Visibility winner
safishamsi/graphify
7.4K stars
Use this as the long-horizon mindshare read, not as the final answer.
Freshness winner
safishamsi/graphify
+1.1K in the weekly window
Last commit Apr 7, 2026
Adoption winner
PrathamLearnsToCode/paper2code
Package mapping still partial
GitStar keeps the comparison directional when linked package telemetry is missing.
Data completeness
Comparison confidence
0/3 package links
2/3 repos have weekly momentum in the current snapshot.
Compare up to three repositories with comma-separated owner/repo names. GitStar keeps missing package or momentum data explicit instead of flattening it into a false zero.
safishamsi/graphify currently leads on long-horizon mindshare with the strongest star base in this comparison. Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
GitStar keeps cross-repository gaps visible. Missing package mappings or partial momentum data lower confidence, but they do not stop the page from showing a directional read.
safishamsi/graphify currently leads on long-horizon mindshare with the strongest star base in this comparison.
Stars are strongest at showing durable visibility, not direct production fit.
safishamsi/graphify has the strongest current movement signal in this set.
Weekly movement is +1.1K in the weekly window.
Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
Treat this as a directional evaluation and verify ecosystem usage directly from package registries.
All compared repos live in the Python ecosystem, but they do not cleanly share the same category footprint.
Treat this as a directional stack comparison and verify the actual problem each repo solves.
Agent skill to turn any arxiv paper into a working implementation
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw, Factory Droid). Turn any folder of code, docs, papers, or images into a queryable knowledge graph
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.