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
依据文字记录的喜爱与推荐艺人、歌曲和曲风,创建播放列表、推荐音乐并发现风格相近的新艺人。新建议会参考偏好档案,并与已收录的音乐区分。
Learns the user's music taste as a written corpus and uses it to build playlists, recommend tracks, and discover adjacent artists. USE WHEN my music, my music taste, what do I like, build me a playlist, make a playlist, playlist for focus or a drive, recommend music, songs like, what should I listen to, new bands, discover new artists, find bands like the ones I have, expand my taste, add to my music, add this band, my favorite bands, music reference, update my music list. NOT FOR song generation, audio editing (AudioEditor), video audio, audio or studio gear.
实测评分 未实测
查看安装方法以文字记录喜爱和他人推荐的歌曲、艺人及曲风,并据此创建播放列表、推荐音乐、寻找与已有喜好相关的新艺人。提出建议前,技能会读取记录中的口味档案,包括偏好倾向和希望避开的元素。它会核对现有列表,不把已经收录的作品当作新发现推荐。推荐新音乐时,会说明它与已有喜好的关联;需要加入记录时,则会附上来源信息,作为新的条目批次追加。
适合为专注工作或驾车挑选音乐,也适合从喜爱的艺人出发寻找相近风格,拓展听歌范围。你还可以记录别人推荐的作品,并将其与真正喜爱的音乐分开管理。此技能不生成歌曲、不编辑音频,也不处理视频音轨或音频设备选购。
需要用于记录音乐口味的配置文件和曲库。若已连接 Spotify,搜索需要应用密钥,创建播放列表还需要用户登录。没有此连接时,技能会通过网络资料核实推荐内容,并以可粘贴到各音乐服务的列表形式提供播放列表。
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。
Holds the user's music taste as a durable corpus (favorite tracks, artists, and genres, each tagged favorite or recommended) and uses it to build playlists, recommend tracks that fit, discover new artists next to the ones they already love, and grow the list as they feed in more. It reasons from the corpus, not from memory.
| Intent | Trigger | File |
|---|---|---|
| BuildPlaylist | "build me a playlist", "make a playlist", "playlist for <mood>" | Intents/BuildPlaylist.md |
| Recommend | "recommend music", "what should I listen to", "songs like X" | Intents/Recommend.md |
| DiscoverArtists | "new bands", "discover new artists", "bands like the ones I have", "expand my taste" | Intents/DiscoverArtists.md |
| AddMusic | "add to my music", "add this band", "log this album", "update my music list" | Intents/AddMusic.md |
| File | Read when |
|---|---|
Setup.md | first-time setup: the config file, the corpus format and playlist defaults, and a read-only proof |
Read <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Music/Config.md first (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). It names the corpus and holds the playlist defaults; when it is absent, the corpus is <configRoot>/LIFEOS/USER/MUSIC/MusicReference.md and the defaults in Intents/BuildPlaylist.md stand. First-time setup: Setup.md.
The corpus (Config.md Corpus: line):
## Taste Profile: the poles of the user's taste with anchor artists for each, and any stated bias (vocals, production, eras, things to avoid). That section is ground truth, never memory.### Batch N section under ## Tracks or ## Artists / Bands, with Artist as the second column, and a ## Counts block closes the file.LIFEOS/HERMES/Heartbeat/ArtistScan.ts) runs, every ⭐ row's Artist cell becomes a live tour-date search. Star only what the user loves.No account or key is needed for the skill itself. Existence checks and playlist creation use the Spotify skill when it is installed (search needs its app keys, playlists also need its user sign-in); without it, suggestions are checked against a fetched web source and playlists are delivered as a pasteable list.
Example 1: Build a playlist
User: "build me a focus playlist for deep work"
→ BuildPlaylist
→ A tight numbered list (Track — Artist — Album) of ⭐ corpus tracks ordered for flow, to paste into any service, or a Spotify playlist via the Spotify skill
Example 2: Discover new artists
User: "what new bands would I like?"
→ DiscoverArtists
→ Adjacent artists not in the corpus, grouped as "Because you have <Anchor> → <New artist>, <New artist>" with safe-fit/stretch tags
Example 3: Grow the corpus
User: "add these to my music: <three artists and an album>"
→ AddMusic
→ A new ### Batch section with schema rows and source tags, and updated counts
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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。