OysterWorkflow is an open-core desktop application (Electron + React) that records a user's computer activity via Screenpipe (screen, audio, UI events), then uses multi-stage LLM calls to extract reusable, structured 'skills' (workflow graphs) that AI agents (Codex, Claude, Hermes) can execute. The system provides a full lifecycle: capture → ingest → normalize → segment → LLM-based workflow discovery → skill extraction → workflow graph generation → quality evaluation → installation into agent skill directories → execution via MCP or Hermes CLI. It also includes a cloud sync layer (Supabase), a marketplace (ClawHub), and a local runtime HTTP server for the UI. Architecturally, it is a hybrid monolith with clear module boundaries: a desktop shell (Electron main process), a local runtime server (Express), a CLI (commander), a React UI (Vite + React Flow), and a product state layer (SQLite-backed with cloud sync). The codebase is TypeScript-first, ESM-only, and heavily tested (200+ test files). It demonstrates strong engineering practices: atomic file I/O, bounded child process management, immutable revision control for workflow graphs, and a sophisticated prompt registry with versioned inheritance. While primarily an application, it contains a significant data pipeline component (ingest → normalize → segment → LLM extraction) that could be considered a research-grade feature engineering system. The repo is actively maintained with frequent commits, CI, and issue triage. Notable risks include heavy sidecar bundling (Screenpipe, Hermes, ffmpeg), rapid prompt set version churn, and a mixed licensing model (Apache 2.0 open core + PolyForm Noncommercial for product code).