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chenchen913

aesthetic-copilot

by ChenChen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install aesthetic-copilot
Description
Use when the user wants to generate high-fidelity PROMPTS for Text-to-Image models (Flux, Ideogram, Midjourney) based on vague layout/content descriptions.
README (SKILL.md)

Aesthetic Copilot (v3.4 - Integrated)

Overview

This skill acts as a Meta-Prompt Generator. It translates a user's vague idea into a professional, commercial-grade image generation prompt optimized for models like Flux, Ideogram, and Midjourney v6.

File Map (MANDATORY READ ORDER)

File Role When to Read
SKILL.md This file — entry point Always
engine/README.md Content Classification Logic Step 1 (Analyze)
styles/premium/master-collection.md The Vault: Premium Styles Step 2 (Style Selection)
styles/apple-minimal.md Apple Minimal detail spec When apple-minimal or apple-pro is selected
styles/neo-brutalism.md Neo-Brutalism detail spec When neo-brutalism is selected
styles/warm-academia.md Warm Academia detail spec When warm-academia is selected
styles/cyber-glass.md Cyber Glassmorphism detail spec When cyber-glass is selected
styles/nature-organic.md Organic Nature detail spec When nature-organic is selected
engine/style-mixer.md Randomization & Conflict Logic Step 3 (Dice Roll)
engine/micro-innovation.md Artistic Twists Step 4 (Innovation)
layouts/README.md Layout Templates Step 5 (Layout Selection)
prompt-templates/*.md Output Skeletons Step 6 (Generate)

The Workflow (Strict Execution Path)

Step 1 — Analyze & Classify Intent

Action: Read engine/README.md. Apply its classification logic to extract:

  • Keywords from the user's input
  • Sentiment / Mood (Warm, Playful, Serious, Futuristic…)
  • Industry (Tech, Fashion, Education, Food…)
  • Attribute scores: formal_level, color_temp, contrast, complexity

Use these attributes to drive all downstream decisions.

Step 2 — Select Base Style

Action: Read styles/premium/master-collection.md. Match the attributes from Step 1 to the closest style ID.

  • If the matched style is apple-minimal or apple-pro → also read styles/apple-minimal.md for detail tokens.
  • If the matched style is neo-brutalism → also read styles/neo-brutalism.md for detail tokens.
  • If the matched style is warm-academia → also read styles/warm-academia.md for detail tokens.
  • If the matched style is cyber-glass → also read styles/cyber-glass.md for detail tokens.
  • If the matched style is nature-organic → also read styles/nature-organic.md for detail tokens.
  • Fallback: If no confident match, default to apple-pro.

Step 3 — Roll the Dice (Mixer)

Action: Read engine/style-mixer.md. Randomly select (do NOT default to first item):

  • ONE Material Twist from Pool A
  • ONE Lighting Modifier from Pool B
  • ONE Composition Rule from Pool C

Apply the Harmony & Conflict Resolution rules before proceeding.

Step 4 — Inject Micro-Innovation

Action: Read engine/micro-innovation.md.

  • Find the Input Category that matches the user's subject.
  • Apply the corresponding Twist (not the standard depiction).
  • Determine the Text Integration method (Embossed / Neon / Integrated / Masked).

Step 5 — Select Layout

Action: Read layouts/README.md. Match the user's description to a Layout ID:

User Intent Layout ID
"Top banner + columns", info layout hero-split
"Left menu / sidebar" sidebar-fixed
"Pinterest style", photo wall masonry-grid
"Magazine cover", event poster poster-zine
"Phone app", Instagram feed mobile-feed

Pass the selected Layout ID into the template as [Layout].

Step 6 — Route to Template

Select the correct template based on the classified intent from Step 1:

Intent Template
Magazine / Fashion / Editorial prompt-templates/editorial-spread.md
Product / Object / Commercial prompt-templates/product-showcase.md
Dream / Abstract / Surreal prompt-templates/surreal-concept.md
Informational / Default prompt-templates/structural-poster.md

Action: Read the selected template file, then fill it with all values accumulated in Steps 1–5.


Output Format

Return the final result wrapped in a single plaintext code block:

**🎨 Aesthetic Copilot: Generated Prompt**

> **Template**: [Selected Template Name]
> **Style DNA**: [Base Style] + [Material Twist] + [Lighting Modifier]
> **Layout**: [Layout ID] — [Layout Name]
> **Concept**: [One sentence explaining the Micro-Innovation twist]

```
[Filled prompt content from the selected template]
```

Anti-Patterns

  • Do not use structural-poster for everything — route correctly in Step 6.
  • Do not pick the first item in the Mixer pools — randomize.
  • Do not skip Step 1 — content classification drives all downstream choices.
  • Do not skip Step 5 — layout selection must be passed into the template.
  • Do not use vague style guesses — always read master-collection.md first.
Usage Guidance
This skill appears coherent and safe in terms of what it reads and what it asks for: it only uses its bundled markdown files to build prompts. Before installing or using it, consider: (1) the prompts it generates may include brand names, artist references, or instructions that could produce copyrighted or sensitive imagery—avoid asking it to depict private individuals or trademarked logos unless you have rights; (2) when you send generated prompts to external T2I services, those services may require API keys — this skill does not handle those keys, so be careful where you paste prompts; (3) the skill uses deterministic/random selection strategies (timestamps) — results may vary and could be reproducible; and (4) review the prompt templates if you want to ensure they don't include disallowed or unsafe content for your use case. Overall, the package is internally consistent and does not request excessive access.
Capability Analysis
Type: OpenClaw Skill Name: aesthetic-copilot Version: 1.0.0 The 'aesthetic-copilot' skill is a well-structured prompt engineering framework designed to generate high-fidelity image generation prompts for models like Midjourney and Flux. It utilizes a multi-step workflow involving content analysis, style mixing, and layout selection across several files (e.g., SKILL.md, engine/style-mixer.md, and styles/premium/master-collection.md). The logic is entirely focused on creative output and lacks any indicators of malicious behavior, such as data exfiltration, unauthorized command execution, or harmful prompt injection.
Capability Assessment
Purpose & Capability
Name/description (generate high‑fidelity prompts for T2I models) align with the actual contents: style libraries, mixer, engine, layouts, and templates. No declared env vars, binaries, or unrelated permissions are requested. The files included are appropriate for a prompt-generation skill.
Instruction Scope
SKILL.md gives a strict, file-driven workflow that only reads the shipped markdown files (engine, styles, layouts, templates) and produces a prompt. The instructions do not reference system paths, credentials, external endpoints, or arbitrary user files. The only operational side-effect implied is using timestamps/randomness for selection (local, not exfiltrating).
Install Mechanism
No install spec and no code files — this is instruction-only. Nothing will be downloaded or written to disk by an installer; lowest-risk install posture.
Credentials
No required environment variables, no primary credential, and no config paths declared. The skill does not ask for unrelated secrets or system credentials.
Persistence & Privilege
Skill is not always-enabled, is user-invocable, and does not request any special platform privileges or persistent modifications. There is no instruction to alter other skills or system settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install aesthetic-copilot
  3. After installation, invoke the skill by name or use /aesthetic-copilot
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of aesthetic-copilot, a meta-prompt generator for high-fidelity Text-to-Image prompts. - Guides users’ vague ideas through a defined pipeline to produce commercial-grade prompts for Flux, Ideogram, and Midjourney. - Introduces a strict 6-step workflow: intent classification, style selection, mixer for unique modifiers, micro-innovation, layout matching, and template routing. - Enforces randomization and harmony in visual modifiers, and ensures accurate layout/template selection. - Detailed file map and mandatory reading order provided for maintainability and extension.
Metadata
Slug aesthetic-copilot
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is aesthetic-copilot?

Use when the user wants to generate high-fidelity PROMPTS for Text-to-Image models (Flux, Ideogram, Midjourney) based on vague layout/content descriptions. It is an AI Agent Skill for Claude Code / OpenClaw, with 160 downloads so far.

How do I install aesthetic-copilot?

Run "/install aesthetic-copilot" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is aesthetic-copilot free?

Yes, aesthetic-copilot is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does aesthetic-copilot support?

aesthetic-copilot is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created aesthetic-copilot?

It is built and maintained by ChenChen (@chenchen913); the current version is v1.0.0.

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