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comiccomic 搜索

Agent Skill

comic 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

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337
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:comic(comic 搜索)
来源仓库:https://github.com/zlh-428/naruto-skills
仓库路径:skills/comic
安装命令:
npx skills add zlh-428/naruto-skills --skill "comic"
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 npx skills 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。

AgentSkills.tonpx skills
npx skills add zlh-428/naruto-skills --skill "comic"

简介

该技能用于发现并集成适用于动漫风格生成或漫画创作的 AI 插件。

  • 可在 Claude、Cursor 等平台中扩展二次元内容生产能力。
  • 通过标准化接口接入外部技能库,支持角色设定与分镜生成。
  • 安装依赖 npx 与指定仓库路径,需保证环境兼容性。
  • 建议核对原始文档中的风格限制与使用条款说明。comic 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
comic
description
Knowledge comic creator supporting multiple styles (Logicomix/Ligne Claire, Ohmsha manga guide). Creates original educational comics with detailed panel layouts and sequential image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".

Knowledge Comic Creator

Create original knowledge comics with multiple visual styles.

Usage

/comic posts/turing-story/source.md
/comic  # then paste content

Options

OptionValues
--styleclassic (default), dramatic, warm, sepia, vibrant, ohmsha, realistic, wuxia, shoujo, or custom description
--layoutstandard (default), cinematic, dense, splash, mixed, webtoon
--aspect3:4 (default, portrait), 4:3 (landscape), 16:9 (widescreen)
--langauto (default), zh, en, ja, etc.

Style × Layout × Aspect can be freely combined. Custom styles can be described in natural language.

Aspect ratio is consistent across all pages in a comic.

Auto Selection

Content SignalsStyleLayout
Tutorial, how-to, beginnerohmshawebtoon
Computing, AI, programmingohmshadense
Pre-1950, classical, ancientsepiacinematic
Personal story, mentorwarmstandard
Conflict, breakthroughdramaticsplash
Wine, food, business, lifestyle, professionalrealisticcinematic
Martial arts, wuxia, xianxia, Chinese historicalwuxiasplash
Romance, love, school life, friendship, emotionalshoujostandard
Biography, balancedclassicmixed

Script Directory

Important: All scripts are located in scripts/ subdirectory of this skill.

Agent Execution Instructions:

  1. Determine this SKILL.md file's directory path as SKILL_DIR
  2. Script path = ${SKILL_DIR}/scripts/<script-name>.ts
  3. Replace all ${SKILL_DIR} in this document with actual path

Script Reference:

ScriptPurpose
scripts/merge-to-pdf.tsMerge comic pages into PDF

File Structure

Each session creates an independent directory named by content slug:

comic/{topic-slug}/
├── source-{slug}.{ext}            # Source files (text, images, etc.)
├── analysis.md                    # Deep analysis results (YAML+MD)
├── storyboard-chronological.md    # Variant A (preserved)
├── storyboard-thematic.md         # Variant B (preserved)
├── storyboard-character.md        # Variant C (preserved)
├── characters-chronological/      # Variant A chars (preserved)
│   ├── characters.md
│   └── characters.png
├── characters-thematic/           # Variant B chars (preserved)
│   ├── characters.md
│   └── characters.png
├── characters-character/          # Variant C chars (preserved)
│   ├── characters.md
│   └── characters.png
├── storyboard.md                  # Final selected
├── characters/                    # Final selected
│   ├── characters.md
│   └── characters.png
├── prompts/
│   ├── 00-cover-[slug].md
│   └── NN-page-[slug].md
├── 00-cover-[slug].png
├── NN-page-[slug].png
└── {topic-slug}.pdf

Slug Generation:

  1. Extract main topic from content (2-4 words, kebab-case)
  2. Example: "Alan Turing Biography" → alan-turing-bio

Conflict Resolution: If comic/{topic-slug}/ already exists:

  • Append timestamp: {topic-slug}-YYYYMMDD-HHMMSS
  • Example: turing-story exists → turing-story-20260118-143052

Source Files: Copy all sources with naming source-{slug}.{ext}:

  • source-biography.md, source-portrait.jpg, source-timeline.png, etc.
  • Multiple sources supported: text, images, files from conversation

Workflow

Step 1: Analyze Content → analysis.md

Read source content, save it if needed, and perform deep analysis.

Actions:

  1. Save source content (if not already a file):

- If user provides a file path: use as-is - If user pastes content: save to source.md in target directory

  1. Read source content
  2. Deep analysis following references/analysis-framework.md:

- Target audience identification - Value proposition for readers - Core themes and narrative potential - Key figures and their story arcs

  1. Detect source language
  2. Determine recommended page count:

- Short story: 5-8 pages - Medium complexity: 9-15 pages - Full biography: 16-25 pages

  1. Analyze content signals for style/layout recommendations
  2. Save to analysis.md

analysis.md Format:

---
title: "Alan Turing: Father of Computing"
topic: Biography
time_span: 1912-1954
source_language: en
user_language: zh
aspect_ratio: "3:4"
recommended_page_count: 12
---

## Target Audience

- **Primary**: Tech enthusiasts curious about computing history
- **Secondary**: Students learning about scientific breakthroughs
- **Tertiary**: General readers interested in biographical stories

## Value Proposition

What readers will gain:
1. Understanding of how modern computing was born
2. Emotional connection to a brilliant but tragic figure
3. Appreciation for human cost of innovation

## Core Themes

| Theme | Narrative Potential | Visual Opportunity |
|-------|--------------------|--------------------|
| Genius vs. Society | High conflict, dramatic arcs | Contrast scenes |
| Code-breaking | Mystery, tension | Technical diagrams as art |
| Personal tragedy | Emotional depth | Intimate, somber panels |

## Key Figures & Story Arcs

### Alan Turing (Protagonist)
- **Arc**: Misunderstood genius → War hero → Tragic end
- **Visual identity**: Disheveled academic, intense eyes
- **Key moments**: Enigma breakthrough, arrest, final days

### Christopher Morcom (Catalyst)
- **Role**: Early friend whose death shaped Turing
- **Visual identity**: Youthful, bright
- **Key moments**: School friendship, sudden death

## Content Signals

- "biography" → classic + mixed
- "computing history" → ohmsha + dense
- "personal tragedy" → dramatic + splash

## Recommended Approaches

1. **Chronological** - follow life timeline (recommended for biography)
2. **Thematic** - organize by contributions (good for educational focus)
3. **Character-focused** - relationships drive narrative (good for emotional impact)

Step 2: Generate 3 Storyboard Variants

Create three distinct variants, each combining a narrative approach with a recommended style.

VariantNarrative ApproachRecommended StyleLayout
AChronologicalsepiacinematic
BThematicohmshadense
CCharacter-focusedwarmstandard

For each variant:

  1. Generate storyboard (storyboard-{approach}.md):

- YAML front matter with narrative_approach, recommended_style, recommended_layout, aspect_ratio - Cover design - Each page: layout, panel breakdown, visual prompts - Written in user's preferred language - Reference: references/storyboard-template.md

  1. Generate matching characters (characters-{approach}/):

- characters.md - visual specs matching recommended style (in user's preferred language) - characters.png - character reference sheet - Reference: references/character-template.md

All variants are preserved after selection for reference.

Step 3: User Confirms All Options

IMPORTANT: Present ALL options in a single confirmation step using AskUserQuestion. Do NOT interrupt workflow with multiple separate confirmations.

Determine which questions to ask:

QuestionWhen to Ask
Storyboard variantAlways (required)
Visual styleAlways (required)
LanguageOnly if source_language ≠ user_language
Aspect ratioOnly if user might prefer non-default (e.g., landscape content)

Language handling:

  • If source language = user language: Just inform user (e.g., "Comic will be in Chinese")
  • If different: Ask which language to use

All storyboards and prompts are generated in user's selected/preferred language.

Aspect ratio handling:

  • Default: 3:4 (portrait) - standard comic format
  • Offer 4:3 (landscape) if content suits it (e.g., panoramic scenes, technical diagrams)
  • Offer 16:9 (widescreen) for cinematic content

AskUserQuestion format (example with all questions):

Question 1 (Storyboard): Which storyboard variant?
- A: Chronological + sepia (Recommended)
- B: Thematic + ohmsha
- C: Character-focused + warm
- Custom

Question 2 (Style): Which visual style?
- sepia (Recommended from variant)
- classic / dramatic / warm / sepia / vibrant / ohmsha / realistic / wuxia
- Custom description

Question 3 (Language) - only if mismatch:
- Chinese (source material language)
- English (your preference)

Question 4 (Aspect) - only if relevant:
- 3:4 Portrait (Recommended)
- 4:3 Landscape
- 16:9 Widescreen

After confirmation:

  1. Copy selected storyboard → storyboard.md
  2. Copy selected characters → characters/
  3. Update YAML front matter with confirmed style, language, aspect_ratio
  4. If style differs from variant's recommended: regenerate characters/characters.png
  5. User may edit files directly for fine-tuning

Step 4: Generate Images

With confirmed storyboard + style + aspect ratio:

For each page (cover + pages):

  1. Save prompt to prompts/NN-{cover|page}-[slug].md (in user's preferred language)
  2. Generate image using confirmed style and aspect ratio
  3. Report progress after each generation

Image Generation Skill Selection:

  • Check available image generation skills
  • If multiple skills available, ask user preference

Character Reference Handling:

  • If skill supports reference image: pass characters/characters.png
  • If skill does NOT support reference image: include characters/characters.md content in prompt

Session Management: If image generation skill supports --sessionId:

  1. Generate unique session ID: comic-{topic-slug}-{timestamp}
  2. Use same session ID for all pages
  3. Ensures visual consistency across generated images

Step 5: Merge to PDF

After all images generated:

npx -y bun ${SKILL_DIR}/scripts/merge-to-pdf.ts <comic-dir>

Creates {topic-slug}.pdf with all pages as full-page images.

Step 6: Completion Report

Comic Complete!
Title: [title] | Style: [style] | Pages: [count] | Aspect: [ratio] | Language: [lang]
Location: [path]
✓ analysis.md
✓ characters.png
✓ 00-cover-[slug].png ... NN-page-[slug].png
✓ {topic-slug}.pdf

Page Modification

Support for modifying individual pages after initial generation.

Edit Single Page

Regenerate a specific page with modified prompt:

  1. Identify page to edit (e.g., 03-page-enigma-machine.png)
  2. Update prompt in prompts/03-page-enigma-machine.md if needed
  3. If content changes significantly, update slug in filename
  4. Regenerate image using same session ID and aspect ratio
  5. Regenerate PDF

Add New Page

Insert a new page at specified position:

  1. Specify insertion position (e.g., after page 3)
  2. Create new prompt with appropriate slug (e.g., 04-page-bletchley-park.md)
  3. Generate new page image (same aspect ratio)
  4. Renumber files: All subsequent pages increment NN by 1

- 04-page-tragedy.png05-page-tragedy.png - Slugs remain unchanged

  1. Update storyboard.md with new page entry
  2. Regenerate PDF

Delete Page

Remove a page and renumber:

  1. Identify page to delete (e.g., 03-page-enigma-machine.png)
  2. Remove image file and prompt file
  3. Renumber files: All subsequent pages decrement NN by 1

- 04-page-tragedy.png03-page-tragedy.png - Slugs remain unchanged

  1. Update storyboard.md to remove page entry
  2. Regenerate PDF

File Naming Convention

Files use meaningful slugs for better readability:

NN-cover-[slug].png / NN-page-[slug].png
NN-cover-[slug].md / NN-page-[slug].md (in prompts/)

Examples:

  • 00-cover-turing-story.png
  • 01-page-early-life.png
  • 02-page-cambridge-years.png
  • 03-page-enigma-machine.png

Slug rules:

  • Derived from page title/content (kebab-case)
  • Must be unique within comic
  • When page content changes significantly, update slug accordingly

Renumbering:

  • After add/delete, update NN prefix for affected pages
  • Slug remains unchanged unless content changes
  • Maintain sequential numbering with no gaps

Style-Specific Guidelines

Ohmsha Style (--style ohmsha)

Additional requirements for educational manga:

  • Default: Use Doraemon characters directly - No need to create new characters

- 大雄 (Nobita): Student role, curious learner - 哆啦A梦 (Doraemon): Mentor role, explains concepts with gadgets - 胖虎 (Gian): Antagonist/challenge role, represents obstacles or misconceptions - 静香 (Shizuka): Supporting role, asks clarifying questions

  • Custom characters only if explicitly requested: --characters "Student:小明,Mentor:教授"
  • Must use visual metaphors (gadgets, action scenes) - NO talking heads
  • Page titles: narrative style, not "Page X: Topic"

Reference: references/ohmsha-guide.md for detailed guidelines.

References

Detailed templates and guidelines in references/ directory:

  • analysis-framework.md - Deep content analysis for comic adaptation
  • character-template.md - Character definition format and examples
  • storyboard-template.md - Storyboard structure and panel breakdown
  • ohmsha-guide.md - Ohmsha manga style specifics
  • styles/ - Detailed style definitions
  • layouts/ - Detailed layout definitions

Extension Support

Custom styles and configurations via EXTEND.md.

Check paths (priority order):

  1. .content-gen-skills/comic/EXTEND.md (project)
  2. ~/.content-gen-skills/comic/EXTEND.md (user)

If found, load before Step 1. Extension content overrides defaults.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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22.76%
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18.27%
按下载量换算62

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13.64%
按下载量换算46

trae

7.41%
按下载量换算25

OpenCode

3.53%
按下载量换算12

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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