gpt-image-2-style-library

AI Community

Repo stars 6,954
Author repo awesome-gpt-image-2

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Choose GPT-Image2 / gpt-image-2 visual styles and industrial prompt templates from the awesome-gpt-image-2 style library. Use wh…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Example Output”, “Reference”, “Workflow”, “Output Defaults”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name gpt-image-2-style-library directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Example Output / Reference / Workflow” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

GPT-Image2 Style Library

Use this skill to turn a user's image-generation intent into a production-ready GPT-Image2 prompt using the awesome-gpt-image-2 style library.

Example Output

City life system map example

Example request: 用 gpt-image-2-style-library 技能生成城市生命系统图谱

Reference

Read references/style-library.md before choosing a template or style.
The reference is generated from data/style-library.json in the repository.
Prefer the reference over memory when template names, categories, covers, or style tags matter.

Workflow

Detect the user's language and answer in that language.
Identify the user's target output: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or special task.
Match the request in this order: template category, visual style tag, scene tag, then nearest example cases.
If one template is clearly strongest, use it directly. If several are plausible, present 2-3 options with short reasons and ask the user to choose.
Build the final prompt with these blocks:
- subject and task
- composition and layout
- visual style and materials
- text and label requirements
- aspect ratio and output format
- constraints and negative details
Include the selected template name and any useful example case IDs.

Output Defaults

Provide a copyable prompt first.
Keep constraints concrete: exact text, aspect ratio, readable labels, layout hierarchy, and avoided artifacts.
For Chinese requests, write the final prompt in Chinese unless the user asks for English.
For English requests, write the final prompt in English unless the user asks for Chinese.
When the user asks for multiple concepts, reuse one template and vary subject, composition, palette, and scene.

Maintenance

When the source repository changes, run:

npm run generate:style-skill

To install the skill into the local Codex skill folder, run:

npm run install:skill

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @freestylefly · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: macOS · Linux · Windows
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview gpt-image-2-style-library.preview

# Example Output

![City life system map example](assets/city-life-system-map.png) Example request: `用 gpt-image-2-style-library 技能生成城市生命系统图谱`

SKILL.md SKILL.md

# GPT-Image2 Style Library

Use this skill to turn a user's image-generation intent into a production-ready GPT-Image2 prompt using the awesome-gpt-image-2 style library.

## Example Output

![City life system map example](assets/city-life-system-map.png)

Example request: `用 gpt-image-2-style-library 技能生成城市生命系统图谱`

## Reference

- Read `references/style-library.md` before choosing a template or style.
- The reference is generated from `data/style-library.json` in the repository.
- Prefer the reference over memory when template names, categories, covers, or style tags matter.

## Workflow

1. Detect the user's language and answer in that language.
2. Identify the user's target output: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or special task.
3. Match the request in this order: template category, visual style tag, scene tag, then nearest example cases.
4. If one template is clearly strongest, use it directly. If several are plausible, present 2-3 options with short reasons and ask the user to choose.
5. Build the final prompt with these blocks:
   - subject and task
   - composition and layout
   - visual style and materials
   - text and label requirements
   - aspect ratio and output format
   - constraints and negative details
6. Include the selected template name and any useful example case IDs.

## Output Defaults

- Provide a copyable prompt first.
- Keep constraints concrete: exact text, aspect ratio, readable labels, layout hierarchy, and avoided artifacts.
- For Chinese requests, write the final prompt in Chinese unless the user asks for English.
- For English requests, write the final prompt in English unless the user asks for Chinese.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Example Output → Reference → Workflow → Output Defaults → Maintenance
terms    -> - Read references/style-library.md before choosing a template or style. · 1. Detect the user's language and answer in that language. · - Provide a copyable prompt first.
files/cmd -> 用 gpt-image-2-style-library 技能生成城市生命系统图谱 · references/style-library.md · data/style-library.json · assets/city-life-system-map.png
body sha256 -> 6c862849e1e3