Stella Selfie

Generate persona-consistent selfie images using Google Gemini or fal (xAI Grok Imagine) and send them to messaging channels via OpenClaw. Supports multi-reference avatar blending for strong character consistency.

When to Use

• - User says "send a pic", "send me a photo", "send a selfie", "发张照片", "发自拍"
• User says "show me what you look like...", "send a pic of you...", "展示你在..."
• User describes a scene: "send a pic wearing...", "send a pic at...", "穿着...发张图"
• User wants the agent to appear in a specific outfit, location, or situation

Prompt Modes

Mode 1: Mirror Selfie (default)

Best for: outfit showcases, full-body shots, fashion content

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Mode 2: Direct Selfie

Best for: close-up portraits, location shots, emotional expressions

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Mode 3: Third-Person Photo

Best for: non-selfie viewpoints, including explicit third-person requests and scenes that should not read as a selfie

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Mode Selection Logic

Default policy:

• - Interpret explicit user requirements first: camera style, outfit emphasis, body framing, scene, pose, and expression.
• Use mirror by default for outfit / full-body / self-presentation requests, even if the user did not explicitly mention a mirror.
• Use direct by default for selfie requests focused on face, emotion, immediacy, or in-the-moment presence.
• Use third_person only when the user explicitly asks for a non-selfie style or clearly describes a shot that should not read as a selfie.

Default mode when no keywords match and timeline is unavailable: INLINECODE8

Resolution Keywords

Step-by-Step Instructions

Step 1: Collect User Input

Determine from the user's message:

• - Explicit context (optional): scene, outfit, location, activity — detect from keywords
• Mode (optional): mirror, direct, or third_person — auto-detect from explicit user intent if not specified
• Target channel: Where to send (e.g., #general, @username, channel ID)
• Channel provider (optional): Which platform (discord, telegram, whatsapp, slack)
• Resolution (optional): 1K / 2K / 4K — default 1K
• Count (optional): How many images — default 1, only increase if explicitly requested
• Has explicit scene?: Does the request contain any specific scene/outfit/location/activity keywords?

Step 2: Enrich with Timeline Context Or Recent Scene Recall

INLINECODE17 is an optional enhancement, not a prerequisite.

• - If timeline_resolve is unavailable in the current environment, skip this step and proceed with Stella's default behavior.
• If the request is a current-state Sparse prompt — for example "发张自拍", "发张照片", "想看看你", "send a selfie", "send a photo", "show me what you look like" — and timeline_resolve is available, load and follow references/timeline-integration.md.
• If the current request clearly refers back to a single recently resolved timeline scene in the current conversation, load and follow references/timeline-integration.md even if the photo request itself is not Sparse.
• If the user already provided a clear standalone scene, outfit, location, activity, or camera requirement and it is not a callback to a recently resolved timeline scene, do not use timeline enhancement. Follow the default policy directly.
• When you do call timeline_resolve, do not freely rewrite the request into output-slot questions. Use the fixed query rules in references/timeline-integration.md.
• Only enable Nano Banana real-world grounding when the prompt can explicitly include a concrete city plus an exact local date/time anchor from timeline data. If those anchors are missing, do not claim real-world synchronization.
• If timeline returns fact.status === "empty", is missing result.consumption, or any error occurs, immediately fall back to Step 3 without mentioning timeline failure to the user.

Never block image generation on timeline availability. Timeline enrichment is best-effort and should only be used for current-state Sparse prompts or explicit callbacks to a recently resolved timeline scene.

Step 3: Assemble Prompt

Select mode from the default policy first.

If the request is Sparse, and you loaded references/timeline-integration.md and obtained usable timeline context, apply its Sparse-only merge and prompt rules.

When that timeline enrichment includes outdoor real-world grounding, keep the grounding clause as a separate strong instruction sentence rather than a soft atmosphere phrase like Make it feel like....

Otherwise, use the user's explicit context directly and keep Stella's original fallback behavior:

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Step 4: Generate Image

Run the Stella script:

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Step 5: Confirm Result

After the script completes, confirm to the user:

• - Image was generated successfully
• Image was sent to the target channel
• If any error occurred, send a concise actionable failure message

Environment Variables

Stella supports multiple providers and a gateway-backed send path, so its sensitive runtime environment variables
are explicitly declared in metadata.openclaw.requires.env for OpenClaw's env-injection allowlist.
The skill also sets metadata.openclaw.always: true, so these declarations do not become hard load-time gates.
Actual credential validation remains runtime-driven inside skill.js, based on the selected provider.

Credential requirements are provider-specific:

• - Default Provider=gemini: requires INLINECODE44
• INLINECODE45: requires INLINECODE46
• INLINECODE47: requires INLINECODE48

Media File Handling (Gemini)

When Provider=gemini, Stella writes generated files to:

• - INLINECODE50

After successful send, Stella deletes the local file immediately. If send fails, the file is kept for debugging.

Skill Environment Options

Configure in your OpenClaw openclaw.json under skills.entries.stella-selfie.env:

Note for Provider=fal users: fal's image editing API only accepts HTTP/HTTPS image URLs. Local file paths (from Avatar / AvatarsDir) are not supported. Configure AvatarsURLs in IDENTITY.md with public URLs of your reference images to enable image editing with fal.

Note for Provider=laozhang users: laozhang.ai uses the Google-native Gemini API format (gemini-3-pro-image-preview). It requires local reference images from Avatar / AvatarsDir and does not use AvatarsURLs. Supports 1K/2K/4K resolution and 10 aspect ratios. Get your API key at api.laozhang.ai — remember to configure a billing mode in the token settings before use.

Delivery Path

• - Stella sends via openclaw message send.
• Delivery auth and routing are handled by the local OpenClaw installation, not by skill-level gateway tokens.

External Endpoints And Data Flow

Security And Privacy

• - Stella reads ~/.openclaw/workspace/IDENTITY.md and local avatar files to build reference context.
• Under Provider=gemini, selected local avatar images are uploaded to Gemini as part of normal image generation.
• Under Provider=fal, only public http/https avatar URLs are sent; local avatar files are not uploaded to fal directly.
• Under Provider=laozhang, local avatar files from Avatar / AvatarsDir are base64-encoded and uploaded to laozhang.ai.
• Generated files (Gemini and laozhang) are written to ~/.openclaw/workspace/stella-selfie/ and deleted after successful send.

User Configuration

Before using this skill, you must configure your OpenClaw workspace. See templates/SOUL.fragment.md for the recommended capability snippet to add to your SOUL.md.

Required: IDENTITY.md

Add the following fields to ~/.openclaw/workspace/IDENTITY.md:

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• - Avatar: Path to your primary reference image (relative to workspace root)
• INLINECODE94: Directory containing multiple reference photos of the same character (different styles, scenes, outfits)
• INLINECODE95: Comma-separated public URLs of reference images — required for Provider=fal (local files are not supported by fal's API)

Required: avatars/ Directory

Place your reference photos in ~/.openclaw/workspace/avatars/:

• - Use jpg, jpeg, png, or webp format
• All photos should be of the same character
• Different styles, scenes, outfits, and expressions work best
• Images are selected by creation time (newest first)

Required: SOUL.md

Add the Stella capability block to ~/.openclaw/workspace/SOUL.md. See README.md ("4. SOUL.md") for the copy/paste snippet.

Installation

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After installation, complete the configuration steps above before using the skill.