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ArtDirection

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。

原文查看原文说明

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).

danielmiessler/LifeOS星标 19,635(GitHub 19,635 + cccskills 0)
集成工具OpenAI

实测评分 未实测

查看安装方法

适用场景

  • 按优先级整理图片修改项
  • 依标准评估影片或幻灯片
  • 发布前检查视觉效果
  • 连同评审记录交付合格作品

中文介绍

功能

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。

适用场景

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。

使用示例

  • “评估这张渲染图,告诉我优先修改哪些地方。”
  • “反复评审并修改这段影片,直到达到标准。”
  • “给这份作品打7分,并记录文字排得太挤。”

注意事项

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。

文档检查

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)。

安全性发送数据、删除文件、执行命令等是否有说明
A良好交付须评审通过,且明确禁止将凭据写入简报或日志。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足需要配置评审服务并为影片安装`ffmpeg`,具体步骤指向`Setup.md`。

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。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

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包含的文件(12)

  • SKILL.md7.4 KB
  • Intents/Calibrate.md964 B
  • Intents/Critique.md4.7 KB
  • Intents/Deliver.md1.4 KB
  • Intents/Direction.md1.4 KB
  • Intents/Loop.md2.0 KB
  • References/BriefExample.json982 B
  • References/Rubric.md7.3 KB
  • Setup.md3.7 KB
  • Tools/Calibrate.ts4.5 KB
  • Tools/Critique.ts73.0 KB
  • Tools/Deliver.ts15.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

ArtDirection

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.

Ideal State

  • Nothing artistic reaches the principal or a public surface without a current passing critique from the judge panel bound to the exact bytes delivered. The panel is cross-vendor whenever two approved vendors are reachable; a one-vendor panel may still pass, and its report carries the vendor_missing flag as weaker evidence.
  • Every verdict is honest: pass only when the pooled median reaches the bar and each vendor median reaches bar minus 0.5, with no UNKNOWN score or critical defect, a cap hit reported with its shortfall, and an unreachable vendor reported as unavailable rather than as a quality failure.
  • Each round names ranked, located defects a producer can fix, judged against the concept-first rubric rather than adjectives.
  • A figure's anatomy and mechanics are judged as a defect class: extra or missing limbs, a grip or pose that does not match the object held, or physically impossible motion is a critical defect located by image region or frame, so the critique fails it; the judges read this rule from References/Rubric.md.
  • The principal's own scores are kept verbatim as labels, and judges change only through a deliberate calibration, never automatically.

Intent Routing

IntentTriggerFile
LoopFull quality loop or iterate to the barIntents/Loop.md
DirectionArt council or new visual directionIntents/Direction.md
CritiqueRate the visuals or inspect a renderIntents/Critique.md
DeliverHand over a passing artifactIntents/Deliver.md
CalibrateRecord the principal's score and noteIntents/Calibrate.md
FileRead when
References/Rubric.mdwriting 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.mdfirst-time setup: the two judge vendors' access, ffmpeg for films, and a no-cost proof

Tool contract

Run with bun skills/ArtDirection/ prefixed to each tool path, from the config root.

ToolCLI contract
CritiqueTools/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]
DeliverTools/Deliver.ts --artifact <file> [--critique <Critique.json>] --to <dir>
CalibrateTools/Calibrate.ts --artifact <file> --verdict <1-10> --note "<text>"

Each intent file carries its tool's defaults, refusals and exit codes.

Data & Config

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.

  • A brief may name "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.
  • Calibration labels append to ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/labels.jsonl (fields in Intents/Calibrate.md).
  • The panel is the vision vendors named by 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.
  • Models come from LIFEOS/TOOLS/models.ts: CROSS_VENDOR.openaiJudge for OpenAI and the high rung of EFFORT_MODEL for Anthropic, never pinned here.
  • Preferences use configRoot above; the current credential and label implementations use the home .claude location.

Gotchas

  • Hand-over is gated by 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).
  • The rubric leads with 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).

Examples

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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