#601

Globales Ranking · von 601 Skills

avatar-portrait AI Agent Skill

Quellcode ansehen: b-open-io/gemskills

Safe

Installation

npx skills add b-open-io/gemskills --skill avatar-portrait

24

Installationen

Avatar Portrait

Generate stylized avatar portraits from reference photos using Gemini image generation, preserving recognizable likeness while applying a requested visual style.

When to Use

Use this skill when the user asks to:

  • Create avatars from photos
  • Generate team member profile images in a specific style
  • Convert headshots to stylized portraits (pixel, retro, illustrated, modern)
  • Create consistent character portraits that resemble real people

Core Principles

Balance Likeness and Style

The critical challenge is achieving BOTH:

  1. Recognizable likeness - Must look like the source person
  2. Chosen style fidelity - Clear style, not photorealistic unless requested

Common failures:

  • Too photorealistic = ignores requested style
  • Too stylized = loses resemblance to source person

Single Source Input

Use ONLY the subject's photo as input. Never use another person's image as a style reference - this causes face blending and loss of likeness.

Prompt Template

Generate a styled avatar portrait from the reference photo.

## INPUT IMAGE
[path to subject's photo] - USE ONLY THIS IMAGE

## CRITICAL: LIKENESS PRESERVATION
Capture the subject's EXACT features from the photo:
- Face shape and jawline
- Eye shape, spacing, and expression
- Nose shape and size
- Mouth/smile characteristics
- Hair texture, color, and style
- Facial hair pattern and coverage (if applicable)
The result must be RECOGNIZABLE as this specific person.

## STYLE REQUIREMENTS
- Style: [style requested by user, e.g. pixel art, painterly, anime, low-poly]
- Keep style cues consistent across linework, shading, and color treatment
- Stylized but maintains individual features
- NOT photorealistic unless explicitly requested

## APPEARANCE
- [Describe clothing from photo or as specified]
- [Describe expression]
- [Describe any accessories]

## BACKGROUND
- [Describe background style and colors]
- Background style should match the character style

## TECHNICAL
- NO TEXT on the image
- 512x512 output
- Portrait orientation, head and shoulders
- Square format

## OUTPUT
Save to: [output path]

Example: Individual Avatar

For a team member named Dan with a reference photo showing short dark hair, beard, black v-neck:

Generate a styled avatar portrait from the reference photo.

## INPUT IMAGE
/path/to/dan-photo.png - USE ONLY THIS IMAGE

## CRITICAL: LIKENESS PRESERVATION
Capture Dan's EXACT features from the photo:
- His specific face shape and jawline
- His eye shape and expression
- His nose shape
- His smile characteristics
- Short dark hair - exact texture and style from photo
- Full dark beard - exact pattern and coverage
The result must be RECOGNIZABLE as Dan.

## STYLE REQUIREMENTS
- Style: retro 16-bit pixel art
- Visible pixels but NOT a pixelated photo filter
- Clean lines, rich colors, consistent shading
- Stylized but maintains individual features

## APPEARANCE
- Black v-neck shirt
- Warm smile showing teeth
- Friendly, approachable expression

## BACKGROUND
- California sunset cityscape
- Warm oranges, pinks, teals
- Pixel art style matching the character

## TECHNICAL
- NO TEXT on the image
- 512x512 output
- Portrait orientation, head and shoulders

## OUTPUT
Save to: /path/to/output/dan-pixel.png

Context Discipline

Do not read generated avatar images back into context. The script outputs only the file path. Ask the user to visually inspect the result and provide feedback for iteration. To inspect programmatically, optimize the image first (via the optimize-images skill).

Iteration Workflow

  1. First attempt: Generate with detailed prompt
  2. Review: Check both likeness AND style
  3. Adjust if needed:
    • If too photorealistic: Emphasize "stylized pixel art character"
    • If likeness lost: Strengthen feature descriptions from source
    • If wrong features: Be more specific about what to capture

Common Issues

Face doesn't match source

  • Ensure ONLY the subject's photo is used as input
  • Add more specific feature descriptions
  • Reference exact details visible in the photo

Too photorealistic

  • Emphasize the style family and key style cues
  • Request "stylized" and "illustrated feel"
  • Add explicit anti-photorealism language unless photorealism was requested

Too cartoonish / loses likeness

  • Strengthen the likeness preservation section
  • List specific features to capture
  • Emphasize "RECOGNIZABLE as this person"

Reference Files

For background style references:

  • references/background-styles.md - Common background approaches for avatars

Installationen

Installationen 24
Globales Ranking #601 von 601

Sicherheitsprüfung

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Warnungen: 0 Bewertung: 90
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So verwenden Sie diesen Skill

1

Install avatar-portrait by running npx skills add b-open-io/gemskills --skill avatar-portrait in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

2

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

3

Der Skill verbessert das Verständnis Ihres Agenten für avatar-portrait, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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