humanize-readme

Rewrites a README.md to remove AI slop — buzzwords, generic openers, fake enthusiasm, and formulaic structure — replacing it with direct, honest, human-sounding writing. This skill should be used when the user wants to humanize a README, remove AI-generated writing patterns, make documentation sound less like ChatGPT wrote it, or asks to "humanize readme", "make it sound human", "remove AI slop from the docs". NOT for factual drift (whether the README's claims are still true — that is a docs-accuracy audit) and not for source code slop (anti-slop).

b4r7x/agent-skills41 installsMITSynced Aug 26

Works with

Claude CodeCursorCodex CLIGitHub CopilotGemini CLI
---
name: humanize-readme
description: Rewrites a README.md to remove AI slop — buzzwords, generic openers, fake enthusiasm, and formulaic structure — replacing it with direct, honest, human-sounding writing. This skill should be used when the user wants to humanize a README, remove AI-generated writing patterns, make documentation sound less like ChatGPT wrote it, or asks to "humanize readme", "make it sound human", "remove AI slop from the docs". NOT for factual drift (whether the README's claims are still true — that is a docs-accuracy audit) and not for source code slop (anti-slop).
license: MIT
---

# Humanize README

Reads the current `README.md`, audits it for AI slop patterns, then rewrites it in a direct, honest, human voice.

## Workflow

### Step 1 — Read the README

Use the path passed as the argument; default to `README.md` when none was given:

```bash
cat <readme-path>   # e.g. cat docs/README.md — falls back to cat README.md
```

Also check what the project actually is (to rewrite with specifics, not generics):

```bash
cat package.json 2>/dev/null | head -20
cat pyproject.toml 2>/dev/null | head -15
ls src/ 2>/dev/null | head -10
```

### Step 2 — Audit for slop

Read `references/slop-patterns.md` for the full list. Flag these in the README:

**High-signal slop patterns:**
- Banned buzzwords: `seamlessly`, `robust`, `scalable`, `leverage`, `cutting-edge`, `comprehensive`, `empower`, `intuitive`, `powerful`, `game-changer`
- 2026-era hedging/filler: `"it's worth noting"`, `"let's explore"`, `"this is designed to"`, `"makes it easy to"`, `"enables developers to"`, `"ensures"`, `"has been designed"`
- Generic openers starting with "In today's...", "This powerful tool...", "This repository aims to..."
- Feature lists with empty adjectives: "Blazing fast", "Enterprise-grade", "Intuitive API"
- Suspiciously polished completeness with no honest gaps
- Conclusions that philosophize about the project
- **Uniform sentence length** — every sentence the same rhythm (burstiness check)
- **No personal voice** — no "why it exists", no honest limitations, no tradeoffs mentioned

### Step 3 — Rewrite

Rewrite the README applying these rules:

**Voice:**
- Write like you'd explain the project over coffee — direct, specific, a bit casual
- Use the actual tech names (not "modern technologies" or "industry-standard tools")
- Keep code blocks, commands, and links exactly as they are
- Preserve the structure (sections) but rewrite the prose

**For each section:**
- **Project description / intro** — one or two sentences: what it does, why it exists. No superlatives.
- **Features** — remove adjectives, add specifics. Not "Fast" → state the actual number if known, otherwise just name the feature plainly
- **Installation / Usage** — keep as-is if already good; strip any "Welcome to the getting started..." filler
- **Why this project** — if it exists and sounds generic, rewrite with a real reason or remove it
- **Contributing / Closing** — strip "star this repo", philosophy, or over-long contributing guides

**Tone rules:**
- Honest about gaps: "Not tested on Windows", "Still experimental", "Works on my machine"
- Varied sentence lengths — short and long, not all the same
- No emoji unless they were already there (and even then, fewer)
- No exclamation marks unless genuinely warranted

**Do NOT:**
- Add content that wasn't there — only rewrite existing content
- Change technical accuracy — keep the same claims, just strip the fluff
- Make it curt to the point of being unhelpful — clarity > brevity
- Touch badge rows (`[![...](...)](#)` lines) — CI badges, version badges, license badges stay exactly as-is

### Step 4 — Output

Output the full rewritten README in a single fenced markdown code block so it can be copied directly.

Before the block, briefly note what you changed (2-3 bullet points max).

**For full banned phrase list and before/after examples:** `references/slop-patterns.md`

More Writing & Documentation skills

paper-context-resolver

lllllllama/rigorpilot-skills

Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.

450.8k

repo-intake-and-plan

lllllllama/rigorpilot-skills

Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.

450.0k

minimal-run-and-audit

lllllllama/rigorpilot-skills

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

449.9k

← All Writing & Documentation skills

Check your AI visibility

One URL in, a 0–100 score and the exact fixes out.

RUN THE CHECK

Browse all the tools

15 tools across six categories
13 of them never send your data anywhere

Free · No signup · No trial clock

SEE THE DIRECTORY