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
根据内容和立场制作配有旁白的视频文章,也可将帖子、想法或功能做成短解说视频,或用作者录音、声音克隆或 DA 朗读博客并制作成视频,供检查后接收。
Turns content plus a stance into a video essay narrated by the DA, any post, idea or feature plus a length into a short DA-narrated explainer video, or the author's own audio reading of their blog post (a recording or their voice clone) into a finished video of that post, with scripted visuals rendered on HyperFrames, presence layer, rendered and read back. USE WHEN video essay, explainer video, short video about this post, N-minute video of my blog post, make a video about this, respond to this video, argue against this in a video, debate this, video response, DA video, narrated video, turn this essay into a video, react to this interview, make a video from my audio, I recorded myself reading the post, turn this post into a video, video from my voice clone, audio to video. NOT FOR the user's camera footage or clips (Video), audio-only podcast cuts (Video), one-off animation (HyperFrames).
实测评分 未实测
查看安装方法VideoEssay 可根据素材制作带旁白的视频。制作视频文章时,它会先围绕某个立场整理书面论点,经过确认后再规划画面结构和脚本,并渲染视频。它也能按指定时长,为帖子、想法或功能制作简短解说视频,不必表达特定立场。另一种方式是把博客文章做成视频,旁白可使用作者的录音、作者的声音克隆,或 DA 的朗读。视频可以呈现博客页面,并配上与朗读段落相对应的画面。完成后,你可以先检查内容和画面,再接收成片。
想以视频表达对访谈或其他视频的看法、用短片介绍文章或功能,或制作贴合博客内容的视频时,可以使用它。它也适合把作者自己朗读博客的录音制作成视频。
首次使用需要进行设置。语音合成使用 Fish Audio。若要使用作者的声音克隆,还需要相应的语音 ID 和 API 密钥。此技能不会发布视频;发布需通过单独流程完成。
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。
Contents: Ideal State · Intent Routing · Tools · Data & Config · Quality loop · Gotchas · Examples
The DA's own video format, in two modes, each a fully automated production that proves itself before the user sees it. Essay mode argues first, then renders. Voice mode (run.json mode: "voice") turns the author's post, read by their recording, their voice clone or the DA reading their exact words (run.json narrator recorded | clone | da), into a video of the real post page scrolling with the spoken sentence highlighted, inserts directed from what the post argues (LIFEOS/TOOLS/VideoMeaning: MeaningMap, Direction, a two-vendor plan review), and the reader's portrait in the corner badge (the author's, or the DA's on a DA-read run).
Essay.md with verbatim, correctly attributed quotes reaches the user at the checkpoint (unless the ask skips it), and only then becomes Script.json and a render.Out/verify.json all green, and the frames and the Out/review/ studio pass read.~/Downloads/<slug>/ only through Deliver.ts, after a passing ArtDirection critique and Proof receipt on those exact bytes.Video.| Intent | Trigger | File |
|---|---|---|
| Argue | "write the argument", "essay first", "what's our take on this", the first step of every new run | Intents/Argue.md |
| Script | "script it", "turn the essay into beats", "plan the visuals" | Intents/Script.md |
| Produce | "produce it", "render the essay", "make the video from the script", re-renders after an edit | Intents/Produce.md |
| Full | "respond to this video", "video essay on this", "argue X in a video", raw content plus a stance to argue | Intents/Full.md |
| Voice | "make a video from my audio", "I recorded the post", "use my clone to read the post", a post plus a recording | Intents/Voice.md |
| Explainer | "make an explainer video", "a short video about this post / idea / feature", "a 2-minute video of my latest post", any source plus a length with no stance to argue | Intents/Explainer.md |
Full is Argue → review checkpoint → Script → Produce. The checkpoint after the essay is the default: text is cheap to change and a render is not. The user skips it by saying so in the ask ("straight through", "no review, just make it"). Voice has no essay and no DA-written narration; its narration rule, stages and inserts are in Intents/Voice.md. Explainer writes an Essay.md with kind: explainer (narration only, no opposing quotes) at the length asked for, then runs Script and Produce straight through; its optional YouTube leg (thumbnail on the post's header image, full description, private upload) runs through Video, never this skill.
| File | Read when |
|---|---|
References/ScriptSchema.md | writing or linting Script.json |
References/Style.md | composing frames, the thumbnail, or running DesignAudit (the house design system) |
References/Tools.md | running or debugging one stage tool directly, choosing a flag, or finding a run's files |
Setup.md | first run on a machine, any tool stops naming Setup.md, or a Config.json field's meaning or default |
Tools/Run.ts drives every run; the other stage tools, the run layout and the composition files are in References/Tools.md.
| Tool | Contract |
|---|---|
Tools/Run.ts --run <slug> [--from stage] [--to stage] [--force] [--music ambient|pulse] [--accept-review-shortfall] [--polish] | essay: lint, then narrate → score beside capture, then build, then verify beside thumbnail, then describe once verify passes (other stages wait only for what they read); after any failure no stage starts; voice: clean → align → plan → meaning → lint → capture → build → verify → describe |
Everything personal lives in the user zone. First-time setup is Setup.md, and bun skills/VideoEssay/Tools/Setup.ts --check reports what is still missing. With no config file every tool stops and names Setup.md.
LIFEOS/USER/CUSTOMIZATIONS/SKILLS/VideoEssay/Config.json: fonts (a missing face is an error, never a fallback), fontFamilies, avatar, runsRoot, blogBaseUrl, possessive, presenter, narrators.da, colors, badge, skipParagraphs and styleFile; each field's meaning and default is in the Setup.md field table, the face roles and colour keys in Setup.md § Faces and colours. Relative paths resolve against the config dir. Never change narrators.da.label for one run; pass --label at ingest instead.LIFEOS/USER/CUSTOMIZATIONS/SKILLS/VideoEssay/public/: created by Setup.ts and linked from Tools/public. It holds the avatar cut-out and staged runs, never the skill tree.LIFEOS/TOOLS/FishTTS.ts). The DA reads with its one voice, LIFEOS_CONFIG.toml [da.voices.main]: its fish_reference_id names the Fish voice and its fish_speed is the pace (no separate presenter voice since 2026-10-24). The author's clone uses [principal.voice] plus two env ids: FISH_PRINCIPAL_VOICE_ID, the Fish clone itself, and ELEVENLABS_PRINCIPAL_VOICE_ID, the id narration recognizes as a clone read. Key: FISH_API_KEY. Fish applies the pace itself, so no chunk is stretched afterward. Setup.md § LIFEOS_CONFIG.toml and § Accounts and secrets have the details.principal.name and the reader to the DA display name, both from LIFEOS_CONFIG.toml. A DA-read badge shows READ BY <READER> · <POSSESSIVE> AI (THEIR AI unless Config.json possessive says otherwise); a post the DA wrote itself (--label <DA NAME> at ingest) reads WRITTEN AND READ BY <READER>. Neither ever carries a voice-vendor logo or a clone credit.LIFEOS/USER/PRINCIPAL/PRONUNCIATIONS.json when present, plus a per-run pronunciations.json, applied to spoken text only.INTERCEPTOR_TEST_CONTEXT_ID in LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Interceptor/preferences.env; Capture.ts stops naming that file when it is missing.Run.ts refuses a run dir under the config root or the user-data repo.Before the critique, every render passes the Assay video eval set (video-eval, skills/Assay/Tools/VideoEval.ts) in Run.ts's eval stage, as each intent's ## Evals section declares it; a blocking result fails the run.
Nothing this skill produces reaches the user as a first draft. The artifact goes through Skill("ArtDirection"): a direction council before new or large work, then the cross-vendor critique panel (skills/ArtDirection/Tools/Critique.ts) scores it against the rubric and names defects (for a run with a Meaning/Direction.rendered.json, pass --direction <run>/Meaning/Direction.rendered.json so each beat is also judged against its own rationale), then this skill iterates on those defects until the bar or reports the shortfall. Record the user's verdict with skills/ArtDirection/Tools/Calibrate.ts.
Hand-over is skills/ArtDirection/Tools/Deliver.ts. It refuses without a passing critique of those exact bytes and a passing Proof receipt (skills/Assay/Tools/Proof.ts run --artifact <file>), and it writes the delivery receipt the gate reads. Critique Out/essay.mp4 with --out <run> so Critique.json sits one level above it, then run Deliver.ts --artifact <run>/Out/essay.mp4 --critique <run>/Critique.json --to ~/Downloads/<slug>/. The thumbnail ships as the ≤ 2 MB q85 JPEG (Intents/Produce.md), critiqued and delivered the same way.
These change what a run does. Failures the tools already handle are documented at their fix sites in Tools/ (the Capture.ts header, Ingest.ts, Clean.ts, Narrate.ts, Build.ts, Page.ts, Verify.ts); read those when debugging that tool.
Tools/public must be the symlink into the USER store, never a real directory. A stray empty public/ dir (left behind by moving the store, for one) makes every Build and Setup fail on the publicDir invariant; delete it and let Setup.ts recreate the link.check fails on text layered under a chapter card (content_overlap) unless both card text blocks carry data-layout-allow-overlap; the attribute is not inherited.FishTTS.ts) on the model TTS_MODEL names (CROSS_VENDOR.fishVoice). Do not switch TTS_MODEL without re-probing that the new model still returns word timings; captions and beat timing depend on them.Capture.ts embeds the page's loaded faces and replaces post.png only once a marker pass proves the web fonts drew. DOM renders still miss ::before/::after content and mask-image icons (a nav's arrow glyphs, say). --pixel shows all of it, but through Capture.sh it is JPEG quality 50, which breaks the exact-colour marker pass (Capture.test.ts covers the fallback-face judgment).check measures text contrast (WCAG AA 4.5:1) and text overlap at sampled times, and refuses the render (white text on a mid-tone coloured header can fall under 4.5:1; a result wider than the space past its stop can overlap a lane label mid-build). Text colours come through readableOn() against the run's own ground, and DesignAudit measures at settle only, so run the seam check on the staged project when a render is refused; its raw JSON (hyperframes check <dir> --json) names the element, colours and time.Build.ts replaces the mp4's audio with exactly what the composition plays; voice renders fail Verify's −14 ± 1 LUFS if that replacement is skipped (Build.test.ts "delivered loudness").Capture.ts shoots each embed in a headless copy of the pinned browser and pastes the pixels in; loading markers must clear within 30 s or the capture fails, and it never writes a loading state. A link card taller than the reading band is never whole at reading zoom; show it with a screenshot insert of the post itself, selected by the card's own link.Example 1: respond to an interview
User: "Make a video essay answering this interview — I disagree with most of it: <youtube url>"
→ Full: Intake pulls the transcript; Argue writes Essay.md (quotes lint-verified, steelman, disagreement, argument), adversarial read, plus the optional writing audits when installed
→ checkpoint: the essay is shown; on "go", Script.json is written and linted
→ Produce: verify.json green, frames read; mp4 and thumbnail delivered through Deliver.ts and the description copied, all in ~/Downloads/<slug>/
Example 2: the DA reads a new post
User: "DA-read video of my new post on composting"
→ Voice: Ingest --narrator da; the post's own words narrated, its page scrolling with the spoken sentence highlighted, embeds and link cards as on the live page
→ meaning-directed inserts on the paragraphs they illustrate; verify.json green; critiqued and delivered to ~/Downloads/<slug>/
Example 3: edit one paragraph after review
User: "Change the ending — softer on the delete-everything advice"
→ Edit Essay.md and the affected beats in Script.json
→ Run.ts --from narrate: only the changed beats re-synthesize; captures stay cached; re-render, re-verify
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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。