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
先梳理文件、工具、子系统或代码库的用途和依赖,再依据证据归类冗余或未使用内容并提出精简方案。获批的改动会通过前后基准检查确认依赖行为仍可用。
Research-first simplification of any file, skill, tool, subsystem or codebase against its ideal state: reads its ISA or derives its purpose, maps every dependent (callers, imports, references, hooks, settings, docs, tests), classifies each element with evidence as load-bearing, duplicate, dead, cruft or over-abstracted, then proposes and applies cuts that keep every behaviour a dependent relies on, proven by before-and-after parity checks. USE WHEN simplify, simplify this, simplify this system, remove cruft, remove duplication, dedupe, dead code, clean this up, this has gotten bloated, too complicated, reduce complexity, consolidate, strip this down without breaking it, what can we delete. NOT FOR always-on context files (Trim), cleanup of just the current diff (/simplify), harmony across many systems (Unify), auditing prompt rules for over-prompting (BitterLessonEngineering), bug hunting (/code-review).
实测评分 未实测
查看安装方法Simplify 可帮助精简文件、技能、工具、子系统或整个代码库。它会先确认目标的用途,再梳理调用方、导入项、引用、设置、文档和测试等依赖关系。随后,它依据调查证据将各项内容归类为必需、重复、未使用、累积的冗余或过度抽象,并据此提出排序后的精简方案。只有你批准的改动才会应用。技能会在修改前后运行基准检查,以确认其他部分所依赖的行为仍然有效。工作可以在调查、提出方案或应用获批改动中的任一阶段结束。
当代码库或工具变得复杂、你想判断哪些内容可以删除,或计划改动前需要先了解某个子系统时,可以使用此技能。它依据引用关系和行为检查判断内容是否多余,而不是只看表面形式。
应用改动前,需要你批准具体方案。调查、方案和应用记录会保存在目标附近的 LIFEOS/MEMORY/WORK/{slug}/simplify/ 目录中。
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。
Simplify makes a target smaller and clearer without breaking anything that depends on it. A target can be a file, a skill, a tool, a subsystem or a whole repo. The work comes in three intents, each building on the record from the one before: Survey maps the target read-only, Propose ranks the cuts and holds them for a yes, and Apply lands the approved cuts with proven parity. The central rule: a cut never drops a behaviour that a dependent or ISA criterion relies on, and anything the evidence cannot explain stays. Records sit next to the target's ISA when there is one.
LIFEOS/MEMORY/WORK/{slug}/simplify/, so the next stage builds on evidence rather than on memory.| Intent | Trigger | File |
|---|---|---|
| Survey | "simplify X", "what can we delete", "map this before we cut", "is this dead" | Intents/Survey.md |
| Propose | "what would you cut", "propose the simplifications", follows Survey in the same run | Intents/Propose.md |
| Apply | "apply them", "y" or approved row numbers after a proposal | Intents/Apply.md |
A bare "simplify X" runs Survey and then Propose, and stops at the proposal. A Survey-only request, which is how a read-only analysis calls this skill, stops at the survey.
None. This skill reads no config and needs no account or key. Each stage writes its record under LIFEOS/MEMORY/WORK/{slug}/simplify/ next to the target's ISA, if it has one; nothing else is created.
settings.json, launchd plists, workflow YAML, skill routing tables, spawned commands and dynamic imports. ReferenceCheck.ts --shipped-paths exists because plain path scans missed spawn and dynamic-import specifiers.Example 1: Simplify a tool
User: "simplify LIFEOS/TOOLS/ReferenceCheck.ts"
→ Survey intent, then Propose
→ Purpose from its header and DocCheck lineage, every caller, findings per function, test baseline
→ A ranked table of cuts, each with its parity check, held for y
Example 2: Land the approved cuts
User: "y, but skip 4"
→ Apply intent
→ Rows 1–3 and 5 land as separate commits, tests show the same pass count before and after, and the measured reduction sits beside the estimate
Example 3: An always-on context file
User: "simplify CLAUDE.md"
→ Routed to Trim, which owns always-on context files and their directive-coverage gate
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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。