markdown in raw text.
danilonovaisvEnable fast semantic search over document collections using AgentDB.
cardmagicClassify text sentiment and spam using pre-trained CLI models.
wshobsonImplement hybrid search combining vector similarity and keyword retrieval with fusion methods.