No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by hardikpandya · AI Tool · ★ 17.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is stop-slop safe to install? View the security audit →
Stop Slop A skill for removing AI tells from prose. What this is AI writing has patterns. Predictable phrases, structures, rhythms. This skill teaches Claude (or any LLM) to catch and remove them. Skill Structure Quick start Claude Code: Add this folder as a skill. Claude Projects: Upload and reference files to project knowledge. Custom instructions: Copy core rules from . API calls: Include in your system prompt. Reference files load on demand. What it catches Banned phrases - Throat-clearing openers, emphasis crutches, business jargon, all adverbs, vague declaratives, meta-commentary. See . Structural clichés - Binary contrasts, negative listings, dramatic fragmentation, rhetorical setups, false agency, narrator-from-a-distance voice, passive voice. See . Sentence-level rules - No Wh- sentence starters, no em dashes, no staccato fragmentation, no lazy extremes, active voice required. Scoring Rate 1-10 on each dimension: | Dimensio
| Stars | 17,802 |
| Forks | 1,298 |
| Category | AI Tool |
| License | MIT |
| Quality Score | 55.4261017966657/100 |
| Open Issues | 57 |
| Last Updated | 2026-03-17 |
| Created | 2026-01-11 |
| Est. Tokens | ~2k |
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stop-slop is A skill file for removing AI tells from prose. It is categorized as a AI Tool with 17.8k GitHub stars.
You can find installation instructions and usage details in the stop-slop GitHub repository at github.com/hardikpandya/stop-slop. The project has 17.8k stars and 1298 forks, indicating an active community.
stop-slop is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to stop-slop on Agent Skills Hub include claude-code-infrastructure-showcase, vision-agent, axton-obsidian-visual-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: