Today's GitHub trending makes one theme unmistakable: AI agents are moving beyond code generation into every corner of knowledge work. The top-gaining repos this Tuesday cover research synthesis, job hunting, hardware-matched model selection, and even visual design — a sign that the agent skill ecosystem is maturing fast and spreading well past developer workflows.
mvanhorn/last30days-skill: Social-Signal Research for Agents
last30days-skill is an agent skill that researches any topic by querying Reddit, X, YouTube, Hacker News, Polymarket, and the web simultaneously, then ranks results by real engagement — upvotes, view counts, prediction-market odds — instead of SEO position. The output is a grounded, cited brief that reflects what people are genuinely engaging with, not what content farms are surfacing. It picked up over 3,100 stars today alone, making it the biggest mover on the platform.
github.com/mvanhorn/last30days-skill
RyanCodrai/turbovec: High-Performance Vector Search in Rust
turbovec is a vector index built on TurboQuant, a novel quantization technique, implemented in Rust with Python bindings for easy integration into AI pipelines. It gained nearly 1,800 stars in a single day, reflecting real appetite from developers building RAG systems who need something faster and more memory-efficient than established libraries. The Rust-first approach means you no longer have to trade Python ergonomics for embedding throughput at scale.
github.com/RyanCodrai/turbovec
santifer/career-ops: AI-Powered Job Search System
career-ops turns Claude Code into a full job-search automation platform with 14 skill modes — resume tailoring, cover letter drafting, company research, interview prep, and outreach sequencing — backed by a Go dashboard, PDF generation, and batch processing. The project crossed 52,000 stars and added over 1,100 today, suggesting developers are finding real value in applying agent harnesses to workflows that have nothing to do with writing software.
github.com/santifer/career-ops
Andyyyy64/whichllm: Match Models to Your Actual Hardware
whichllm addresses a problem every local-LLM enthusiast faces: published benchmarks are run on data-center hardware that has nothing in common with your laptop or workstation. Instead of relying on those numbers, whichllm measures tokens per second, memory consumption, and first-token latency on your specific machine and recommends models you'll actually be happy running. As the catalogue of available local models keeps growing, this kind of hardware-aware guidance becomes increasingly valuable.
pbakaus/impeccable: A Design Language Built for AI Interfaces
impeccable is a design language and component system aimed specifically at AI harnesses — structured layout rules, typography guidelines, and interaction patterns that agents can follow when generating UIs or documents. It earned over 550 new stars today, pointing to a growing recognition that as AI-generated interfaces proliferate, consistent visual standards need to ship alongside the code-generation capabilities that produce them.