dot_claude/skills/second-pass/SKILL.md
Use after completing a bug fix, feature, refactor, or tk task when the first implementation taught enough context to replace it with a simpler, cleaner, or more coherent version before finalizing.
npx skillsauth add nijaru/dotfiles second-passInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Second pass is a post-completion rewrite of the just-finished change set. It uses what the first pass revealed, but it does not broaden scope. If the goal is routine behavior-preserving cleanup, use refactor. If the goal is to evaluate a ground-up replacement before implementation, use rewrite.
Do not polish the first pass. Re-derive the minimal final implementation from the now-known problem, then replace any discovery-shaped code that no longer earns its complexity.
Report what changed between first pass and second pass, why it is simpler, and the verification result. If the first pass is already the cleanest implementation, say so and leave it unchanged.
| Excuse | Reality | | :--- | :--- | | "The first pass works." | Correctness is the floor; second pass removes discovery artifacts before they become design. | | "I'll just rename a few things." | Cosmetic edits are refactoring, not second pass. Reconsider the implementation shape. | | "This is a chance to improve nearby code." | Do not expand scope. Use the learned context only for the completed change set. |
development
Use when writing, migrating, or reviewing Zig code across recent stable versions (0.14-0.16), especially to correct stale syntax or stdlib, build.zig, allocator, formatting, or runtime API knowledge.
documentation
Use when reviewing or revising text (prose, docs, commits) to remove AI patterns and improve voice/clarity.
content-media
Use when fetching X/Twitter post content by URL, or searching for recent X posts.
development
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.