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Agent Skill · 开发

ai-talking-photo(艾会说话的照片)

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。

ai-talking-photo 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,744 个 Skill 里排第 8,486。

总安装 3,143周安装 135GitHub Stars 公开资料未说明下载 1,102核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:ai-talking-photo(艾会说话的照片)
来源仓库:https://github.com/whitejohnk-26/ai-talking-photo
安装命令:
openclaw skills install ai-talking-photo
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 ai-talking-photo

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

让静态照片栩栩如生,同步面部动画与音频。适合让肖像说话、唱歌或旁白的视觉内容创作者。支持上传面部照片并生成动态效果。ai-talking-photo 属于开发类 Skill,可作为该场景下的辅助能力补充。安装前请确认权限范围和维护状态,注意是否涉及联网或文件读写。

建议结合原始 README 和来源仓库进一步核验具体用法。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install ai-talking-photo;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ai-talking-photo 怎么安装?

在终端执行 openclaw skills install ai-talking-photo。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-talking-photo 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ai-talking-photo 需要什么权限?

权限级别是需要联网。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。

ai-talking-photo 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ai-talking-photo 用的人多吗?

公开数据是累计安装 3,143 次,近一周安装 135 次,下载 1,102 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ai-talking-photo 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ai-talking-photo

艾会说话的照片 · 本页

3,143—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
ai-talking-photo
version
1.0.0
displayName
AI Talking Photo — Animate Still Images Into Lifelike Speaking Portraits
description
>
metadata
{"openclaw": {"emoji": "🗣️", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "short_prompts"}}

Getting Started

Welcome! With AI Talking Photo, you can turn any portrait into a speaking, animated video in moments. Upload your photo and audio clip (or tell me what you'd like the subject to say) and let's bring it to life!

Try saying:

  • "Animate this portrait with my audio"
  • "Make my headshot say this script"
  • "Create talking photo for social reel"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

  • Generate a UUID as client identifier
  • POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
  • Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Make Any Photo Speak With One Upload

Static photos hold stories that never get told. AI Talking Photo changes that by turning a single still image into an animated, speaking portrait — no camera, no studio, no video shoot required. Whether it's a historical figure, a product mascot, a family member, or your own headshot, this skill breathes voice and movement into the image in seconds.

The process is straightforward: provide a clear face photo and either an audio file or a text script you want spoken. The skill analyzes the facial geometry, maps lip movements to the audio waveform, and generates a short video where the subject appears to genuinely speak. Subtle head motion, eye blinks, and micro-expressions are layered in to avoid the uncanny stiffness of early deepfake tools.

Creators use this for memorial tribute videos, branded spokesperson content, educational history lessons, social media reels, and interactive storytelling. If you can photograph a face, you can give it a voice — that's the core promise of AI Talking Photo.

Routing Animate Portrait Requests

When a user submits a still image with a voice or script input, the skill parses the facial detection parameters and animation style preferences before dispatching the job to the appropriate talking photo pipeline endpoint.

User says...ActionSkip SSE?
"export" / "导出" / "download" / "send me the video"→ §3.5 Export✅
"credits" / "积分" / "balance" / "余额"→ §3.3 Credits✅
"status" / "状态" / "show tracks"→ §3.4 State✅
"upload" / "上传" / user sends file→ §3.2 Upload✅
Everything else (generate, edit, add BGM…)→ §3.1 SSE❌

Talking Photo API Reference

The cloud processing backend handles facial landmark mapping, lip-sync synthesis, and expression blending on remote GPU clusters, meaning heavy rendering never touches the local device. Completed animated portrait outputs are returned as video streams or downloadable clips once the synthesis job finalizes.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: ai-talking-photo
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend saysYou do
"click [button]" / "点击"Execute via API
"open [panel]" / "打开"Query session state
"drag/drop" / "拖拽"Send edit via SSE
"preview in timeline"Show track summary
"Export button" / "导出"Execute export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Best Practices

The quality of your output is almost entirely determined by the quality of your input photo. Avoid images with heavy filters, strong side-lighting, or partial face occlusion — these confuse the facial landmark detection and produce jittery or misaligned lip movements. A neutral expression in the source photo gives the animation engine the most flexibility to map a wide range of speech sounds accurately.

Keep audio clips clean and free of background music during the lip-sync generation phase. If your final video needs music, add it as a separate layer after the talking photo is rendered. This prevents the model from misreading musical frequencies as speech phonemes.

For emotional impact — especially in memorial or tribute videos — choose audio that is paced naturally and not too fast. Rapid speech compresses lip movements and reduces the realism of the animation. A speaking rate of 120–150 words per minute tends to yield the most convincing results. Finally, always review the generated video before publishing; small manual trims at the start and end of the clip can remove any initialization frames where the face hasn't yet settled into the animation.

Integration Guide

Getting started with AI Talking Photo requires just two inputs: a face image and an audio source. For best results, use a front-facing photo where the subject's mouth and eyes are clearly visible, unobstructed by hands, masks, or extreme angles. JPEG and PNG formats work well; aim for at least 512×512 pixels to preserve animation quality.

For audio, you can supply an MP3, WAV, or M4A file up to 60 seconds, or simply paste a text script and select a voice style — the skill will synthesize the speech internally before animating the photo. If you're embedding the output in a website or presentation, request the export in MP4 format with a transparent-background option for overlay use.

When building workflows — such as auto-generating spokesperson videos from a CMS or producing personalized video messages at scale — pass the image URL and script text as variables. The skill returns a video URL or file you can route directly into your delivery pipeline, email platform, or social scheduler.

平台分布

OpenClaw

86.06%
按下载量换算948

安全审计

VirusTotal

通过

ClawScan

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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