按类别或厂商查找并获取网络安全报告,比较威胁、勒索软件、数据泄露、云安全和AI趋势。分析会注明每项数据的厂商、年份及报告,并分别呈现来源间的分歧。
- 按类别查找威胁报告
- 获取指定厂商的报告
- 比较多家厂商的勒索软件趋势
从真实目标出发重新审视大型系统、架构或战略,梳理适当的讨论范围,独立构思方案,并将与现有计划的差异转化为可执行的改进,同时保留实际约束。
Thinking skill for big problems that swaps the solver: reads the scope it was given, infers the scope the asker most likely means, maps that, then asks how a vastly smarter, fully informed intelligence with none of our habits or invented limits would do it, and turns every gap between its approach and ours into a change we can make. Built for large scope (how a whole system is implemented, an architecture, a strategy); when the intended scope is unclear or too small, a short interview settles it first. USE WHEN superior, how would a smarter intelligence do this, what would a genius or superintelligence do, if you knew everything, are we thinking too small, best possible approach, how should this whole system be built, analyze how this big thing is implemented. NOT FOR constraint teardown (FirstPrinciples), many options (BeCreative), attacking a plan (RedTeam).
实测评分 未实测
查看安装方法面对大型问题时,此技能会根据提问者给出的范围,推断其真正想解决的问题,并据此确定合适的讨论范围。随后,它会像绘制地图一样梳理该范围,结合相关领域知识和当前方法,独立构思方案,不受现有计划牵引。它会找出新方案与当前方案之间的差异,并将每项差异转化为可执行的修改,或明确说明哪些是真正无法克服的限制。范围不清楚时,会先用简短问题确认;提问者已指定范围时,则会保留该范围。
适用于整个系统的构成、架构、包含多个部分的开发工作、战略等需要累积多项判断的问题。它更适合重新审视较大的目标或决策;对于小型函数或单项工作,重新评估通常难以带来明显差异。也可以借此检查计划中是否存在基于假设而设定的限制。
讨论范围过小或含糊时,会先确认范围。提问者设定的限制会继续保留。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。
不计入评分。 由 AI(OpenAI Codex)于 2026年10月28日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Every approach we take is shaped by the mind taking it: what it happens to know, what it has seen done before, what it assumes is expensive or impossible, how much effort it expects to spend. Superior holds the task fixed and replaces that mind. It asks how an intelligence far beyond ours would do this exact task — one that knows everything relevant including the current frontier, carries no habits or inherited conventions, invents no constraints, reasons cleanly from first principles, and cares only about the real goal behind the request. Then it measures the distance between that approach and the one we were about to take, and closes as much of it as reality allows.
Superior is built for big problems: how a whole system is implemented, an architecture, a strategy, a multi-part build. On a one-function question the superior mind and ours give the same answer, so the pass earns nothing. The value appears at scale, where habits and invented limits compound across many decisions.
FirstPrinciples changes what we believe about the problem. Superior changes who is solving it. They pair well: Superior often surfaces a constraint we invented, and FirstPrinciples is the tool for proving it was invented.
| Intent | Trigger | File |
|---|---|---|
| Elevate | any Superior request; invoked as a thinking skill on a hard task | Intents/Elevate.md |
The lens is defined by what that mind has and lacks, never by adjectives like "more careful" or "more thorough":
The pass begins from the scope it was given (the named file, feature, system or question) and works out what the asker most likely means by it. The literal ask is evidence, not the boundary: "how is the login implemented" usually means the auth flow end to end, not the one handler that was opened, and almost never means the whole product.
Abstract up to the proper scope, and no further. The proper scope is the smallest unit that contains the real goal: the level where the decision the asker cares about actually lives, reasoned from what was named, the surrounding conversation, the project, and what the answer will be used for. "The whole system" is the scope only when the ask points there. The result is stated before mapping, in one line: Scope: <inferred scope>, from <what was asked>, because <reason>.
When the inference is not confident, Elevate's scope interview settles it. Its questions find the right level: what this belongs to, what decision the answer feeds, and how far to reach (this piece, the feature, the subsystem). A scope the caller already fixed is the scope.
User customizations, when present, live at <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Superior/ (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). Any PREFERENCES.md, configurations, or resources there override the defaults; when the directory does not exist, proceed with skill defaults.
Example 1: Hard design work
User: "Design the sync layer for the notes app. Run Superior on it."
→ Elevate, full mode: scope map of the app, server API and clients first; a fresh agent gets the map, goal and constraints, never our draft design
→ It returns a CRDT-based local-first design; we had planned server-authoritative REST with conflict prompts
→ Gap table: knowledge (CRDT libraries are mature), convention (server-as-truth), invented constraint ("offline is an edge case")
→ Changed plan adopts local-first; the one real limit (existing server API) is kept as a migration path
Example 2: Ask too small to run
User: "Superior on this retry helper."
→ The helper alone is too small, and it has callers in two places, so two scopes are plausible
→ Elevate asks: "I'd read this as the job runner's retry path; is that right, or is it the HTTP client?"
→ Principal: "Job runner; jobs keep double-firing."
→ Scope: the job runner's retry and dispatch path, not the whole runner; the map shows retries in three layers; the superior approach is idempotent jobs, which makes the helper unnecessary
Example 3: Inline lens during a max-effort run
A depth turn ("think deeply") on a strategy question; Superior runs beside FirstPrinciples
→ Elevate, light mode: one inline pass, stated before the current plan is reread
→ Superior approach dissolves half the task by reframing who the audience is
→ Two gaps adopted, one blocked by a principal-set budget, recorded in the ISA Decisions
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按类别或厂商查找并获取网络安全报告,比较威胁、勒索软件、数据泄露、云安全和AI趋势。分析会注明每项数据的厂商、年份及报告,并分别呈现来源间的分歧。
通过三轮分析,对比单个功能或组件的设计需求与整个系统的目标,梳理两者的冲突、合适的实现范围,以及方案是否符合整体方向。
按文章主题寻找格言或引文,按相关程度排序,并附上作者和推荐理由。可添加带出处的引文、按主题研究思想家的言论、整理使用记录,帮助避免重复引用。
按用途选择制作流程,生成博客插图、技术图解、比较图、漫画和图标等视觉内容,并输出供预览的图像。
依据简报和制作标准评估图片、影片、幻灯片及网站页面截图,由视觉评审给出评分和缺陷优先级,支持有限轮次的修改;通过评审后才能交付,并可记录人工评分供后续校准。
按主题、研究领域或论文编号查找 arXiv 论文,并整理标题、作者、摘要和链接;查看新投稿,也可在有内容时获取 AlphaXiv 的人工智能生成概述。