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muapi-photo-pack-skillmuapi 照片包技能

Agent Skill

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

总安装

8,726

周安装

371

GitHub Stars

公开资料未说明

下载量

3,057
OpenClaw

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-0

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:muapi-photo-pack-skill(muapi 照片包技能)
来源仓库:https://github.com/anil-matcha/muapi-photo-pack-skill
安装命令:
openclaw skills install muapi-photo-pack-skill
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install muapi-photo-pack-skill

简介

muapi-photo-pack-skill 用于基于参考图像批量生成风格一致的专业照片,保留人物特征。

  • 适用于广告拍摄、内容创作或视觉素材快速迭代等效率提升场景。
  • 通过 OpenClaw 调用图像生成模型,支持自定义背景与构图调整。
  • 使用时需确保输入图片版权合法,输出结果不侵犯第三方肖像权。
  • 建议限制生成数量与分辨率,避免超出模型配额或产生额外费用。

SKILL.md

slug
muapi-photo-pack-skill
name
muapi-photo-pack-generator
version
0.1.0
description
Generate a pack of professional or aesthetic photos from a single reference image while preserving the exact identity of the person.
acceptLicenseTerms
true

📸 Photo Pack Generator Expert Skill (Identity-Lock Edition)

Transform a single reference photo into a collection of themed images while maintaining extremely high facial identity fidelity.

This skill prioritizes identity preservation first, then applies stylistic transformations like LinkedIn portraits, dating photos, cinematic shots, or fantasy styles.

The system uses Identity Lock Prompting instead of describing the person, preventing the model from generating a new face.


Core Principles

1️⃣ Identity Lock (MOST IMPORTANT)

The generated images must always depict the same person from the reference image.

All prompts MUST include identity lock instructions.

Required identity rules:

  • Preserve the exact facial identity from the reference image
  • Do not modify eye shape or spacing
  • Do not modify nose structure
  • Do not modify jawline or chin shape
  • Do not modify cheekbones
  • Do not modify face proportions
  • Identity must remain identical to the reference photo

2️⃣ Vision-First Scene Analysis

The agent MUST analyze the reference image before generation.

However the analysis must NOT describe the person (age, ethnicity, hair etc).

Allowed analysis fields:

  • head orientation
  • facial angle
  • expression
  • lighting direction
  • framing (portrait / half body / full body)

Example:

Head orientation: slight left tilt Expression: neutral friendly Lighting: soft frontal light Framing: head and shoulders portrait


Agent Execution Flow

Step 1 — Grounding Check

Ensure the user has provided a reference image.

Supported inputs:

  • local image
  • URL
  • uploaded file

Step 2 — Vision Analysis

Extract scene attributes only.

DO NOT describe:

  • age
  • ethnicity
  • beard
  • hair
  • body type

Identity must come directly from the image.


Step 3 — Category Selection

If the user does not specify a category suggest:

  • LinkedIn
  • Tinder
  • OldMoney

Step 4 — Prompt Construction

Use the reference image as the identity source.

Preserve the exact facial identity from the reference image.

Identity must remain identical to the reference photo.

Do not change:

  • eye shape
  • eye spacing
  • nose structure
  • jawline
  • cheekbones
  • face proportions

Maintain similar head orientation as the reference.

Scene example:

Outdoor café portrait Soft natural daylight 35mm portrait lens Shallow depth of field Photorealistic skin texture


Step 5 — Negative Prompt

Always include:

different person altered face changed facial features new identity generic face beautified face plastic skin face distortion


Step 6 — Execution

Example:

bash scripts/generate-pack.sh \ --image "./my_face.jpg" \ --category "LinkedIn" \ --identity-lock true \ --num 5


Supported Categories

CategoryBest ForAesthetic
LinkedInProfessionalStudio
CEOFoundersOffice
TinderDatingLifestyle
OldMoneyLuxuryEstate
CyberpunkFantasyNeon
FitnessGymAthletic
TravelSocialBali/Paris
90sRetroVintage
HolidaySeasonalFestive

Guardrails

Fidelity First

Identity preservation is always more important than style.

Never Re-Describe the Person

Avoid prompts like:

"Indian man in his 20s with short hair"

This causes the model to generate a new face.

Identity must come from the reference image only.


Recommended Models

Best results with:

  • nano-banana-edit

Result

This system produces:

  • consistent identity
  • photorealistic images
  • multi-style photo packs
  • professional outputs

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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

能力 5

展示第三方安全扫描或审计结果

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

OpenClaw

73.53%
按下载量换算2,248

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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