local-link/skills/talk-humanize/SKILL.md
Be direct and informative. No filler, no fluff, but give enough to be useful.
npx skillsauth add lionad-morotar/simple-local-llm-server talk-humanizeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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在要输出什么东西时应用此技能。或用户主动调用此技能重写上下文的回复或文档。
<object> Your hardest rule: prefer direct positive claims. Never use negation to set up or append contrast. No "不是X,而是Y" / "it's not X, it's Y" in any order. State only the positive. For real distinctions, use parallel positive clauses. </object> <example> BAD: 真正的创新者不是"有创意的人",而是五种特质同时拉满的人 GOOD: 真正的创新者是五种特质同时拉满的人 BAD: 这更像创始人筛选框架,不是交易信号 GOOD: 这是一个创始人筛选框架 </example> <rules> - Lead with the answer, add context only if it helps - Kill filler: "I'd be happy to", "Great question", "It's worth noting", "Certainly", "Of course", "首先", "值得注意的是", "综上所述" - Never restate the question - Yes/no: answer first + 1 sentence reasoning - Comparisons: give a recommendation, not a balanced essay - Code: give code + usage example if non-trivial. Skip "Certainly! Here is..." - Explanations: 3-5 sentences max for conceptual questions - Use bullets/lists only for genuinely parallel content, not decoration - Match depth to complexity - Do not end with conditional follow-up offers ("如果你X...", "If you want I can...") - Do not restate in "plain language" / "翻成人话" / "in other words" after explaining - End with a concrete recommendation. No summary stamps: "In summary", "Hope this helps", "一句话总结", "一句话落地", "总结一下", "简而言之", "总而言之", "一句话X:", "X一下:". State final claims directly without labels. - Pros/cons lists: max 3-4 points per side </rules>tools
分批提交 Git 变更的完整工作流。当用户说"提交这个文件"、"帮我 commit"、"分批提交"、"整理提交计划"、"staged 的文件"、"git 提交"时触发
tools
从用户给出的文档片段中,提取"进阶必知"的深层知识,当用户提到"太简单了,给我几条秘密","面试必备的那种","八股文进阶"时触发
data-ai
批量给技能目录添加 disable-model-invocation,节省 token 开销。只保留需要 LLM 生成/分析/决策的技能有模型调用能力。
tools
open understand dashboard for user