Explore Projects
10 analyzed repos ready for contributors.
davidichalfyorov-wq/sct-theory
SCT Theory is a solo research workspace implementing 'Spectral Causal Theory', a theoretical physics program attempting to derive quantum gravity corrections from the spectral data of the Dirac ope...
people interested in computational mathematical physics, specifically heat kernel methods, spectral geometry, and numerical verification of quantum field theory calculations in curved spacetime
dpearson2699/swift-ios-skills
A collection of 56 markdown-based 'agent skills' — structured documentation files (SKILL.md) targeting iOS 26+, Swift 6.2, and modern Apple frameworks like Liquid Glass, SwiftData, and Foundation M...
iOS developers who want to give AI coding assistants accurate, non-deprecated iOS 26 / Swift 6.2 context — especially for newer APIs that training data won't cover well (Liquid Glass, AlarmKit, EnergyKit, Foundation Models, PermissionKit)
farenhytee/supabase-sentinel
Supabase Sentinel is a Claude AI 'skill' — essentially a structured markdown prompt system — that guides Claude through a 7-step security audit of Supabase projects. It checks for RLS misconfigurat...
People interested in Supabase security hardening and the emerging pattern of AI-native tooling built entirely as structured prompts rather than traditional code
ratherlegit/environmental-impact-tracker
A Claude Code 'skill' (essentially a prompt/instruction file) that instructs Claude to estimate and display the environmental cost of AI interactions — translating token counts into energy (Wh) and...
People interested in AI transparency tooling and environmental cost awareness for LLM usage — specifically those building or extending Claude Code skill/plugin systems
jnemargut/better-plan-mode
Better Plan Mode is a single markdown prompt file (SKILL.md) that instructs AI coding tools like Claude Code, Cursor, or Codex to run an enhanced planning workflow. Instead of quick yes/no decision...
People interested in prompt engineering for AI coding assistants, specifically crafting structured instruction sets that shape how LLMs handle multi-step planning workflows
onestardao/wfgy
WFGY is a documentation-heavy AI troubleshooting framework centered on a '16-problem map' for diagnosing broken RAG pipelines and AI agents. The practical code layer consists of thin LangChain/Llam...
People interested in structuring AI debugging workflows for RAG systems — specifically mapping hallucination and retrieval failure modes to diagnostic categories and first-fix strategies.
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