bushiershi: 对抗模型输出不良表达习惯的 Agent Skill

by jinhanbuilds · Codex Skill · ★ 62

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

How to install bushiershi

npx skills add jinhanbuilds/bushiershi --skill bushiershi -y

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

Security audit verdict: SAFE · quality 53/100

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.

agent-skillsai-agentsai-writingchinese-writingcodexcontent-editingllmwriting-assistant

Quick Facts

Stars62
Forks0
CategoryCodex Skill
LicenseMIT
Quality Score53/100
Last Updated2026-08-26
Created2026-08-25
Platformscodex
Est. Tokens~2k

About bushiershi

不是而是 是一个对抗模型输出不良表达习惯的 Agent Skill。 它仅在明确调用 时生效。 它从哪里来 我用了三年多 GPT。过去几个月,我把真实任务里反复纠正的表达问题整理成了这个 Skill。 阅读「不是而是」的由来 人话的底层思路 人话感来自内容本身:具体对象、真实动作、清楚的关系和有依据的判断,比模仿口语、短句或某种“像人”的语气更重要。 AI 味常常是一种写作动作:问题不只出在某几个词,还出在模型怎样组织结论、制造完整感、补意义和结束一段话。 改得顺不能以失真为代价:更短、更直接的文字,如果改变了事实、程度、归因或不确定性,只是更自信的误写。 只控制最终成稿:这个 Skill 处理读者实际看到的文字,不承担 Agent 全部思维、协作和任务推进方式。 快速使用 一键安装 在希望使用该 Skill 的 Git 项目目录中执行: 该命令会把 Skill 安装到当前项目的 。...

Compatible Skills

These tools work well together with bushiershi for enhanced workflows:

  • writing-agent — semantic(0.55)+complementary+shared_fw(openai)+rare_topics+similar_pop (67%)
  • clarity — semantic(0.68)+complementary+rare_topics+similar_pop (63%)
  • chinese-longnovel-skill — semantic(0.54)+complementary+rare_topics+similar_pop+shared_platform (63%)
  • ZeroSlop — semantic(0.48)+complementary+rare_topics+similar_pop+shared_platform (61%)

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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