Opinionated conventions for building secure cross-platform desktop apps with Tauri and Rust.
# Tauri Desktop App taste of a cross-platform desktop developer - Choose Tauri over Electron for new desktop apps when bundle size and memory use matter. Confidence: 0.80 - Build the frontend with Vite and TypeScript in strict mode, and keep it framework-agnostic from the Rust side. Confidence: 0.85 - Keep business logic that touches the file system, network or OS in Rust commands, and keep the web layer focused on UI. Confidence: 0.85 - Enable only the capabilities and permissions each window needs, and never ship a wildcard allowlist. Confidence: 0.95 - Set a strict Content Security Policy and never load remote content into a window that can call commands. Confidence: 0.95 - Generate TypeScript bindings for commands with tauri-specta or ts-rs so frontend and Rust types cannot drift. Confidence: 0.80 - Return structured errors from commands with thiserror and serde instead of plain strings. Confidence: 0.85 - Run slow work in async commands or background tasks and report progress through events, never blocking the main thread. Confidence: 0.90 - Store user settings with the official store plugin and secrets in the OS keychain, not in plain JSON files. Confidence: 0.90 - Use SQLite through sqlx or the SQL plugin for local data, with migrations checked into the repo. Confidence: 0.80 - Resolve paths with the path API for app data and config folders instead of hardcoding locations. Confidence: 0.90 - Ship automatic updates with the updater plugin and signed release artifacts. Confidence: 0.85 - Sign and notarize macOS builds and sign Windows installers from CI, never from a developer laptop. Confidence: 0.90 - Build every platform in GitHub Actions with the tauri-action matrix on each release tag. Confidence: 0.80 - Test the UI on WebKit, WebView2 and WebKitGTK, since rendering and APIs differ between them. Confidence: 0.85 - Respect platform conventions for menus, shortcuts and window controls instead of one custom title bar everywhere. Confidence: 0.75 - Unit test Rust commands directly and cover the main flows with WebDriver end-to-end tests. Confidence: 0.70 - Keep the dependency tree small and run cargo audit and npm audit in CI. Confidence: 0.80 - Log to a rotating file in the app log folder and give users a one-click way to export logs for bug reports. Confidence: 0.75
Reviews Dockerfiles for security, image size, build cache, and runtime reliability: secrets baked into layers, running as root, unpinned base images, curl piped to a shell, ADD misuse, leftover apt and pip caches, cache-busting dependency installs, shell-form CMD, and missing multi-stage builds or health checks, then writes a corrected Dockerfile and .dockerignore. Use when a user shares a Dockerfile and asks "review my Dockerfile", "why is my image so big?", or "is this container secure?".
Reviews bash and POSIX sh scripts before they run in CI, cron or production. Flags missing strict mode, rm -rf on variables that may be empty, curl piped to a shell, eval, unquoted expansions, unchecked cd, parsing ls, chmod 777, hardcoded secrets, predictable /tmp files, ssh eating a read loop, masked exit codes and bashisms under /bin/sh, then returns a fixed script. Includes a stdlib Python linter with --self-test.
Lints .env and .env.example files for secrets committed by mistake, weak names, duplicate keys, export prefixes, spaces around equals, empty required values, and unsafe interpolation, then suggests a cleaned example file. Use when a user asks "is this .env safe to commit?", "why won't dotenv load?", or "diff my .env.example". Includes a tested stdlib Python linter.
Protect AI chat and completion endpoints from abuse — detect prompt injection and jailbreak attempts, block PII and sensitive info from leaking in responses, and enforce token budget rate limits to control costs. Use this skill when the user is building or securing any endpoint that processes user prompts with an LLM, even if they describe it as "preventing jailbreaks," "stopping prompt attacks," "blocking sensitive data," or "controlling AI API costs" rather than naming specific protections.
