Backblaze B2 cloud storage via b2sdk Python SDK. Auth, upload, download, buckets, keys, large files, sync.
Docker on Mac with OrbStack. Containers, Dockerfiles, images, Compose, BuildKit, volumes, networking. Use when working with Dockerfiles, compose.yaml, docker/docker compose/buildx commands, or containerizing any service.
Electron desktop app development across macOS and Windows. Including security, performance, using code beyond Javascript & Typescript, native APIs, storage, debugging and troubleshooting issues, storage, and electron best practices. Use when working with electron code, troubleshooting, testing, preparing for release.
Build Electron apps for macOS with Next.js/React/TypeScript/Bun. Covers project setup, main/renderer/preload architecture, IPC, macOS window styling, vibrancy sidebar, traffic lights, native file system, dialogs, audio capture, desktopCapturer, storage (electron-store, SQLite), native modules (N-API, node-gyp, @electron/rebuild), memory optimization (V8 cage, leaks, window pooling), performance (startup, main-thread, virtualization), permissions and entitlements, notifications, dock/tray/menus, deep links, auto-update (electron-updater, Squirrel.Mac), electron-builder packaging, DMG, code signing, notarytool notarization, universal arm64+x64 builds, Mac App Store, context isolation and contextBridge security. Use when building, debugging, packaging, or distributing any Electron app targeting macOS.
Relevant when connecting your app/code with Google Calendar API v3 via TypeScript/JavaScript. Includes OAuth2, token refresh, CRUD on events, recurring events, conference/join links, RSVP, attendees, incremental sync, push notifications, and more.
When using the `datasets` library, `huggingface_hub`, or working with datasets on the HuggingFace Hub. Covers loading, streaming, resumable download, partial access, discovery, creation, upload, folder builders, WebDataset, data cards, caching, FAISS, and integrations.
Find & remove duplicate/near-duplicate images using perceptual hashing (PHash, AHash, DHash, WHash) and CNN embeddings. Use when deduplicating image datasets or comparing image similarity in python. Libraries: imagededup and imagehash
Build LLM evaluations with Inspect AI (inspect-ai Python package by UK AISI). Use this skill whenever the user mentions Inspect AI, inspect-ai, LLM evaluation frameworks, eval tasks, eval datasets, solvers, scorers, agent evals, model grading, agentic benchmarks, SWE-bench evals, or any task involving evaluating language models systematically. Also trigger for questions about sandboxing model code execution, tool-use evals, multi-agent evaluation, eval log analysis, or running benchmarks like MMLU, HumanEval, GSM8K, HellaSwag, ARC with Inspect. Even if the user just says "write an eval" or "benchmark this model", consider this skill.