Provides a brain layer for AI agents that synthesizes answers, traverses a self-wiring knowledge graph, and highlights gaps in team knowledge. Ships hybrid retrieval, citation-aware synthesis, and MCP integrations for Claude/Codex to power meeting prep and company-wide memory.
Lets AI coding agents compile your documents and chat histories into a maintained Obsidian vault: it ingests sources, distills them into interconnected markdown pages, tracks deltas and provenance, and exposes query/lint/export skills across many agents.
Turns source documents into a linked, source-cited Obsidian wiki for grounded Q&A and research. Local-first vault workflows with content-addressed capture, provenance ledgers, BM25 retrieval, and Claude Code / Agent Skills integration.
An AI-agent value-investing research framework for Claude Code/Codex that encodes Buffett/Munger/Duan Yongping/Lilu methodologies into multi-agent skills — enforces decisive buy/sell outputs, multi-source financial rigor, and reproducible research workflows for investment decision-making.
Automates video editing driven by LLM agents: reads word-level transcripts to propose and execute cuts, remove filler words, auto grade color, burn subtitles, and generate animation overlays. Self-evaluates every cut before showing a preview; aimed at talking-heads, tutorials and interviews.
Turns books, long videos, and podcasts into executable, testable AI agent skills using a structured RIA‑TV++ pipeline. Produces multi-file skill packs (BOOK_OVERVIEW.md, SKILL.md, INDEX.md, DIGEST.md), applies triple verification and pressure tests, and can install skills into Claude Code/Cursor for agent use.
Runs multiple AI agents in parallel inside a single macOS browser, giving each agent an isolated Space that can use your real logins without touching your tabs; controllable via an ego-browser JavaScript skill to perform web automation with fewer tokens and faster task completion.
Synthetic JSON dataset of model-generated prompts and step-by-step reasoning traces (≈90k rows, ~75M tokens) created with Claude Sonnet 4.6 and cross-checked by Gemini 3.1 Pro — intended for training or fine-tuning LLMs on natural reasoning, multi-domain code/math, and instruction following. Hosted on Hugging Face, MIT license.
A curated collection of production-ready Agent Skills that turn tasks—presentation production, image generation, local KB retrieval, article assembly, and web-design—into agent-loadable skill folders. Uses a SKILL.md contract, supports multiple agent runtimes (Claude Code, Cursor, Codex), and offers modular install paths with pinned release zips.
Provides a single MCP endpoint that lets AI coding agents search AWS docs, run sandboxed Python scripts, and make authenticated AWS API calls with enterprise guardrails like IAM condition keys, CloudWatch metrics, and CloudTrail auditing.
Enables Claude to “watch” videos by extracting timestamped frames plus captions/transcripts and feeding them to Claude for grounded Q&A. Key features: native captions first, Whisper fallback, frame deduplication, and multiple detail modes (transcript/efficient/balanced/token-burner). Useful for summarizing, debugging, and extracting moments.
Generates page-scale UI designs and audits for Claude Code, Cursor, and Codex using a 57-gate “anti-AI-slop” rule set — produces distinct, non-template HTML+CSS outputs and supports audit, redesign, and study verbs with a built-in pre-emit self-critique.