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Tauri 2.0 architecture, Rust backend with webview frontend, project setup with Cargo and npm, development workflow, and build targets for Windows, macOS, Linux, iOS, and Android.

Tauri 2.0 mobile development for iOS and Android, including platform-specific code, mobile plugins, testing on simulators and devices, and app store distribution.

Common Tauri 2.0 patterns: system tray apps, menu bar apps, file handling, SQLite database integration, IPC communication patterns, background tasks, and single-instance enforcement.

Tauri 2.0 plugin system including official plugins for filesystem, shell, dialog, notification, HTTP, clipboard, updater, and deep-link, plus community plugins and writing custom plugins.

Tauri 2.0 security model including capability-based permissions, allowlist configuration, Content Security Policy, IPC safety, sandboxing, code signing, auto-update security, and supply chain considerations.

Bun's built-in bundler: Bun.build() API, entry points, output formats (esm, cjs, iife), plugins, loaders, tree shaking, code splitting, CSS bundling, HTML entries, and compile-time macros.

Bun runtime overview: all-in-one JavaScript runtime, bundler, test runner, and package manager. Installation, project initialization, Node.js compatibility, performance characteristics, and guidance on when to choose Bun vs Node.

Building HTTP servers with Bun: Bun.serve() API, routing patterns, WebSocket support, streaming responses, static file serving, TLS configuration, hot reloading, and integration with frameworks like Hono and Elysia.

Migrating from Node.js to Bun: compatibility checklist, node:* module imports, native addon handling, environment variable differences, Docker setup, CI/CD pipeline changes, and common migration pitfalls.

Bun as a package manager: bun install, bun add, bun remove, the binary lockfile (bun.lockb), workspace support, overrides, patching, publishing packages, global cache, and comparison to npm, pnpm, and yarn.

Production patterns for Bun: Docker deployments, TypeScript configuration, shell scripting with Bun.$, monorepo setup, database access patterns, and deployment to Fly.io and Railway.

Bun-native runtime APIs including Bun.serve(), Bun.file(), Bun.write(), Bun.spawn(), Bun.sleep(), Bun.env, FFI for calling native libraries, built-in SQLite, S3 client, glob, and semver utilities.

Bun's built-in test runner: bun test command, describe/it/expect assertions, mocking and spies, snapshot testing, lifecycle hooks, DOM testing with happy-dom, code coverage, and watch mode.

Communicate with customers during an active incident — status page, email, in-app banners, social media. The engineering decisions are hard but separable from the communications decisions; this skill covers the latter. Use when an incident has customer impact and the question of what to tell them, when, becomes pressing.

Serve as the incident commander during an active production incident. The IC coordinates the response, tracks the status, communicates with stakeholders, and makes the calls. Distinct from the engineers investigating root cause. Use when an incident exceeds what one engineer can handle alone.

Write runbooks the on-call engineer at 03:00 AM can actually follow. Covers structure, decision points, escalation criteria, and the difference between procedural runbooks and diagnostic runbooks. Use when documenting any production system that can fail in ways the primary on-call may not have seen before.

Define a severity scale that triggers the right response without overreaction or underreaction. Covers SEV-1 through SEV-4, the customer-impact criteria, the response expectations per level, and how to handle escalation and de-escalation during an incident. Use when designing or reviewing your team's incident management process.

Write postmortems that turn outages into learning, not blame. Covers the blameless framing, the timeline reconstruction, the contributing-factors analysis, and the action-items discipline. Use after any production incident, near-miss, or significant degradation that the team needs to learn from.

Integrate static and dynamic application security testing into the CI/CD pipeline so security checks run on every PR and before every deploy. Covers tool selection, gating policy, false-positive management, and reporting. Use when adding security automation to an existing pipeline.

Encode security policy as version-controlled, testable artifacts that the build pipeline enforces — instead of as PDF documents or wiki pages. Covers policy frameworks (OPA, Cedar), guardrails, automated audits, and the cultural shift from review-based to enforcement-based security.

Build the detection layer that catches attacks in production — log collection, signal-to-noise tuning, alert prioritization, and the detection-engineering discipline. Distinct from prevention; detection is the layer that fires when prevention fails.

Defend against supply-chain attacks: malicious dependencies, typosquats, compromised build pipelines, unsigned artifacts. Covers SBOM generation, artifact signing, dependency lockfile discipline, and supply-chain monitoring. Use when designing a hardened build and release pipeline.

Run a threat modeling session as part of a design review for any significant new feature or system. Covers the STRIDE framework, attack-tree decomposition, asset identification, and the artifact the modeling produces. Use when designing or reviewing any system with security implications.

Connect an LLM agent to a browser to perform tasks: navigation, form filling, data extraction, multi-step workflows. Covers tool design, the Page-as-context pattern, screenshot-based vision, and the failure modes specific to LLM-driven browsing. Use when building agents that interact with the web.

Diagnose and fix flaky end-to-end tests. Covers the categories of flake (timing, ordering, environment, real bug), the diagnostic process, and the architectural patterns that prevent flakes. Use when a test suite has flake rates above 1% and the team is losing trust in CI.

Use Playwright to drive browsers reliably across Chrome, Firefox, and WebKit. Covers selectors, waiting strategies, network interception, parallelism, and the patterns that distinguish flaky test suites from reliable ones. Use for E2E testing, web scraping, and any agent-driven browser task.

Build scrapers that run reliably across thousands of pages, handle rate limits, evade detection, and produce clean structured output. Covers proxy strategy, rate limiting, CAPTCHA handling, the legal and ethical considerations, and the architecture of a scraper that doesn't break weekly.

Design the tool-permission model for an LLM agent so that compromise of the model has bounded blast radius. Covers per-tool risk classes, confirmation flows, capability scoping, and audit logging. Use when building agents with any tools that have real-world side effects.

Defend against prompt injection delivered via tool outputs — fetched webpages, retrieved documents, third-party API responses, RAG context. The attacks the user didn't author but the model still acts on. Use when building agents with tool access and any path through which untrusted content reaches the model.

Sanitize user input before passing it to the LLM to reduce injection attack surface. Covers content filtering, structural separation, prompt sandwiching, and the limits of input-side defenses. Use as one layer of an LLM application's defense in depth.

Recognize the categories of prompt injection attack — direct injection, indirect injection via tool output, jailbreaks, exfiltration, and the failure modes specific to LLM applications. Foundation for designing defenses. Use when building any LLM application that processes untrusted input or has tool access.

Coordinate multiple specialized agents on a single task — when to hand off, what context to transfer, and how to avoid the failure modes specific to multi-agent systems. Covers supervisor/worker patterns, peer collaboration, and the blackboard model. Use when building systems where one LLM agent isn't enough.

Design the tools that an LLM agent uses. Covers naming, parameter design, return formats, error handling, and the difference between tools that LLMs use successfully and ones they consistently misuse. Use when building or refining the tool set for any agent.

Build the eval suite that tells you whether changes to your agent improve it or regress it. Covers eval-set construction, the metrics beyond pass/fail, the cost of running evals, and the practice of treating evals as the source of truth for agent behavior. Use when building any agent system you intend to ship and iterate on.

Use LangGraph (or equivalent state-machine frameworks) to express agent workflows as explicit graphs of nodes and edges. Covers the state-machine model, conditional edges, persistence, and the patterns that distinguish well-structured agent workflows from spaghetti Python with control-flow scattered everywhere.

Build the episodic memory layer that stores specific past events and surfaces them when relevant. Covers event extraction, structured vs. unstructured storage, decay strategies, and the patterns that distinguish helpful episodic memory from noisy "remember everything" systems. Use when an agent needs to remember concrete past interactions across sessions.

Build the agent's accumulated model of the user — preferences, expertise, context, recurring patterns. The "what we know about this user" layer that shapes every interaction. Distinct from episodic memory (specific events). Use when designing an agent that should adapt to individual users over time.

Design the memory architecture for a stateful agent — what's in context, what's in retrieval, what's in episodic store, what's in semantic store. Covers the boundaries and the tradeoffs that distinguish well-architected memory from naive blob retrieval. Use when an agent needs to remember things across turns or sessions.

Implement an agent memory system using a vector database with retrieval- augmented generation. Covers chunking strategies, embedding choices, hybrid retrieval, reranking, and the patterns that produce useful retrieval rather than noisy retrieval. Use when scaling agent memory beyond what fits in context.

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