Software Development Skills

Generates publication-grade README.md files by statically analyzing the actual target codebase — never guesswork or generic templates. Walks the import/AST graph for a salience-ranked Mermaid architecture diagram, extracts LaTeX-annotated formulas from docstrings/comments into a Mathematical Foundations section, auto-detects language/deps/license for the badge row, and enforces one canonical section order with a strict zero-placeholder guarantee (unsupported sections are omitted, never stubbed). Also runs --audit mode across a multi-repo workspace to report README drift (missing badges, out-of-order ToC, stale license blocks). Use whenever the user asks to generate, draft, write, or rewrite a README for a repo or codebase; wants docs to "look professional" or "match a template"; asks to audit or standardize READMEs across repos; or mentions a Mermaid diagram, badge row, or Mathematical Foundations section.

Transforms any rough, vague, or underperforming prompt into a clear, structured, high-performing AI instruction. Use whenever the user wants a better prompt: "improve this prompt", "my prompt isn't working", "refine this", "make this better", "optimise this instruction", "clean up my system prompt", "this keeps giving me bad outputs", "write me a prompt for", "I need a prompt that". Also trigger when the user describes what they want an AI to do — even without mentioning a "prompt" — if the intent is to instruct an AI model. Works for system prompts, one-shot prompts, chain-of-thought prompts, and multi-turn conversation setups.

Rewrites AI-generated code, in any popular language (Python, JS/TS, Java, C/C++, C, Go, Rust, MATLAB, R, SQL, Bash, etc.), into a verbose, linear, engineering-student style — top-down procedural scripts, no functions/abstraction unless asked, unrolled loops, an inline comment on nearly every line, dashed section dividers with bracketed task labels, task-prefixed variable names, and boxed ASCII console output. Use whenever the user wants code to look "hand-written," "less AI-generated," "like a student wrote it," "homework style," or "verbose," or references their own/a professor's required commenting convention with dashes, bracketed labels, or heavy inline comments — even if they never say "skill." Also trigger when the user hands Claude code and asks to add exhaustive step-by-step comments, remove abstraction, unroll loops, or make logic maximally explicit.