slopkit: Anti-slop skills for AI agents: slopbeth cleans the shipped writing, slopgent cleans the conversation

by ehmo · Codex Skill · ★ 108

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

How to install slopkit

npx github:ehmo/slopkit install
npx github:ehmo/slopkit install --all

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 69/100

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.

ai-slopai-writinganti-slopbenchmarkclaude-codecodexeditinghermesopenclawopencode

Quick Facts

Stars108
Forks7
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score69/100
Last Updated2026-07-22
Created2026-06-18
Platformsclaude-code, codex, python
Est. Tokens~17k

About slopkit

Slopkit Two anti-slop skills for coding agents, installed by the same means. slopbeth cleans the writing you ship. It rewrites and reviews an artifact, cutting AI tells without changing what the draft can honestly claim. slopgent cleans the conversation. It shapes the agent's own replies so they stay honest about what actually ran, action-first, and plain. They are separate skills.

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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:

View on GitHub → Browse Codex Skill tools