Humanizer CLI

Markdown-based agent skill that rewrites AI-sounding text to read human-written without changing what it says — works with any agent that supports skills. Rewrites against the 35 patterns from Wikipedia's 'Signs of AI writing' (WikiProject AI Cleanup): a first pass free to restructure, then a check of the draft against those patterns and the original claims before rewriting what still sounds artificial. Its rules bar invented facts — names, numbers, dates and quotes must come from the source or the writer — and preserve a writer's personal style (or follow a provided sample). The skill shows its work: the first rewrite and a critique of what still sounds artificial precede the final version.

soundshuman is an open-source writing-analysis and repository-auditing toolkit for identifying patterns associated with AI-generated prose. It combines a humanizer skill, a machine-readable rule pack, and a zero-dependency CLI: the skill uses a 41-pattern catalog, statistical signals, voice calibration from writing samples, and a draft–audit–final workflow; the rule pack stores vocabulary tiers, phrase fixes, and weighted regular-expression detectors in JSON; and the CLI scores text from 0 to 100, explains findings with line numbers and triggered rules, applies selected mechanical fixes, scans repositories, and can gate CI. The project states that its goal is inspectable editing rather than defeating AI detectors, with rewrites delivered as reviewable Git diffs and a no-invented-facts rule. The CLI runs on Node 18 or later without dependencies and supports pasted text, files, repository audits, and directory scans. The skill is distributed as plain Markdown for agent harnesses and as a Claude Code plugin; the repository also includes reference materials, a pre-commit hook, CI integration, and installation instructions. It is MIT licensed, with upstream copyrights preserved in the license.

humanizer-stack is an open-source writing-humanization toolkit packaged as Claude Code Skills. Its two-pass pipeline first scans and revises surface-level tells such as hype vocabulary, em-dash overuse, formulaic phrasing, antithesis, vague attribution, inflated symbolism, and redundant exposition; a second structural pass audits discourse-level patterns grounded in the StoryScope study. The structural audits cover theme explicitness, structural tidiness, emotional framing, reference specificity, reader engagement, and shape convergence, with deterministic Python scanners for mechanical copy and structural tells. The repository includes the skills, reference materials, genre-specific guidance, and documentation describing how the passes are chained.

Human Review is a local visual review tool and agent skill for editing HTML and Markdown files, reviewing localhost pages, and sending feedback to an AI coding agent. It opens a file or URL in a browser, where users can edit text and basic formatting, rearrange or remove page elements, resize or paste images, and attach comments to exact phrases, images, charts, or sections. Direct HTML edits and image changes save automatically; Markdown and localhost reviews are sent to the agent, which applies the changes to the source and refreshes the page. The tool includes CLI commands for review sessions, polling, status, and setup, plus a local server, editing and feedback SDK, browser client, Markdown renderer, and instructions for agent harnesses such as Claude Code and Codex. It runs on the user's computer without an account, cloud service, database, or API key.

SlopMonster is a Python linter for detecting formulaic or suspicious AI-generated writing in HTML pages, Markdown, plain text, landing pages, READMEs, emails, and scripts. Its scorer checks AI-associated vocabulary, sentence constructions, punctuation cadence, rule-of-three rhythms, and unsupported sales claims, assigns a score out of 5, and can fail a build when copy scores below 5/5. Its workflow scores the draft, performs a three-pass rewrite that removes suspect vocabulary and sentence shapes and replaces them with specific copy, sends the draft to a different model family for cleansing, and scores the result again. The cleansing script can use a Codex or Claude CLI, refuses to route a draft back to its own model family, and prints a prompt when no supported rival CLI is available. A GitHub Actions workflow is included as a reusable build gate; the scorer itself uses only Python's standard library, while the cleansing step requires an external AI CLI or manual prompt handling. The repository also includes regression tests, a catalogue of detection rules, rewrite principles, and worked before-and-after examples. It is distributed under the MIT license and can also be installed as an agent skill for Claude Code or used by other agents through its plain-Markdown instructions.