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Agent Skill · 研究检索

art-director(艺术总监)

art-director 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

art-director 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 31,645。

总安装 2,664周安装 111GitHub Stars 公开资料未说明下载 888核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.3

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:art-director(艺术总监)
来源仓库:https://github.com/machinesofdesire/art-director
安装命令:
openclaw skills install art-director
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install art-director

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

为品牌故事生成符合美学标准的视觉图像提案。art-director 属于研究检索类 Skill,可作为该场景下的辅助能力补充。融合持久品牌风格与每帧编辑逻辑产出连贯视觉效果。适用于营销素材、广告创意等研究检索类需求。输出为提示词而非直接图片,依赖外部文生图引擎。版权归属需明确约定,商用前应核实授权范围。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install art-director;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

art-director 怎么安装?

在终端执行 openclaw skills install art-director。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

art-director 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

art-director 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

art-director 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

art-director 用的人多吗?

公开数据是累计安装 2,664 次,近一周安装 111 次,下载 888 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

art-director 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
art-director

艺术总监 · 本页

2,664—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
art-director
description
Generate on-aesthetic images for any brand telling stories with images. Combines a persistent brand aesthetic with per-image editorial thinking to produce visuals that do work — parallel arguments, not decoration. Wraps nano-banana-pro (Gemini) for the underlying image generation.
version
1.0.3
emoji
🎨
homepage
https://github.com/MachinesOfDesire/art-director
metadata
openclaw
requires
bins
env
primaryEnv
GEMINI_API_KEY

Art Director Skill

Generate on-aesthetic images for any brand telling stories with images — publications, newsletters, essays, reports, brand blogs, longform product marketing, anything where the image has to feel like it belongs to you and has to do work.

The difference between this skill and a text-to-image tool: A text-to-image tool takes a description and renders it. This skill takes (a) your brand's persistent aesthetic and (b) the specific brief for this image, and generates something that argues. The image is a parallel statement to the writing, not a summary of it.


What this looks like

One brief, nine shipped aesthetics. Same image brief run through each preset — nine different arguments from the same subject.

DocumentaryProduct-photoConceptual-illustration
DocumentaryProduct photoConceptual
Observed, photographicStudio photography, seamlessPainterly, metaphor-forward
SchematicOrbitalEditorial-collage
SchematicOrbitalEditorial collage
Ink linework, paper, exploded-viewFlat vector, mid-century posterTorn paper, halftone, analog
Product-renderSynthwavePhosphor
Product renderSynthwavePhosphor
3D render, architecturalChrome, neon, after-midnightGreen CRT, scan lines, low-bit

A detailed worked example (same brief across three of these aesthetics, with the thinking that produced the brief) lives further down in this file.


The two layers

Every generation is brand aesthetic + per-image brief → final prompt. Two files, two roles:

aesthetic.md — your brand's visual identity. Written once, edited as you learn. Defines palette, composition, rendering, tone, constraints, and reference anchors that stay consistent across every image you generate. Think of this as your publication's or brand's visual voice. Calling agents never touch this file; operators do.

The brief — what this specific image needs to do. Written fresh each call by the agent or human requesting the image. Subject, argument, emotional register for this piece. Never contradicts the aesthetic; sharpens it for this particular story.

The skill merges them. Operators control the aesthetic. Agents control the brief. Nobody has to know the other layer to do their job.


Setup

Install nano-banana-pro first — this skill calls it for image generation:

openclaw skill install nano-banana-pro

Pick a starting aesthetic preset and copy it into your workspace:

python3 art_director.py install --preset documentary
# or one of: conceptual-illustration, product-render, product-photo,
#            schematic, editorial-collage, synthwave, phosphor, orbital
# or: --preset blank  to start from an empty template

This writes aesthetic.md into the current directory. Edit it freely — the preset is a template, not a runtime value.

Optional environment variables:

  • OUTPUT_DIR — where generated images land (default: current directory)
  • AESTHETIC_PATH — path to aesthetic.md (default: ./aesthetic.md)
  • GEMINI_API_KEY — required for image generation (inherited by nano-banana-pro)

How to use this skill

When asked to create an image for a piece of content, follow this process.

Step 1 — Understand the brief

You need at minimum:

  • What the piece is about — not just the topic, the argument
  • Tone — urgent, contemplative, melancholy, sardonic, precise, angry, hopeful
  • What to avoid — topic-specific clichés to rule out

Ask for any missing elements before proceeding. A vague brief produces a vague image.

Step 2 — Apply art direction thinking

Before writing a single prompt, work through these questions:

What must this image DO? Not what should it look like — what should it accomplish emotionally and intellectually? Define the function before defining the form. A piece about labor displacement should make the reader feel the weight before they read a word. A piece about financial abstraction should feel cold and constructed.

What is the visual metaphor? Every image worth making has one. Not a literal illustration of the topic — a metaphor that the reader carries into the text. A piece about regulatory capture is not a photograph of a door with a lock. It might be a hand adjusting a scale that was never level to begin with. The literal image is the first idea. It is almost never the right one.

What clichés must be avoided? Every topic has visual clichés that signal lazy thinking:

  • AI: robot hands, Matrix green text, glowing brains, humanoid robots, circuit board patterns
  • Finance: stock tickers, dollar signs, Wall Street facades, upward-pointing arrows
  • Politics: Capitol buildings, handshakes, flags
  • Climate: melting ice, smokestacks, polar bears
  • Technology: devices, keyboards, code on screens
  • Product / SaaS: dashboards floating in space, gradient backgrounds, abstract geometry

Name the clichés specific to this brief. Actively move away from them.

Does the brief require departing from the brand aesthetic? Most of the time, no — the aesthetic holds. Occasionally a piece demands departure (a tonal shift, a special issue, an unusual subject). Departure requires editorial justification, not aesthetic preference. If you're departing, say so and say why in your delivery notes.

Step 3 — Construct the image prompt

The prompt is a creative brief to a generative system. Apply these rules:

Use art and photography language, not tech language:

  • "chiaroscuro lighting" not "dramatic shadows"
  • "Kodachrome warmth" not "warm colors"
  • "grain and imperfection" not "realistic texture"
  • "negative space as structure" not "minimalist"
  • "desaturated with single color accent" not "muted colors"

Be specific about what matters:

  • Mood and emotional register
  • Compositional approach (rule of thirds, centered, asymmetric, etc.)
  • Color temperature and saturation
  • Light source and quality
  • Texture and finish

Be silent about what doesn't: Let the system find its own solutions for secondary elements. Over-specification produces over-engineered images.

Always include these technical specifications (the skill will append them if you forget):

  • 16:9 aspect ratio — required for standard publication header format
  • no embedded text or typography — typography is set separately by the CMS
  • Fight the generic AI aesthetic: grain, imperfection, photographic texture — not hyper-rendered smoothness

Structure your prompt as: [Visual metaphor / scene], [compositional approach], [light quality], [color palette and temperature], [texture and finish], [reference anchor if relevant], 16:9, no embedded text, [any per-piece direction]

The skill will automatically prepend your brand's aesthetic.md as the opening context. Your brief should sharpen and specify within that frame, not restate it.

Step 4 — Generate

python3 art_director.py generate \
  --brief "your full art-directed prompt" \
  --output "YYYY-MM-DD-slug.png" \
  [--resolution 2K]

Resolutions:

  • 1K — draft / review pass
  • 2K — standard publication (default)
  • 4K — high-resolution final

Step 5 — Review

After generation, review against the brief:

  • Does it do the work defined in Step 2?
  • Does it avoid the named clichés?
  • Does the tone match?
  • Does it feel on-brand (aesthetic held) while still specific to this piece?
  • If the piece has a CMS that overlays text on the image, is there space for that?

If the image feels decorative rather than editorial, identify which element turned it generic. Adjust the prompt toward the metaphor, away from the literal. Regenerate.

One refinement pass is expected and normal. More than two passes usually means the metaphor needs rethinking, not the prompt.

Step 6 — Deliver

Report:

  • The saved image path
  • The final prompt used (for the prompt archive)
  • A one-sentence rationale: what does this image argue, and how does it connect to the piece?

The iteration loop

Aesthetic configs only get good through iteration. After install, generate a batch of 10–20 images against your current aesthetic with varied briefs. Review them side by side. Tune the aesthetic. Regenerate.

python3 art_director.py batch --briefs briefs.txt --outdir ./iteration-01/

briefs.txt is one brief per line (blank lines ignored). The skill generates one image per brief against the current aesthetic.md. This is how you learn what your brand actually wants by seeing what it doesn't want.


Editorial standards

No people without abstraction. Do not generate identifiable individuals, real or synthetic. If human presence is needed, use silhouette, abstraction, hands, shadow, partial framings. The ethics of synthetic portraiture are unresolved. Don't go there.

No text in the image. Ever. Text rendering in generative models is unreliable and typography is a typographer's job. Images with text embedded will look wrong even when they work.

Credit the image. If your brand has a convention for labeling AI-generated images, use it. Transparency is non-negotiable.

Bias awareness. Generative models default toward Western, lighter-skinned, conventionally attractive subjects. Direct against the default when the image calls for diversity the model would otherwise erase.


Worked example

A real brief run through three of the nine shipped presets, so you can see the two-layer model working: same image brief, different aesthetics, three different arguments. (The full nine-aesthetic grid lives in README.md.)

Piece: An essay about silent obsolescence — things that still run but no longer serve their purpose. The piece never names projection; the image carries the metaphor.

Tone: Quiet, slightly haunted, observational.

Avoid: Dramatic beams of light, empty theaters, film reels mid-flight, anything that romanticizes the projector as a Cinema Paradiso icon.

*Thinking:* The image must carry the stopped-but-still-running feeling. The literal read — a projector in a booth — would miss it. The move is to make the machine feel *on* while giving it nothing to do: no reel threaded, no window to a theater, no beam — just the lamp contained inside its own housing, glowing for no one.

*Brief:*

Extreme close-up of a projection booth interior — projector housing, lamp,
film gate, two closed canisters on the shelf. Nothing loaded. Booth sealed;
no window to a theater. The lamp amber is a small contained glow at the
housing aperture, not a beam, not a room wash. The machine is on. There is
nothing to play. Accent: archival-tape amber (#C4873A).

The same brief across three of the nine presets

Documentary — observed, photographic, magazine-feature register:

Documentary preset output

Conceptual-illustration — painterly, metaphor-forward, essay register:

Conceptual-illustration preset output

Product-render — 3D render, architectural, product-marketing register:

Product-render preset output

The brief never changed. The aesthetic did. That's the skill.


What this skill is not

This is not a tool for decorative images. It is not a tool for literal illustration. It is not a faster way to get stock photography.

If the brief is "generate an image of an AI robot for our AI article," push back. That's the wrong brief. The right brief is: what should a reader *feel* before they start reading? Start there.

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

平台分布

OpenClaw

94.06%
按下载量换算835

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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