watermarks-remover

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.

Reverify is an AI-agent verification toolkit for reverse engineering and related code analysis. It places deterministic tools between an agent and its claims: the model proposes hypotheses about a binary or candidate implementation, while parsers, disassemblers, pattern scanners, emulators, equivalence checks, and optional analysis engines compare them with ground truth and return evidence-backed VERIFIED, REFUTED, INCONCLUSIVE, OBSERVED, or INVALIDATED results. Its pure-Python core handles PE, ELF, and Mach-O parsing, x86/x64 and ARM/ARM64 disassembly, byte and pattern inspection, CPU micro-emulation, Protobuf/TLV dissection, and Frida hook generation; optional installations add Capstone, Unicorn, LIEF, Z3, or angr for deeper analysis. The project exposes the verification loop through a command-line interface and an MCP server that agents such as Claude Code and Cursor can call. It also records verified, observed, proved, and refuted results in a content-keyed local ledger, allowing grounded facts and negative findings to survive context resets, compaction, and new sessions. A rollover and orchestration system hands sessions off through files rather than model-written summaries, keeping verified facts separate from unverified notes. Reverify includes claim types for bytes, typed reads, instructions, patterns, emulation, behavioral or formal equivalence, imports, exports, sections, and—when angr is installed—functions, calls, references, and reachability. It is distributed from PyPI and licensed under the MIT License. The repository states that it is intended for authorized reverse engineering, malware analysis, CTF work, interoperability research, and software the user owns or is permitted to analyze.

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.

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.

Agent Skills is a curated skill registry and npm-based CLI for extending AI coding agents such as Claude Code, Cursor, Copilot, and Antigravity with packaged instructions, templates, and reference files. The CLI fetches a catalog, lets users search and select skills, caches downloads locally, and installs them to selected agents by copying or symlinking them at global or project scope; it also supports updating, removing, auditing, and managing cached skills. The repository describes the catalog as an open-source, human-curated library with CI static analysis, content hashing, lockfiles, path isolation, symlink guards, sanitization, audit logging, and Snyk Agent Scan checks. It also includes an MCP server that exposes progressive-disclosure tools for listing, searching, reading, and fetching skill files. The CLI source is MIT-licensed; maintainer-authored skills are generally CC BY 4.0, while third-party skills retain their own licenses.