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Agent Skill · 开发

didit-biometric-age-estimation(迪迪特生物年龄估计)

didit-biometric-age-estimation 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。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。

didit-biometric-age-estimation 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 4,194。

总安装 8,808周安装 367GitHub Stars 公开资料未说明下载 2,936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:didit-biometric-age-estimation(迪迪特生物年龄估计)
来源仓库:https://github.com/rosasalberto/didit-biometric-age-estimation
安装命令:
openclaw skills install didit-biometric-age-estimation
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 didit-biometric-age-estimation

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。

通过面部图像估算个体年龄的生物特征识别技术服务。适用于年龄门禁控制、未成年人保护或身份验证增强场景。输入图片 URL 或 Base64 编码即可获得置信区间内的年龄预测值。依赖 Didit 独立 API,准确率受光照、姿态与遮挡因素影响。禁止用于种族歧视或非法监控目的,须符合当地法律法规要求。

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 didit-biometric-age-estimation;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

    它需要接触敏感数据,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

didit-biometric-age-estimation 怎么安装?

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

didit-biometric-age-estimation 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。

didit-biometric-age-estimation 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

didit-biometric-age-estimation 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。

didit-biometric-age-estimation 用的人多吗?

公开数据是累计安装 8,808 次,近一周安装 367 次,下载 2,936 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。

didit-biometric-age-estimation 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
didit-biometric-age-estimation

迪迪特生物年龄估计 · 本页

8,808—接触敏感数据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
didit-biometric-age-estimation
description
>
version
1.0.0
metadata
openclaw
requires
env
primaryEnv
DIDIT_API_KEY
emoji
🎂
homepage
https://docs.didit.me

Didit Age Estimation API

Overview

Estimates a person's age from a facial image using deep learning. Also performs a passive liveness check to prevent spoofing.

Key constraints:

  • Supported formats: JPEG, PNG, WebP, TIFF
  • Maximum file size: 5MB
  • Image must contain one clearly visible face
  • Accuracy: MAE ±3.5 years overall; ±1.5 years for under-18

Capabilities: Age estimation with confidence scoring, gender estimation, passive liveness detection, configurable age thresholds, per-country age restrictions, adaptive mode with ID verification fallback for borderline cases.

Liveness methods (workflow mode):

MethodSecurityBest For
ACTIVE_3D (Action + Flash)HighestBanking, government, healthcare
FLASHING (3D Flash)HighFinancial services, identity verification
PASSIVE (single-frame CNN)StandardLow-friction consumer apps

API Reference: https://docs.didit.me/standalone-apis/age-estimation Feature Guide: https://docs.didit.me/core-technology/age-estimation/overview


Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks, or via programmatic registration (see below).

Getting Started (No Account Yet?)

If you don't have a Didit API key, create one in 2 API calls:

  1. Register: POST https://apx.didit.me/auth/v2/programmatic/register/ with {"email": "you@gmail.com", "password": "MyStr0ng!Pass"}
  2. Check email for a 6-character OTP code
  3. Verify: POST https://apx.didit.me/auth/v2/programmatic/verify-email/ with {"email": "you@gmail.com", "code": "A3K9F2"} → response includes api_key

To add credits: GET /v3/billing/balance/ to check, POST /v3/billing/top-up/ with {"amount_in_dollars": 50} for a Stripe checkout link.

See the didit-verification-management skill for full platform management (workflows, sessions, users, billing).


Endpoint

POST https://verification.didit.me/v3/age-estimation/

Headers

HeaderValueRequired
x-api-keyYour API keyYes
Content-Typemultipart/form-dataYes

Request Parameters (multipart/form-data)

ParameterTypeRequiredDefaultDescription
user_imagefileYes—Facial image (JPEG/PNG/WebP/TIFF, max 5MB)
rotate_imagebooleanNofalseTry 0/90/180/270 rotations for non-upright faces
save_api_requestbooleanNotrueSave in Business Console Manual Checks
vendor_datastringNo—Your identifier for session tracking

Example

import requests

response = requests.post(
    "https://verification.didit.me/v3/age-estimation/",
    headers={"x-api-key": "YOUR_API_KEY"},
    files={"user_image": ("selfie.jpg", open("selfie.jpg", "rb"), "image/jpeg")},
    data={"vendor_data": "user-123"},
)
print(response.json())
const formData = new FormData();
formData.append("user_image", selfieFile);

const response = await fetch("https://verification.didit.me/v3/age-estimation/", {
  method: "POST",
  headers: { "x-api-key": "YOUR_API_KEY" },
  body: formData,
});

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "liveness": {
    "status": "Approved",
    "method": "PASSIVE",
    "score": 89.92,
    "age_estimation": 24.3,
    "reference_image": "https://example.com/reference.jpg",
    "video_url": null,
    "warnings": []
  },
  "created_at": "2025-05-01T13:11:07.977806Z"
}

Status Values & Handling

StatusMeaningAction
"Approved"Age verified above threshold, liveness passedProceed with your flow
"Declined"Age below minimum or liveness failedCheck warnings for specifics
"In Review"Borderline case, needs reviewTrigger ID verification fallback or manual review

Error Responses

CodeMeaningAction
400Invalid requestCheck file format, size, parameters
401Invalid API keyVerify x-api-key header
403Insufficient creditsTop up at business.didit.me

Response Field Reference

FieldTypeDescription
statusstring"Approved", "Declined", "In Review", "Not Finished"
methodstring"ACTIVE_3D", "FLASHING", or "PASSIVE"
scorefloat0-100 liveness confidence score
age_estimationfloatEstimated age in years (e.g. 24.3). null if no face
reference_imagestringTemporary URL (expires 60 min)
video_urlstringTemporary URL for active liveness video. null for passive
warningsarray{risk, log_type, short_description, long_description}

Accuracy by Age Range

Age RangeMAE (years)Confidence
Under 181.5High
18-252.8High
26-403.2High
41-603.9Medium-High
60+4.5Medium

Warning Tags

Auto-Decline

TagDescription
NO_FACE_DETECTEDNo face found in image
LIVENESS_FACE_ATTACKSpoofing attempt detected
FACE_IN_BLOCKLISTFace matches a blocklist entry

Configurable (Decline / Review / Approve)

TagDescription
AGE_BELOW_MINIMUMEstimated age below configured minimum
AGE_NOT_DETECTEDUnable to estimate age (image quality, lighting)
LOW_LIVENESS_SCORELiveness score below threshold
POSSIBLE_DUPLICATED_FACESignificant similarity with previously verified face

Warning severity: error (→ Declined), warning (→ In Review), information (no effect).


Common Workflows

Basic Age Gate

1. Capture user selfie
2. POST /v3/age-estimation/ → {"user_image": selfie}
3. Check liveness.age_estimation >= your_minimum_age
4. If "Approved" → user meets age requirement
   If "Declined" → check warnings for AGE_BELOW_MINIMUM or liveness failure

Adaptive Age Estimation (Workflow Mode)

Uses workflow_type: "adaptive_age_verification" — creates a session where borderline ages trigger automatic ID verification fallback.

1. POST /v3/workflows/ → {"workflow_type": "adaptive_age_verification", "is_liveness_enabled": true, "is_age_restrictions_enabled": true}
2. POST /v3/session/ → create session with the workflow_id from step 1
3. User takes selfie → system estimates age
4. Clear pass (well above threshold) → Approved instantly
   Clear fail (well below threshold) → Declined
   Borderline case → automatic ID verification fallback
5. If ID fallback triggered: per-country age restrictions apply

Per-Country Age Restrictions

Configure in Console per issuing country:

CountryMin AgeOverrides
USA18Mississippi: 21, Alabama: 19
KOR19—
GBR18—
ARE21—
Use "Apply age of majority" button in Console to auto-populate defaults.

Utility Scripts

estimate_age.py: Estimate age from a facial image via the command line.

# Requires: pip install requests
export DIDIT_API_KEY="your_api_key"
python scripts/estimate_age.py selfie.jpg
python scripts/estimate_age.py photo.png --threshold 21 --vendor-data user-123

平台分布

OpenClaw

93.67%
按下载量换算2,750

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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