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。
Directs visual quality across images, films, slides, and site captures through a direction council, two vendors' vision judgments against anchored craft criteria, ranked defects, bounded iteration, gated delivery, and recorded human calibration labels. USE WHEN critique this, art direction, quality loop, rate the visuals, is this pro, iterate until it's great, deliver the render, art council. NOT FOR producing the artifact itself (use the appropriate image, motion, story-film, video-essay, web-design, or slide-production capability), for text writing audits, or for rating and rendering an ISA (ISA).
实测评分 未实测
查看安装方法ArtDirection依据简报评估图片、影片、幻灯片和网站页面截图。简报记录受众、参考资料、视觉方向和质量标准;方向评审小组将视觉意图整理到Direction.md。随后,视觉评审依据制作标准打分,并按优先级列出可修复的问题。需要修改时,最多可进行四轮评审,并报告作品是否达到标准;若未达标,也会说明尚存问题。只有评审通过的作品才能进入交付流程。人工评分和意见也可以作为标签记录,供之后校准评审使用。
适合在发布前检查图片或视频的视觉效果,将评审意见整理成有先后顺序的修改清单,或在交付合格作品时附上评审记录。
需要配置评审服务。首次评审影片还需要ffmpeg。ArtDirection负责制作完成后的质量评估和交付,不负责创作图片、影片、幻灯片或网站页面。它不用于文章写作审查,也不用于评估或渲染ISA。
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。
A shared quality loop for artistic deliverables: the Brief defines audience, register, references and bar; a Direction council resolves the visual intent into Direction.md; the domain skill produces the artifact against it; Critique yields two vendors' scores and ranked defects; iteration reaches the bar or reports the shortfall at four rounds; only a passing result reaches Deliver; and Calibrate records the principal's own verdict as evidence for later judge calibration. The domain skill makes the artifact; this skill supplies the quality evidence.
vendor_missing flag as weaker evidence.References/Rubric.md.| Intent | Trigger | File |
|---|---|---|
| Loop | Full quality loop or iterate to the bar | Intents/Loop.md |
| Direction | Art council or new visual direction | Intents/Direction.md |
| Critique | Rate the visuals or inspect a render | Intents/Critique.md |
| Deliver | Hand over a passing artifact | Intents/Deliver.md |
| Calibrate | Record the principal's score and note | Intents/Calibrate.md |
| File | Read when |
|---|---|
References/Rubric.md | writing a brief or reading a critique: the anchored axes, document and figure rules, defect shape and the Brief.json schema (References/BriefExample.json is a filled brief); Critique reads it at runtime |
Setup.md | first-time setup: the two judge vendors' access, ffmpeg for films, and a no-cost proof |
Run with bun skills/ArtDirection/ prefixed to each tool path, from the config root.
| Tool | CLI contract |
|---|---|
| Critique | Tools/Critique.ts --video <mp4> | --image <png> [--images <glob>] --brief <Brief.json> [--bar 8.5] [--samples 12] [--concurrency N] [--vendors openai,anthropic] [--sample-seed <n>] [--out <dir>] [--json] [--allow-identity] |
| Deliver | Tools/Deliver.ts --artifact <file> [--critique <Critique.json>] --to <dir> |
| Calibrate | Tools/Calibrate.ts --artifact <file> --verdict <1-10> --note "<text>" |
Each intent file carries its tool's defaults, refusals and exit codes.
User customizations live at <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/ (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). When it exists, its PREFERENCES.md, configurations and resources override the defaults here; when absent, the defaults stand. First-time setup (accounts, keys, config, a proof it works) is in Setup.md.
"calibration": "<target>". Critique then adds that Calibrate target's Taste.json (the principal's labeled likes and dislikes) to the judge prompt and records its labels_hash in the report; a target with no profile yet is noted under outstanding. Briefs without the field produce the prompt they always did.~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/labels.jsonl (fields in Intents/Calibrate.md).art_judges in <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/Config.md (OpenAI, Anthropic and Google have seats; a missing file or key stops the critique with an error naming both). That folder's PREFERENCES.md, when present, is appended to References/Rubric.md as install-specific judging rules, and check_set.version hashes the two together. OpenAI vision runs as codex exec -i through the codex CLI signed in to ChatGPT (Assay/Tools/OpenAIVision.ts; no API key is read); Anthropic vision runs through LIFEOS/TOOLS/Inference.ts image input (the claude CLI on the subscription), never falling back to another vendor. Panel selection (--vendors) is in Intents/Critique.md. No credentials belong in briefs or logs.LIFEOS/TOOLS/models.ts: CROSS_VENDOR.openaiJudge for OpenAI and the high rung of EFFORT_MODEL for Anthropic, never pinned here..claude location.Deliver.ts because a first-draft video reached the principal with no critique on 2026-09-17, while the rule lived only in prose (the story is in the USER extension's History.md).concept (does the artifact carry its idea as one metaphor a stranger reads at a glance), and overall follows concept most. Changed 2026-09-28 after the principal's first Calibrate scores showed the old craft-first rubric ranking the scored artifacts almost at random (the measurement is in the USER extension's History.md).User: "iterate until it's great" on a render → Loop intent → Brief.json and Direction.md exist, critique rounds run until the artifact passes the bar, or the four-round cap is reported with its shortfall.
User: "rate the visuals" → Critique intent → Critique.json with both vendors' scores, ranked defects and a pass, fail or unavailable verdict.
User: "that one's a 6, the type is cramped" → Calibrate intent → one label appended with verdict 6 and the note in the principal's words.
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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。
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。
用代码断言或校准后的模型评审来评估提示词、模型和智能体,并比较多轮试验或A/B结果。报告可靠性指标,也可将主观判断拆成结构化问题和评估流程。