git:20260429.ac9b3f8 to git:20260508.3657f47

38 added, 15 removed. Audit A to A.

---

name: rare-style-explorer

- description: Generate and refine AIGC image prompts by combining rare, prompt-ready visual sub-style tags from a bundled 260-entry style library. Use when the user wants style exploration, image prompt variants, rare visual styles, non-generic aesthetics, style mixing, prompt matrices, or subject-to-style ideation for image generation.

+ description: Generate and refine AIGC image prompts by combining rare, prompt-ready visual sub-style tags from a bundled 620-entry style library. Use when the user wants style exploration, image prompt variants, rare visual styles, non-generic aesthetics, style mixing, prompt matrices, or subject-to-style ideation for image generation.

---

# Rare Style Explorer

## Overview

Use this skill to turn a subject into reusable AIGC prompt variants using rare sub-style tags rather than broad labels such as minimalism, Bauhaus, or cyberpunk.

The bundled library lives at `references/style_library.json`. Load it only when the user asks for specific style lookup, filtering, auditing, or manual curation. For normal prompt generation, run `scripts/explore_styles.py`.

+ The library is keyword-first. Some 2026-05 entries were distilled from public Midjourney SREF style-reference galleries and documentation, but this skill does not store SREF codes and should not output `--sref` parameters unless the user explicitly changes the goal.

+ Final prompt variants should be written in Chinese by default. English prompt tokens remain in the style metadata for lookup and manual conversion, but the user-facing prompt text should use Chinese style names, Chinese visual DNA, and Chinese anti-drift constraints.

## Default Workflow

1. Identify the subject and output goal.

- Product or packaging: prefer `product`.

- Character, avatar, IP, portrait: prefer `character`.

- Poster, cover, social visual: prefer `poster`.

- Narrative scene: prefer `scene`.

- Same subject with multiple surfaces: prefer `material-series`.

- Fast exploration: use `minimal`.

2. Generate combinations with:

```bash

- python3 scripts/explore_styles.py "SUBJECT" --mode minimal --count 8

+ python3 scripts/explore_styles.py "SUBJECT" --mode minimal --count 8 --freshness high

```

3. Review the generated style IDs and remove combinations that conflict with the subject, platform, or brand.

- 4. Output in this order unless the user requests another format:

+ 4. If the user asks for a more targeted direction, use `--style-family`:

+ - `film`: cinematic genres and light color

+ - `fashion`: editorial styling and subculture looks

+ - `product`: toy/product/material presentation

+ - `photography`: photographic tone and media defects

+ - `illustration`: drawing, manga, animation, and picture-book media

+ - `graphic`: posters, packaging, catalogs, and print layouts

+ - `craft`: regional craft, folk pattern, and historical media

+ - `digital`: UI, game, old software, and screen media

+ - `space`: architectural and scene atmosphere

+ - `material`: surface and tactile material variants

+ 5. Output in this order unless the user requests another format:

1. analysis dimensions

2. selected style logic

3. prompt variants

4. variable slots

5. negative constraints

6. reusable template

## Combination Rules

Build each prompt from:

```text

- {subject}, {base_style}, {surface_or_light}, {format_or_space},

- clear silhouette, strong visual identity, high detail,

- avoid generic modern minimalism, avoid random extra text, avoid messy symbols,

- avoid losing subject identity

+ {subject}，{base_style}，{surface_or_light}，{format_or_space}，

+ 主体轮廓清晰，视觉识别度强，高细节，

+ 避免泛化的现代极简风，避免随机多余文字，避免混乱符号，

+ 避免丢失主体身份

```

Use one strong base style. Add zero or one surface/light style. Add zero or one format/space style. Add zero or one defect layer only when a more analog or media-specific finish is useful.

Do not stack too many strong style anchors. If the subject is fragile, such as a logo, facial identity, product silhouette, or readable packaging, prioritize recognizability over novelty.

## Script Usage

Run from the skill folder:

```bash

python3 scripts/explore_styles.py "ceramic cat perfume bottle" --mode product --count 6 --seed 42

python3 scripts/explore_styles.py "martial arts heroine" --mode character --count 5

python3 scripts/explore_styles.py "AI knowledge base app icon" --mode poster --count 8 --format json

python3 scripts/explore_styles.py "retro cafe mascot" --style-id S008 --count 4

+ python3 scripts/explore_styles.py "fashion portrait" --mode character --style-family fashion --freshness high --count 8

+ python3 scripts/explore_styles.py "blind box toy" --mode product --style-family product --freshness high --count 8

```

Useful options:

- `--mode`: `minimal`, `product`, `character`, `poster`, `scene`, `material-series`.

- `--count`: number of prompt variants.

- `--seed`: reproducible random seed.

- `--style-id`: force a base style by library ID, then vary supporting layers.

- `--format`: `markdown` or `json`.

+ - `--freshness`: `normal` or `high`; `high` biases toward newer, lower-frequency, visually specific entries.

+ - `--style-family`: narrow the base style pool to a concrete family such as `film`, `fashion`, `product`, `photography`, `illustration`, `graphic`, `craft`, `digital`, `space`, or `material`.

+ - `--avoid-generic` / `--no-avoid-generic`: generic suppression is on by default; turn it off only when you need broader styles.

## Manual Library Lookup

Use `references/style_library.json` when you need to:

- - inspect all 260 entries

+ - inspect all 620 entries

- search by Chinese style name, English prompt token, category, subject suitability, or failure mode

- select styles manually for a themed series

- quote style metadata such as `容易翻车` or `补救提示`

For quick shell lookup:

```bash

python3 - <<'PY'

import json

p='references/style_library.json'

data=json.load(open(p, encoding='utf-8'))

for s in data['styles']:

if 'giallo' in s.get('English prompt tokens','').lower() or '铅黄' in s.get('中文风格名',''):

- print(s['style_id'], s['中文风格名'], s['English prompt tokens'])

+ print(s['style_id'], s['中文风格名'], s['视觉DNA / 关键词'], s['English prompt tokens'])

```

## Output Standards

- Keep prompts specific, visual, and generation-ready. Include both Chinese explanation and English prompt text when useful.

+ Keep prompts specific, visual, and generation-ready. The final prompt should be Chinese. Use English prompt tokens only as metadata or when the user explicitly asks for an English version.

+ Prefer precise sub-style phrases such as `sun-faded folk horror poster photography`, `chrome Y2K fashion editorial`, `pastel ceramic toy photography`, or `overexposed tropical VHS travelogue`. Avoid relying on isolated generic words such as cinematic, surreal, retro, futuristic, cyberpunk, minimalism, or aesthetic.

Always include anti-drift constraints for exploration outputs:

```text

- avoid generic modern minimalism, avoid random extra text, avoid messy symbols,

- avoid distorted hands/faces, avoid losing subject identity

+ 避免泛化的现代极简风，避免随机多余文字，避免混乱符号，

+ 避免手部和面部畸形，避免丢失主体身份

```

For product prompts, add:

```text

- readable shape, clean background, no clutter, product remains recognizable

+ 造型可读，干净背景，无杂物，产品保持可识别

```

For character prompts, add:

```text

- clear face, expressive pose, 1-2 key accessories only

+ 面部清晰，姿态有表现力，只保留1-2个关键配饰

```

For poster or cover prompts, add:

```text

- limited pseudo-typography, strong title area, no long readable text

+ 少量伪文字，明确标题区域，不要长段可读文字

```