Alternatives to embeddings
50 AI Skills apps to use instead, best rated first.
Top picks
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webapp-testing
LaunchNative Python Playwright scripts for testing local web applications with server lifecycle management. Includes with_server.py helper to automatically start and manage single or multiple servers before running automation scripts. Supports re
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content-research-writer
LaunchAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on
- O
openhue
Launchopenhue is an agent skill from the OpenClaw project, listed on skills.sh. It lets an agent control Philips Hue lights and scenes through a Hue Bridge with the OpenHue CLI: turn lights on and off, dim rooms, set scenes such as movie mode, an
- B
batch-grill-me
Launchbatch-grill-me is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It interviews you relentlessly about a plan until you share an understanding. It maps decisions as a tree and asks every question it can answer now
- I
implement
Launchimplement is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It implements work from a spec or tickets: it fetches the ticket, works test-first at agreed seams, runs type checks and tests as it goes, reviews the re
- F
frontend-design
LaunchDistinctive, production-grade frontend interfaces that reject generic AI aesthetics through intentional design choices. Guides aesthetic direction selection (brutalist, maximalist, retro-futuristic, luxury, organic, etc.) before implementat
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brainstorming
Transform rough ideas into fully-formed designs through structured questioning
finishing-a-development-branch
Guides completion of development work by presenting clear options
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
About embeddings
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support
embeddings is an agent skill from the ruflo project for working with vector embeddings, used for semantic search and pattern matching. It stores vectors in a persistent cache built on sql.js, a SQLite build that runs as WebAssembly so it works across platforms, and it searches them with HNSW indexing, a graph-based index for fast nearest-neighbour lookup. The skill also supports a Poincaré ball model for hierarchical data, and it offers normalization with L2, L1, min-max and z-score methods, and chunking with configurable size and overlap, which is useful for splitting long documents before they are embedded. The skill's features table gives the claimed speedups, including a search that is 150 to 12,500 times faster with HNSW and 75 times faster with the agentic-flow ONNX integration. Those are the skill's own figures and depend on the data and hardware, so measure them on your own data. It has a list of commands for embedding and searching, and the skill says to use it for semantic search, pattern matching and similar lookups. It suits developers adding search over notes, code or past agent results to an application. The skill passes Gen Agent Trust Hub, Socket and Snyk on skills.sh, and it has about 1,200 installs. Install it with npx skills add https://github.com/ruvnet/ruflo --skill embeddings.
- Price
- Free
- Made by
- ruvnet
- Category
- AI Skills
- Website
- github.com/ruvnet/ruflo/embeddings