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ivangdavila

Face

by Iván · GitHub ↗ · v1.0.0 · MIT-0
linuxdarwinwin32 ✓ Security Clean
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Install in OpenClaw
/install face
Description
Put a human face on your agent with fast avatar shortlists, gender control, transparent options, and reusable profile links. Use when the user wants an avata...
README (SKILL.md)

Put a Face on Your Agent

Use this skill to give an agent, persona, group, or Telegram surface a real human-looking face fast.

The job is simple:

  • open a stable face listing URL
  • extract a small shortlist of faces
  • send the options to the user to choose
  • if one is approved, reuse that link as the avatar or suggest saving it in identity

Core Workflow

1. Start from the stable URL

Default to:

https://generated.photos/faces/beautified/{gender}/young-adult

Notes:

  • beautified and young-adult are the best default for this use case
  • gender is the main parameter that usually matters
  • the site may also accept .../young-adult/{gender}, but the canonical form resolves to .../{gender}/young-adult

2. Only trust filters the site already exposes

From the page HTML, the route families that were verified as stable are:

  • head pose: front-facing, left-facing, right-facing
  • race: asian-race, black-race, latino-race, white-race
  • eye color: brown-eyes, grey-eyes, blue-eyes, green-eyes
  • hair color: brown-hair, black-hair, blond-hair
  • hair length: short, medium, long
  • emotion: joy, neutral

Do not invent deeper URL combinations from memory. If the user wants narrower filtering, open the base page and follow the exact links that page emits.

3. Extract faces from HTML before using a browser

The page already contains:

  • /face/... detail links
  • data-image-id
  • https://images.generated.photos/... preview URLs

Use a direct fetch first. Return 3 to 6 options max.

python3 - \x3C\x3C'PY'
import json
import re
from urllib.request import Request, urlopen

url = "https://generated.photos/faces/beautified/female/young-adult"
raw = urlopen(Request(url, headers={"User-Agent": "Mozilla/5.0"})).read().decode()

cards = re.findall(
    r'href="(/face/[^"]+)".*?data-image-id="([^"]+)".*?src="(https://images.generated.photos[^"]+)"',
    raw,
    re.S,
)
transparent = {
    image_id: thumb.replace("\\u002F", "/")
    for image_id, thumb in re.findall(
        r'id:"([^"]+)".*?transparent:\{thumb_url:"([^"]+)"',
        raw,
    )
}

for n, (detail, image_id, thumb) in enumerate(cards[:6], 1):
    print(json.dumps({
        "n": n,
        "detail": "https://generated.photos" + detail,
        "thumb": thumb,
        "transparent": transparent.get(image_id),
    }))
PY

4. Treat background as extraction, not routing

Do not model background as a clean route parameter.

What was verified:

  • the page UI shows a background area
  • the route itself does not expose a simple stable public background parameter
  • transparent versions appear in embedded data as transparent.thumb_url

Rule:

  • if the user wants a transparent avatar, prefer transparent.thumb_url
  • if the user wants a bigger preview, open the chosen detail page and use the 512px og:image or twitter:image

5. Always keep the user in the loop

Default output should be:

  • 3 to 6 numbered options
  • one normal preview per option
  • transparent preview too when available
  • the detail page link

Do not auto-pick unless the user asks.

6. Reuse the chosen face properly

Once the user chooses one:

  • propose using it as the avatar for the agent, group, Telegram profile, or similar
  • if it is their long-term face, suggest storing the chosen link in identity
  • keep the detail page link, not just the image preview URL

Recommended Response Shape

When you answer, keep it compact:

I found 4 options for your agent face.

1. Option A
   Detail: ...
   Preview: ...
   Transparent: ...

2. Option B
   Detail: ...
   Preview: ...
   Transparent: ...

Tell me which one you want and I’ll prepare it as the avatar.

Common Mistakes

  • guessing URL variants instead of following the page's real filters
  • trying to control background through the route
  • showing too many faces at once
  • saving only the preview URL instead of the detail link
  • changing an avatar without explicit approval

External Endpoints

Endpoint Data Sent Purpose
https://generated.photos Filter path segments and face detail page requests Find faces and open detail pages
https://images.generated.photos Image GET requests only Preview and transparent preview retrieval

No other data is sent externally.

Usage Guidance
This skill scrapes generated.photos web pages and images.urls to build avatar shortlists; it does not request credentials. Before installing, consider: (1) generated.photos' terms of service and whether scraping is permitted or whether you should use their official API; (2) privacy and ethical concerns when using human-like faces for agent identities; (3) the skill performs network GETs so ensure your environment permits outbound requests; and (4) the metadata mentions 'curl' but the runtime snippet uses python3 — confirm that python3 is available and network access is allowed. If you need stricter guarantees, ask the author to use an official API or include explicit rate-limiting and TOS compliance instructions.
Capability Analysis
Type: OpenClaw Skill Name: face Version: 1.0.0 The 'Face' skill is a legitimate tool designed to help users find and select AI-generated avatars from generated.photos. The included Python script in SKILL.md is a straightforward web scraper that extracts image URLs and metadata using standard libraries, and the instructions emphasize keeping the user in the loop and obtaining explicit approval before making changes.
Capability Assessment
Purpose & Capability
The skill's purpose (fetching avatar shortlists from generated.photos) matches the instructions and required tools. Minor mismatch: the metadata lists 'curl' as a required binary but the SKILL.md uses python3/urllib for fetching; curl appears unnecessary but not dangerous.
Instruction Scope
SKILL.md confines actions to fetching HTML and images from generated.photos and images.generated.photos, extracting preview/transparent URLs, and presenting 3–6 options to the user. It does not instruct reading local files, other environment variables, or sending data to unexpected endpoints. It explicitly warns not to auto-pick without user approval.
Install Mechanism
This is an instruction-only skill with no install spec or code files, so nothing is written to disk. That is proportionate for the task.
Credentials
No credentials or environment variables are requested (appropriate). The only minor oddity is the declared dependency on 'curl' despite the provided code using python3 for network requests.
Persistence & Privilege
The skill is not 'always' enabled and does not request any persistent system-wide privileges or configuration changes. It recommends storing chosen links in identity but does not require writing them automatically.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install face
  3. After installation, invoke the skill by name or use /face
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Refines the positioning around putting a human face on the agent with faster avatar selection.
Metadata
Slug face
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Face?

Put a human face on your agent with fast avatar shortlists, gender control, transparent options, and reusable profile links. Use when the user wants an avata... It is an AI Agent Skill for Claude Code / OpenClaw, with 302 downloads so far.

How do I install Face?

Run "/install face" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Face free?

Yes, Face is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Face support?

Face is cross-platform and runs anywhere OpenClaw / Claude Code is available (linux, darwin, win32).

Who created Face?

It is built and maintained by Iván (@ivangdavila); the current version is v1.0.0.

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