Whenever I need an AI agent skill, I end up searching GitHub, Reddit, X, and random recommendations.
Maybe I'm missing something, but is there a trusted directory/registry for AI agent skills yet?
Right now it feels hard to know what's actually good, maintained, and safe to install.
Made these with a meme generation skill I built while experimenting with AI agents. Thought they were funny enough to share xD
I built a meme generation skill for AI agent memes.
It cooked up these. Thought you folks would appreciate them. xD
If you want to try it:
https://skillpatch.dev/skill/meme-generation
Hi guys,
This first image is our knowledge graph.
While building AI coding tools, we realized static code graphs are great at understanding explicit relationships like symbols, imports and calls, but they miss architectural intent, runtime relationships, coding conventions and historical context from commits and PRs.
So we built LatentGraph (https://lgraph.dev/), a dynamic relationship graph that combines static analysis, AI-inferred relationships and Git history to give coding agents a richer understanding of a codebase.
This approach achieved the highest retrieval precision across the repositories we tested compared to the other knowledge graph approaches we evaluated.
If you want to try it:
Or explore it on live repositories first (no signup required):
https://lgraph.dev/showcase
Benchmarking blog: https://blogs.latentforce.ai/latentgraph-benchmark
GitHub: https://github.com/LatentForce-ai/latentgraph-mcp-server (a ⭐ is appreciated :) )
We're still early. We've already thrown away multiple graph designs because some relationships added noise, while others we almost ignored turned out to be surprisingly valuable.
What do you think? Open to feedback and ideas :)
Hi guys,
This first image is our knowledge graph.
While building AI coding tools, we realized static code graphs are great at understanding explicit relationships like symbols, imports and calls, but they miss architectural intent, runtime relationships, coding conventions and historical context from commits and PRs.
So we built LatentGraph (https://lgraph.dev/), a dynamic relationship graph that combines static analysis, AI-inferred relationships and Git history to give coding agents a richer understanding of a codebase.
This approach achieved the highest retrieval precision across the repositories we tested compared to the other knowledge graph approaches we evaluated.
If you want to try it:
Or explore it on live repositories first (no signup required):
https://lgraph.dev/showcase
Benchmarking blog: https://blogs.latentforce.ai/latentgraph-benchmark
GitHub: https://github.com/LatentForce-ai/latentgraph-mcp-server (a ⭐ is appreciated :) )
We're still early. We've already thrown away multiple graph designs because some relationships added noise, while others we almost ignored turned out to be surprisingly valuable.
What do you think? Open to feedback and ideas :)