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Chuanzhangzhenren Prompts

Community
zhangxiansheng-888
chuanzhangzhenren-prompts

Generate realistic AI portrait photography prompts by translating photo references, portrait ideas, photography judgments, or style articles into executable shooting-brief prompts. Use when the user asks for 真人写真提示词, AI人像写真, 上传真人照片生成写真, 保留本人长相, 脸部细节, 五官锚点, 妆容, 滤镜, 质感词, 摄影感prompt, 焦段/光圈/景深/光线/色调怎么写进人像prompt, 单人写真, 情侣写真, 九宫格写真, 职业写真, 头像, 生活方式人像, 人像变量库, 填空模板, prompt for realistic portrait photos, or a reusable portrait prompt framework. Do not use for non-human illustration, product/scene/poster prompts without real-person portrait intent, video prompts, storyboards, or purely illustrated characters.

Overview

Publisherzhangxiansheng-888
RepositorychuanzhangAIshijie
Skill namechuanzhangzhenren-prompts
Stars
73
Forks
11
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhangxiansheng-888 on GitHub. Read the source before you install it.

Installation

Install the Chuanzhangzhenren Prompts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zhangxiansheng-888/chuanzhangAIshijie/tree/main/skills/chuanzhangzhenren-prompts
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhangxiansheng-888/chuanzhangAIshijie.git /tmp/chuanzhangAIshijie
mkdir -p .claude/skills
cp -r /tmp/chuanzhangAIshijie/skills/chuanzhangzhenren-prompts .claude/skills/chuanzhangzhenren-prompts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chuanzhangzhenren Prompts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chuanzhangzhenren Prompts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chuanzhangzhenren Prompts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

船长AI视界 真人感人像提示词

品牌署名

需要署名、标注来源或展示品牌时,只写:

船长AI视界

不要输出其他作者、品牌署名或旧名称。

Create model-agnostic prompts for realistic AI portrait photography. Treat each prompt as a compact shooting brief, not a pile of style words.

The skill has two jobs:

  1. Preserve real-person identity when a photo is provided.
  2. Translate photography intent into prompt language: purpose, subject temperament, lens distance, camera angle, depth of field, light, background information, tone, realism details, and constraints.
  3. Read a user's rough idea, article, reference style, or failed prompt into a variable map, then choose only the variables needed for the final image.

Core Rule

Lead with identity control:

text
请只把上传照片作为人物身份参考,用于保留本人真实长相。不要保留原照片里的背景、衣服、道具、坐姿、拍摄环境和原始构图。请重新生成一张全新的写真。

Then build the portrait with controlled modules:

  1. Image purpose: avatar, professional portrait, lifestyle portrait, magazine editorial, commercial beauty, documentary, cinematic night portrait.
  2. Identity anchors: face shape, facial proportion, brows and eyes, nose, lips, skin tone, age feel, hairline, base hairstyle, overall temperament.
  3. Subject temperament: relaxed, calm, professional, gentle, detached, mature, candid, confident.
  4. Face detail: face shape, facial proportion, brow-eye structure, eyes, nose, lips, skin tone, age feel, hairline, texture, and what must not be beautified away.
  5. Makeup and filter: base makeup, brow, eye makeup, blush area, lip color, retouching level, diffusion, grain, tone, glow, and highlight behavior.
  6. Camera language: crop, camera angle, lens look, aperture feel, background information.
  7. Light and tone: light direction, softness, catchlights, color style, film/editorial anchor.
  8. Scene rebuild: new background, wardrobe, props, hairstyle, memory point.
  9. Expression rhythm: gaze, micro-expression, captured moment, mild asymmetry.
  10. Relationship and body control for couples/contact: left/right, foreground/background, hand ownership, contact point, realistic anatomy.
  11. Negative constraints: avoid stranger face, influencer face, over-beautification, westernization, AI plastic skin, excessive blur, broken hands, fused faces.

Workflow

  1. Identify the task type: single portrait, portrait set, 3x3 grid, couple portrait, detail close-up, or prompt framework.
  2. If a real photo is referenced or attached, start with the identity-reference sentence. If no photo is provided, state that identity preservation requires a reference photo and continue with a generic portrait prompt only if useful.
  3. Ask at most one question when the missing information changes the result materially: subject count, desired style, or output format. Otherwise make a reasonable choice and proceed.
  4. Build the prompt in this order: 身份参考(如有) -> 图像用途 -> 身份锚点 -> 脸部细节 -> 主体气质 -> 妆容体系 -> 滤镜质感 -> 构图机位 -> 镜头焦段感 -> 景深背景 -> 光线设计 -> 场景服装发型 -> 真实感细节 -> 人体空间 -> 负面约束
  5. For users who know photography terms but cannot write prompts, translate their choices into visible effects. Example: "85mm" becomes "85mm portrait lens look, natural facial proportions, clean background separation"; "f/2.8" becomes "moderate shallow depth of field, face sharp, background still recognizable".
  6. For a set, write a shot table before the final prompt. Each shot must differ in angle, pose, gaze, crop, and emotional moment.
  7. For couples or physical contact, explicitly assign left/right, front/back, hand ownership, gaze direction, and contact point.
  8. If the user asks for variables, texture, SOP, article distillation, or "更全面", first extract a variable map, then output the prompt. Read references/variables-and-texture.md.
  9. If the user asks about face detail, makeup, filter, identity similarity, "不像本人", "AI脸", "网红脸", "朦胧滤镜", "胶片滤镜", or a style like campus/Korean/professional/beauty/night, read references/face-makeup-filter.md.
  10. Output a copy-ready Chinese prompt by default. Add an English prompt only when the user asks for bilingual output or a tool expects English.

Output Shapes

Single Portrait

markdown
**真人写真提示词**
[copy-ready prompt]

**这版控制的重点**
[1-3 short bullets: identity, style, body/contact, or set rhythm]

Portrait Set

markdown
**镜头表**
01 [angle + pose + gaze + emotion]
02 ...

**整组统一提示词**
[global prompt]

**单张补充提示词**
01 [shot-specific prompt]
02 ...

Framework

When the user asks for a reusable skill, template, SOP, or method, output:

  1. Minimum reusable prompt skeleton.
  2. Fill-in variables.
  3. Negative prompt block.
  4. Example for one concrete style.
  5. Common failure fixes.

Variable Map

When the user asks how to use an article, reference style, variables, texture words, or a failed prompt:

markdown
**读取结果**
[purpose / subject / scene / lens / light / tone / texture / constraints]

**可选变量**
[short grouped variables, not every possible word]

**推荐组合**
[one best combination for this task]

**真人写真提示词**
[copy-ready prompt]

Style Guidance

Use concrete photographic language instead of empty tags.

  • Weak: 韩系, 高级感, 胶片感, 自然光.
  • Better: 浅色摄影棚, 柔和窗边漫射光, 50mm 自然视角, 半身近景, 皮肤保留真实纹理, 米白针织衫和浅粉发夹.
  • Weak: 电影感, 背景虚化, 真实感.
  • Better: 夜景街边半身人像, 85mm portrait lens look, f/1.8 feel, mixed neon and tungsten practical lights, restrained glow, realistic catchlights, optical bokeh, natural highlight roll-off.
  • Weak: 亲密情侣合照.
  • Better: 女生从男生肩后探出脸看向镜头, 右手轻轻搭在男生右肩, 男生低头轻笑看向画面左下方, 像刚被她逗笑的一瞬间.

Failure Fixes

  • If the result looks like the original photo: strengthen "do not retain original background, outfit, props, pose, composition".
  • If the result is pretty but not like the person: expand identity anchors and reduce hairstyle, makeup, face-shape changes.
  • If every image repeats: create a shot table with distinct crop, angle, gaze, pose, and emotion.
  • If couples feel empty: write the emotional action, not only the relationship label.
  • If contact breaks anatomy: specify left/right, hand ownership, contact point, body proportion, and natural overlap.
  • If the image feels AI-made: reduce generic beauty words, add imperfect captured-moment language, mild asymmetry, natural skin texture, and non-studio emotional timing.
  • If the image is pretty but directionless: add image purpose first, then choose one lens look, one aperture feel, one lighting setup, and one tone anchor.
  • If blur or film effects look fake: replace vague "朦胧/胶片/虚化" with moderate shallow depth of field, optical bokeh, subtle grain, restrained halation, natural highlight roll-off.

Boundaries

  • Do not create video prompts, camera movement over time, storyboards, timestamps, or sound cues.
  • Do not use this for generic product, poster, scene, concept art, illustration, or non-human character prompts. Use the general still-image prompt skill instead.
  • Do not promise exact identity preservation; phrase it as prompt control.
  • If the subject is or appears to be under 18, keep the portrait non-sexual, age-appropriate, and everyday/editorial.

Optional Reference

For complex sets, couples, grids, or when the user asks for the full method, read references/portrait-planning.md.

For photography-language conversion, style presets, lens/aperture/light/tone choices, or when the user says they know the image but cannot write the prompt, read references/photography-brief.md.

For article reading, prompt-writing modes, variable libraries, texture vocabulary, style recipes, and completeness checks, read references/variables-and-texture.md.

For face anchors, facial detail, makeup systems, filter recipes, retouching levels, and identity-preservation constraints, read references/face-makeup-filter.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Chuanzhangzhenren Prompts AI skill do?

Generate realistic AI portrait photography prompts by translating photo references, portrait ideas, photography judgments, or style articles into executable shooting-brief prompts. Use when the user asks for 真人写真提示词, AI人像写真, 上传真人照片生成写真, 保留本人长相, 脸部细节, 五官锚点, 妆容, 滤镜, 质感词, 摄影感prompt, 焦段/光圈/景深/光线/色调怎么写进人像prompt, 单人写真, 情侣写真, 九宫格写真, 职业写真, 头像, 生活方式人像, 人像变量库, 填空模板, prompt for realistic portrait photos, or a reusable portrait prompt framework. Do not use for non-human illustration, product/scene/poster prompts without real-person portrait intent, video prompts, storyboards, or purely illustrated char...

Why use Chuanzhangzhenren Prompts on TypingMind?

Because you install it once and use it with any model. Chuanzhangzhenren Prompts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chuanzhangzhenren Prompts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhangxiansheng-888/chuanzhangAIshijie/tree/main/skills/chuanzhangzhenren-prompts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Chuanzhangzhenren Prompts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chuanzhangzhenren Prompts?

As many as you like. As long as a model supports skills, you can use Chuanzhangzhenren Prompts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chuanzhangzhenren Prompts AI skill free?

Yes. It is published on GitHub by zhangxiansheng-888 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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