attention-span: Make your agents talk human

by alexgreensh · Claude Skill · ★ 1.5k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 63/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

adhd-friendlyai-slop-reductionanti-slopclaude-codeclaude-pluginclaude-skillseli5explainabilityexplainable-aioutput-style

Quick Facts

Stars1,516
Forks55
LanguagePython
CategoryClaude Skill
LicenseAGPL-3.0
Quality Score63/100
Open Issues2
Last Updated2026-10-30
Created2026-08-04
Platformsclaude-code, codex, python
Est. Tokens~18k

About attention-span

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

Compatible Skills

These tools work well together with attention-span for enhanced workflows:

  • attention-control — semantic(0.41)+complementary+rare_topics+similar_pop+shared_platform (59%)
  • otto-cognitive-nudge — semantic(0.37)+complementary+rare_topics+similar_pop+shared_platform (57%)
  • hush — semantic(0.31)+complementary+rare_topics+similar_pop+shared_platform (55%)
  • claude-modular — semantic(0.20)+complementary+rare_topics+similar_pop+shared_platform (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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