# Unslop > Use this skill when you need to run the unslop repo, analyze a domain for repetitive AI defaults, generate a reusable skill file, and verify that the output is specific and materially different from the baseline. - Skill: `mshumer/unslop` (Agent Skill) - Install (CLI): `npx skillmds@latest add mshumer/unslop` - Raw SKILL.md: https://api.skillmd.com/api/skills/mshumer/unslop/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: mshumer (https://skillmd.com/u/mshumer) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/mshumer/unslop --- # unslop Use this repo to generate a domain-specific profile that removes repetitive AI defaults. ## Workflow 1. Clone `https://github.com/mshumer/unslop` if the repo is not already present. 2. Enter the repo root and use a Python virtual environment. 3. Decide whether the job is `text` or `visual`. Text: writing, emails, essays, tutorials, copy, code explanations. Visual: websites, landing pages, HTML pages, UI mockups. 4. Install Playwright only for visual runs: `pip install playwright && playwright install chromium` 5. Run the tool: `python3 unslop.py --domain ""` `python3 unslop.py --domain "" --type visual --count 20 --concurrency 3` ## Output Review Check `unslop-output/analysis.md` and `unslop-output/skill.md`. - `analysis.md` must be concrete, counted, and specific. - `skill.md` should mostly say what to avoid, not prescribe one new stock style. - For visual runs, compare `unslop-output/before-after/before.html` and `unslop-output/before-after/after.html`. - The `after` result should feel meaningfully less generic than `before`. If the analysis is thin or obviously missed repeated patterns, rerun or rewrite the analysis from inside `unslop-output` after reviewing the screenshots and sample files directly. ## Deliverable Return: - The generated `skill.md` - The main repeated patterns the analysis found - Any caveats about sample quality, missing screenshots, or weak comparison output