thehardwarestore.ai

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── 99161 repos indexed ── | filtered by tag: #ai-humanizer
blader/humanizer ★ 55509 4372 APPLICATION

Humanizer is a skill (prompt) for AI agents that rewrites AI-generated text to sound human-written without altering factual content. It is built on Wikipedia's 'Signs of AI writing' guide and provides 26 numbered patterns grouped into six categories (staging, rhythm, inflation, formatting, leftovers, wrong reader). The skill is distributed as a single markdown file (SKILL.md) with YAML metadata, installable via plugin manifests for Claude Code, Cursor, and other agents through the Skills CLI. The repository also includes a validation script, issue templates, and CI workflows to maintain consistency across package files. While not a traditional software application, it functions as a tool that agents execute, with clear entry points (/humanizer command) and a well-defined workflow (mark tells, draft rewrite, check, finalize). The codebase is meticulously maintained with active issue triage, regular releases, and automated validation.

RATING: 9.5/10 MAINT: active DRIFT: low
▸ 3 reusable components found
devswha/patina ★ 359 36 APPLICATION

Patina is a deterministic, pattern-based AI text detection and humanization tool, primarily targeting Korean but supporting English, Chinese, and Japanese. It operates as a Node.js CLI (`patina-cli`), a web API (Vercel serverless), and an agent skill (Claude Code, Codex, Cursor). The core workflow: analyze source text for AI-typical patterns (stylometry, lexicon, discourse tells), build a structured prompt, invoke an LLM backend (OpenAI, Claude, Gemini, Kimi, etc.), verify meaning preservation (MPS/fidelity), and output a humanized rewrite with a deterministic audit trail. It enforces strict meaning guards (number safety, polarity, causation) and provides scoring, diff, and verification modes. The architecture is modular: a shared deterministic analysis core (`src/features/`) runs identically in Node and browser; a prompt builder assembles context from pattern packs, document types, and personas; multiple backend adapters abstract LLM invocation; and a verification pipeline ensures fidelity. The project includes extensive research infrastructure (benchmarking, human evaluation panels, corpus management) and a commercial Pro tier with Polar license validation. It is well-tested with over 200 unit/e2e tests, CI/CD, and security hardening (SSRF guards, input fencing, atomic writes).

RATING: 9.2/10 MAINT: active DRIFT: low
▸ 5 reusable components found
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