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 alexgreensh · Claude Skill · ★ 979
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is attention-span safe to install? View the security audit →
English · Español A small collection of output styles for Claude Code that change how it talks to you, not how it codes. Answer-first, plain English, easy to skim. Each one is a single markdown file you drop in and switch on. The concise-by-default rules aren't just kind to your attenti
| Stars | 979 |
| Forks | 29 |
| Language | Python |
| Category | Claude Skill |
| License | AGPL-3.0 |
| Quality Score | 64.9872464449385/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-06 |
| Created | 2026-08-04 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~18k |
These tools work well together with attention-span for enhanced workflows:
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attention-span is Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.. It is categorized as a Claude Skill with 979 GitHub stars.
attention-span is primarily written in Python. It covers topics such as adhd-friendly, ai-slop-reduction, anti-slop.
You can find installation instructions and usage details in the attention-span GitHub repository at github.com/alexgreensh/attention-span. The project has 979 stars and 29 forks, indicating an active community.
attention-span is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to attention-span on Agent Skills Hub include shuorenhua, fluent-korean, anti-ai-slop-writing. 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: