DeepPaperNote is an agent skill for deep-reading a single paper and generating high-quality Obsidian-style research notes. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
Human Infra: docs-as-code knowledge base for human runtime infrastructure, longevity evidence, future-waiting paths, disembodied CNS concepts, and memory-editing boundaries.
A markdown template for taking notes to summarize research papers.
Obsidian-first LLM Wiki skill pack with ontology-ready bootstrap, canonical JSONL truth layers, and optional graph projection.
Turning same-topic mixed materials into style-controlled Markdown notes, outlines, knowledge frameworks, and presentation outlines.
Local Markdown workspace for technical writing, research notes, security blogs, and static site publishing.
A ChatGPT-to-Obsidian capture plugin for AI-assisted learning and research notes.
Sunzi Reading / 孙子读论文 Skill: turn dense academic papers into short, warm, beginner-friendly explanations and reading notes.
Research notes on maintaining and reasoning about LLM context windows across long-lived projects. Speculative ideas, heuristic boundaries, and design sketches without empirical validation.
Structured workflow notes for Zn battery electrolyte and interface analysis
Deliver deep research with a 5-layer Claude skill for markets, products, competitors, regulations, and opportunities
Stanford AI for Lean Club progress on Erdős problems: papers, frontier notes, visualizer data, and Lean formalization.
Mathematical modeling workspace for MCM-style analysis, report drafting, visualization, and reproducible experiment notes.
An experimental research project developing a foundational theory of frameworks — the structures through which the human mind constructs and engages with reality.
A framework for clustering research notes
前沿物理仿真与智能感知技术调研资料库 | Frontier physics simulation research notes
An introduction to actors with working Akka example to demonstrate an actor based approach to concurrency and it's affect of software design on scalability.
SDFI emerges specifically under conditions of recursive self-description and sustained high semantic density, not in ordinary task-oriented interaction.This work is intended as a reference for researchers and system designers thinking about neutrality, termination behavior, and control surfaces in future AI systems.
Notes & experiments on LLMs, open-weight models, multimodal systems, and cloud deployment.
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