whit3rabbit
UserSkills to make AI writing sound more human
Categories
Indexed Skills (8)
rabbit-writes
Write, edit, or audit prose in a specific person's saved voice, or strip machine-writing patterns when there is no voice to apply. Use whenever the user will send or publish text as themselves (emails, Slack and chat messages, reports, incident writeups, reviews, proposals, documentation, personal correspondence), and whenever the user asks to humanize text, remove AI-isms or AI slop, de-slop a draft, check whether writing sounds AI-generated, make a draft sound less like a chatbot, rewrite something in their voice, match their style, make it sound like them, swap or change the active voice, or draft new prose that will not read as machine output. Covers detect-only audits, in-place file edits, full voice conversions, and drafting from scratch.
rabbit-readme-improver
Draft a new README.md, or audit and improve an existing one, using patterns measured from 100 real, currently-trending GitHub repos rather than generic advice, and written in the user's own saved voice rather than a generated open-source register. Use when the user asks to write a README, create a project README, improve or clean up their README, review a README against best practices, add badges or a table of contents, restructure a README's sections, make a README look more professional, or make one sound like they wrote it. Covers new-project READMEs and edits to existing files.
rabbit-reads
Distill a book, paper, or thesis into a folder of terse per-concept cheatsheets with a README index. Use whenever the user asks to "read this PDF and turn it into a doc set", "extract the practices from this book", wants "chapter summaries", "cheatsheets" or "best-practice cards", asks for "craft notes from a novel" or "the claims and methods out of an arxiv paper", or says "make study notes from this thesis". Covers normalizing any source format to text (txt, md, docx, pdf, doc, rtf, html, odt, epub), mapping the source's structure to section line ranges, planning the doc set, fanning the writing out to subagents, and verifying the result, cut by concept and never one file per chapter.
rabbit-rewrites
Rewrite the prose the engine flagged using a small local model over an OpenAI-compatible endpoint, instead of spending a frontier model on it. Use when the user wants to de-slop or humanize a draft offline, on a Raspberry Pi, in CI, or in a pre-commit hook, mentions llama.cpp, llama-server, Ollama, LM Studio, vLLM, or OpenRouter for writing work, asks which local model is good enough to clean up their writing, wants rewriting that costs no API tokens, or asks to benchmark or compare models on a rewriting task. Covers endpoint setup, planning what would be sent, applying gated rewrites in place, and measuring a model's pass rate.
voice-setup
Build, measure, edit, or switch a personal writing voice profile for the rabbit-writes plugin. Three ways in, from writing samples, from an interview, or from both together, which is the one to recommend. Use when the user wants to teach the system how they write, create their own writing style, set up or replace a voice, capture their tone, analyze documents they wrote to extract a voice, be interviewed about their style, change whose voice is active, blend two voices, or convert their writing samples into a reusable style profile. Also use when a draft "doesn't sound like me" and the saved profile needs correcting.
rabbit-claude-md
Audit, tighten, and restructure CLAUDE.md and AGENTS.md memory files so the root file stays a short "where am I" plus rules instead of a changelog. Use when the user asks to audit, improve, clean up, shrink, or split a CLAUDE.md or AGENTS.md, says their memory file is too long, stale, or being ignored, wants gotchas moved to docs or per-module memory files, or mentions CLAUDE.md / AGENTS.md maintenance or project memory. Reports named failure modes with evidence and a per-item disposition plan before touching anything, and holds the prose to the active voice profile.
openai-schema
This skill queries the OpenAI OpenAPI spec (~26k lines, 148 endpoints, 477 schemas) without loading the full file into context. Use when implementing or modifying Anthropic-to-OpenAI translation logic, checking OpenAI API field names/types, verifying schema shapes, or comparing request/response structures. Triggers on questions about OpenAI API structure, field types, endpoint parameters, or schema definitions.
synrepo
Use when a repository has synrepo context available.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.