dotfiles

Context pressure checkpoint, self-handoff, and durable lesson capture workflow. Use when an agent is near the context limit, context usage is above 98%, auto-compaction or context warnings appear, the user says high tide, before `/clear`, or after a long confusing/debugging session where lessons learned should be preserved for the next fresh session or written to project-local `docs/.ai/lessons/`.

Generate execution-ready, model-specific prompts for handing work to a different LLM agent or fresh session. Use for shared-workspace delegation, cold-start continuation, cross-model transfer, one-shot worker directives, or requests such as "create a handoff prompt", "delegate this", "hand this off", and "prepare context for another agent". Supports current Anthropic Claude, OpenAI GPT/Codex, Google Gemini, and xAI Grok families.

Surface hidden unknowns before, during, and after implementation using structured techniques — clickable mocks, intervention brainstorms, semantics maps, tweakable plans, implementation notes, buy-in docs, and more. Use PROACTIVELY when entering an unfamiliar codebase or domain, when a feature request is ambiguous or underspecified, when the user says "I'm not sure", "I'll know it when I see it", or has no design direction, when porting from a reference implementation, when writing an implementation plan, before merging a large diff, or when packaging work for reviewer sign-off. Also triggers on explicit asks: "blindspot pass", "interview me", "quiz me before I merge", "show me design directions", "teach me <domain>", "what am I missing?".

Profile Rust code using samply and related evidence to identify CPU bottlenecks, allocation hot paths, and allocator-fragmentation symptoms. Use when performance is slow, RSS grows unexpectedly, before optimizing, or when the user asks to profile.

Measure and improve Go program performance on modern Go (1.24+). Use when profiling Go code, diagnosing CPU or memory bottlenecks, investigating latency or contention, writing or fixing benchmarks, comparing benchmark results, using pprof or trace data, applying PGO, or tuning hot-path Go code.

Behavior-preserving cleanup for recently changed code. Use after implementing or modifying code, when explicitly invoked as `$code-simplifier`, or when the user asks to simplify, clean up, or refactor the current diff without widening scope.

Decompose large agent-built features into atomic, reviewable chunks using a diff-size budget (~1500 lines) as the signal. Use this whenever implementing a non-trivial or net-new feature with an agent, when an agent has produced a huge diff/PR that is painful to review, when the user says a change is "too big", "too much to review", or "should be broken up", or when planning how to parallelize feature work across multiple agents. Reach for it on any "build this whole feature" request, even when the user does not explicitly ask to decompose — getting under the review-ability threshold is the goal. Skip it for small, self-contained features that will obviously land well under the threshold, and for large-but-mechanical sweeps (renames, codemods, lockfile churn) where the diff is big but trivially reviewable.

Build terminal UIs with ratatui following 2026 Rust best practices. Use when: (1) Creating new TUI apps, (2) Adding widgets/layouts, (3) Keyboard navigation/state management, (4) Image integration via ratatui-image, (5) Async event handling, (6) Shimmer/loading animations via tui-shimmer, (7) Reviewing TUI code, (8) Release optimization. Covers v0.30.1 API, Elm Architecture, StatefulWidget, color-eyre.

Become a wise, incredibly effective teacher who verifies the human deeply understands the work done in the current session — the problem, the solution, and the broader impact — before the session ends. Use whenever the user asks to be taught or walked through what happened ("teach me this session", "make sure I understand what we did", "explain these changes to me", "quiz me on this", "what did we actually change?"), before they review or commit work they didn't write line-by-line themselves, or any time they signal they want mastery rather than a summary — even if they don't say the word "teach".

Produce a single self-contained .html artifact (no build, no external assets) when the task is spatial, interactive, visual, multi-axis, or long-running. Triggers on "build me an HTML for X", "make a dashboard / status snapshot / comparison / decision tree / timeline / architecture map", "render this plan as a clickable site", or when content can't fit markdown structurally. Skips short replies, code-only outputs, simple plans.

Behavioral checklist for disciplined LLM coding. Use when starting non-trivial tasks to enforce assumption surfacing, minimal changes, and verifiable goals. Derived from Andrej Karpathy's observations on LLM coding pitfalls.

Save AI agent session information for later resumption. Use when: (1) Finishing a work session and want to save progress (2) User says "save session", "remember this session", or "save your session ID" (3) Before ending a long coding/debugging session (4) When user wants to be able to resume this conversation later Supports Claude, Codex, Gemini, Copilot, Cursor, and other AI agents.

Deliberate fresh-eyes self-review and repair after making changes. Use when an agent has just written or modified code, config, tests, or docs and should pause to look for obvious bugs, regressions, missing tests, confusing behavior, or risky assumptions, fix the clear local issues it finds, and only then finalize, hand off, or commit. Supports Claude Code, Codex, and Gemini with provider notes in references/.

Orchestrate multi-step work with Claude Code CLI as a bounded worker while Codex stays lead developer. Use when Codex should split a task into scoped sub-tasks, delegate planning or implementation to Claude with `claude -p`, run parallel Claude sessions, isolate risky edits in worktrees, define custom Claude subagents, or move repeated enforcement into Claude hooks/settings.

Run an external LLM code review with Codex CLI, Gemini CLI, or both. Use when the user asks for a second opinion, external review, Codex review, Gemini review, or wants a model-vs-model review of current changes, a branch diff, a specific commit, or a GitHub pull request.

Build, sign, and deploy iOS/macOS apps using Zed Editor and CLI-only workflows without Xcode UI. Use when: (1) Setting up a new iOS/macOS project with XcodeGen, (2) Configuring secure credential management via xcconfig files, (3) Setting up Zed Editor with Swift LSP, (4) Building/deploying to physical devices via CLI, (5) Creating CI/CD pipelines for Apple apps, (6) Troubleshooting CLI build issues. Covers XcodeGen, xcode-build-server, justfile commands, devicectl, and GitHub Actions scaffolding following 2026 best practices.

Use when profiling native macOS or iOS apps with Instruments/xctrace. Covers correct binary selection, CLI arguments, exports, and common gotchas.

Find and analyze screenshots from ~/Downloads/Screenshots/ for UI/UX review. Use when asked to look at screenshots, review UI designs, inspect interface errors, analyze layout issues, or examine visual problems. Triggers on "check my latest screenshot", "review my UI", "analyze this screen capture".