Clawra Selfie logo

Clawra Selfie

OrganizationPopular
sumelabs
clawra-selfie

Edit Clawra's reference image with Grok Imagine (xAI Aurora) and send selfies to messaging channels via OpenClaw

Overview

Publishersumelabs
Repositoryclawra
Skill nameclawra-selfie
Stars
2.4K
Forks
384
Bundled files
9
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sumelabs on GitHub. Read the source before you install it.

Installation

Install the Clawra Selfie AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sumelabs/clawra.git \
  .claude/skills/clawra-selfie
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clawra Selfie in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Clawra Selfie on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Clawra Selfie is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Clawra Selfie

Edit a fixed reference image using xAI's Grok Imagine model and distribute it across messaging platforms (WhatsApp, Telegram, Discord, Slack, etc.) via OpenClaw.

Reference Image

The skill uses a fixed reference image hosted on jsDelivr CDN:

https://cdn.jsdelivr.net/gh/SumeLabs/clawra@main/assets/clawra.png

When to Use

  • User says "send a pic", "send me a pic", "send a photo", "send a selfie"
  • User says "send a pic of you...", "send a selfie of you..."
  • User asks "what are you doing?", "how are you doing?", "where are you?"
  • User describes a context: "send a pic wearing...", "send a pic at..."
  • User wants Clawra to appear in a specific outfit, location, or situation

Quick Reference

Required Environment Variables

bash
FAL_KEY=your_fal_api_key          # Get from https://fal.ai/dashboard/keys
OPENCLAW_GATEWAY_TOKEN=your_token  # From: openclaw doctor --generate-gateway-token

Workflow

  1. Get user prompt for how to edit the image
  2. Edit image via fal.ai Grok Imagine Edit API with fixed reference
  3. Extract image URL from response
  4. Send to OpenClaw with target channel(s)

Step-by-Step Instructions

Step 1: Collect User Input

Ask the user for:

  • User context: What should the person in the image be doing/wearing/where?
  • Mode (optional): mirror or direct selfie style
  • Target channel(s): Where should it be sent? (e.g., #general, @username, channel ID)
  • Platform (optional): Which platform? (discord, telegram, whatsapp, slack)

Prompt Modes

Mode 1: Mirror Selfie (default)

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

make a pic of this person, but [user's context]. the person is taking a mirror selfie

Example: "wearing a santa hat" →

make a pic of this person, but wearing a santa hat. the person is taking a mirror selfie

Mode 2: Direct Selfie

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

a close-up selfie taken by herself at [user's context], direct eye contact with the camera, looking straight into the lens, eyes centered and clearly visible, not a mirror selfie, phone held at arm's length, face fully visible

Example: "a cozy cafe with warm lighting" →

a close-up selfie taken by herself at a cozy cafe with warm lighting, direct eye contact with the camera, looking straight into the lens, eyes centered and clearly visible, not a mirror selfie, phone held at arm's length, face fully visible

Mode Selection Logic

Keywords in RequestAuto-Select Mode
outfit, wearing, clothes, dress, suit, fashionmirror
cafe, restaurant, beach, park, city, locationdirect
close-up, portrait, face, eyes, smiledirect
full-body, mirror, reflectionmirror

Step 2: Edit Image with Grok Imagine

Use the fal.ai API to edit the reference image:

bash
REFERENCE_IMAGE="https://cdn.jsdelivr.net/gh/SumeLabs/clawra@main/assets/clawra.png"

# Mode 1: Mirror Selfie
PROMPT="make a pic of this person, but <USER_CONTEXT>. the person is taking a mirror selfie"

# Mode 2: Direct Selfie
PROMPT="a close-up selfie taken by herself at <USER_CONTEXT>, direct eye contact with the camera, looking straight into the lens, eyes centered and clearly visible, not a mirror selfie, phone held at arm's length, face fully visible"

# Build JSON payload with jq (handles escaping properly)
JSON_PAYLOAD=$(jq -n \
  --arg image_url "$REFERENCE_IMAGE" \
  --arg prompt "$PROMPT" \
  '{image_url: $image_url, prompt: $prompt, num_images: 1, output_format: "jpeg"}')

curl -X POST "https://fal.run/xai/grok-imagine-image/edit" \
  -H "Authorization: Key $FAL_KEY" \
  -H "Content-Type: application/json" \
  -d "$JSON_PAYLOAD"

Response Format:

json
{
  "images": [
    {
      "url": "https://v3b.fal.media/files/...",
      "content_type": "image/jpeg",
      "width": 1024,
      "height": 1024
    }
  ],
  "revised_prompt": "Enhanced prompt text..."
}

Step 3: Send Image via OpenClaw

Use the OpenClaw messaging API to send the edited image:

bash
openclaw message send \
  --action send \
  --channel "<TARGET_CHANNEL>" \
  --message "<CAPTION_TEXT>" \
  --media "<IMAGE_URL>"

Alternative: Direct API call

bash
curl -X POST "http://localhost:18789/message" \
  -H "Authorization: Bearer $OPENCLAW_GATEWAY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "action": "send",
    "channel": "<TARGET_CHANNEL>",
    "message": "<CAPTION_TEXT>",
    "media": "<IMAGE_URL>"
  }'

Complete Script Example

bash
#!/bin/bash
# grok-imagine-edit-send.sh

# Check required environment variables
if [ -z "$FAL_KEY" ]; then
  echo "Error: FAL_KEY environment variable not set"
  exit 1
fi

# Fixed reference image
REFERENCE_IMAGE="https://cdn.jsdelivr.net/gh/SumeLabs/clawra@main/assets/clawra.png"

USER_CONTEXT="$1"
CHANNEL="$2"
MODE="${3:-auto}"  # mirror, direct, or auto
CAPTION="${4:-Edited with Grok Imagine}"

if [ -z "$USER_CONTEXT" ] || [ -z "$CHANNEL" ]; then
  echo "Usage: $0 <user_context> <channel> [mode] [caption]"
  echo "Modes: mirror, direct, auto (default)"
  echo "Example: $0 'wearing a cowboy hat' '#general' mirror"
  echo "Example: $0 'a cozy cafe' '#general' direct"
  exit 1
fi

# Auto-detect mode based on keywords
if [ "$MODE" == "auto" ]; then
  if echo "$USER_CONTEXT" | grep -qiE "outfit|wearing|clothes|dress|suit|fashion|full-body|mirror"; then
    MODE="mirror"
  elif echo "$USER_CONTEXT" | grep -qiE "cafe|restaurant|beach|park|city|close-up|portrait|face|eyes|smile"; then
    MODE="direct"
  else
    MODE="mirror"  # default
  fi
  echo "Auto-detected mode: $MODE"
fi

# Construct the prompt based on mode
if [ "$MODE" == "direct" ]; then
  EDIT_PROMPT="a close-up selfie taken by herself at $USER_CONTEXT, direct eye contact with the camera, looking straight into the lens, eyes centered and clearly visible, not a mirror selfie, phone held at arm's length, face fully visible"
else
  EDIT_PROMPT="make a pic of this person, but $USER_CONTEXT. the person is taking a mirror selfie"
fi

echo "Mode: $MODE"
echo "Editing reference image with prompt: $EDIT_PROMPT"

# Edit image (using jq for proper JSON escaping)
JSON_PAYLOAD=$(jq -n \
  --arg image_url "$REFERENCE_IMAGE" \
  --arg prompt "$EDIT_PROMPT" \
  '{image_url: $image_url, prompt: $prompt, num_images: 1, output_format: "jpeg"}')

RESPONSE=$(curl -s -X POST "https://fal.run/xai/grok-imagine-image/edit" \
  -H "Authorization: Key $FAL_KEY" \
  -H "Content-Type: application/json" \
  -d "$JSON_PAYLOAD")

# Extract image URL
IMAGE_URL=$(echo "$RESPONSE" | jq -r '.images[0].url')

if [ "$IMAGE_URL" == "null" ] || [ -z "$IMAGE_URL" ]; then
  echo "Error: Failed to edit image"
  echo "Response: $RESPONSE"
  exit 1
fi

echo "Image edited: $IMAGE_URL"
echo "Sending to channel: $CHANNEL"

# Send via OpenClaw
openclaw message send \
  --action send \
  --channel "$CHANNEL" \
  --message "$CAPTION" \
  --media "$IMAGE_URL"

echo "Done!"

Node.js/TypeScript Implementation

typescript
import { fal } from "@fal-ai/client";
import { exec } from "child_process";
import { promisify } from "util";

const execAsync = promisify(exec);

const REFERENCE_IMAGE = "https://cdn.jsdelivr.net/gh/SumeLabs/clawra@main/assets/clawra.png";

interface GrokImagineResult {
  images: Array<{
    url: string;
    content_type: string;
    width: number;
    height: number;
  }>;
  revised_prompt?: string;
}

type SelfieMode = "mirror" | "direct" | "auto";

function detectMode(userContext: string): "mirror" | "direct" {
  const mirrorKeywords = /outfit|wearing|clothes|dress|suit|fashion|full-body|mirror/i;
  const directKeywords = /cafe|restaurant|beach|park|city|close-up|portrait|face|eyes|smile/i;

  if (directKeywords.test(userContext)) return "direct";
  if (mirrorKeywords.test(userContext)) return "mirror";
  return "mirror"; // default
}

function buildPrompt(userContext: string, mode: "mirror" | "direct"): string {
  if (mode === "direct") {
    return `a close-up selfie taken by herself at ${userContext}, direct eye contact with the camera, looking straight into the lens, eyes centered and clearly visible, not a mirror selfie, phone held at arm's length, face fully visible`;
  }
  return `make a pic of this person, but ${userContext}. the person is taking a mirror selfie`;
}

async function editAndSend(
  userContext: string,
  channel: string,
  mode: SelfieMode = "auto",
  caption?: string
): Promise<string> {
  // Configure fal.ai client
  fal.config({
    credentials: process.env.FAL_KEY!
  });

  // Determine mode
  const actualMode = mode === "auto" ? detectMode(userContext) : mode;
  console.log(`Mode: ${actualMode}`);

  // Construct the prompt
  const editPrompt = buildPrompt(userContext, actualMode);

  // Edit reference image with Grok Imagine
  console.log(`Editing image: "${editPrompt}"`);

  const result = await fal.subscribe("xai/grok-imagine-image/edit", {
    input: {
      image_url: REFERENCE_IMAGE,
      prompt: editPrompt,
      num_images: 1,
      output_format: "jpeg"
    }
  }) as { data: GrokImagineResult };

  const imageUrl = result.data.images[0].url;
  console.log(`Edited image URL: ${imageUrl}`);

  // Send via OpenClaw
  const messageCaption = caption || `Edited with Grok Imagine`;

  await execAsync(
    `openclaw message send --action send --channel "${channel}" --message "${messageCaption}" --media "${imageUrl}"`
  );

  console.log(`Sent to ${channel}`);
  return imageUrl;
}

// Usage Examples

// Mirror mode (auto-detected from "wearing")
editAndSend(
  "wearing a cyberpunk outfit with neon lights",
  "#art-gallery",
  "auto",
  "Check out this AI-edited art!"
);
// → Mode: mirror
// → Prompt: "make a pic of this person, but wearing a cyberpunk outfit with neon lights. the person is taking a mirror selfie"

// Direct mode (auto-detected from "cafe")
editAndSend(
  "a cozy cafe with warm lighting",
  "#photography",
  "auto"
);
// → Mode: direct
// → Prompt: "a close-up selfie taken by herself at a cozy cafe with warm lighting, direct eye contact..."

// Explicit mode override
editAndSend("casual street style", "#fashion", "direct");

Supported Platforms

OpenClaw supports sending to:

PlatformChannel FormatExample
Discord#channel-name or channel ID#general, 123456789
Telegram@username or chat ID@mychannel, -100123456
WhatsAppPhone number (JID format)1234567890@s.whatsapp.net
Slack#channel-name#random
SignalPhone number+1234567890
MS TeamsChannel reference(varies)

Grok Imagine Edit Parameters

ParameterTypeDefaultDescription
image_urlstringrequiredURL of image to edit (fixed in this skill)
promptstringrequiredEdit instruction
num_images1-41Number of images to generate
output_formatenum"jpeg"jpeg, png, webp

Setup Requirements

1. Install fal.ai client (for Node.js usage)

bash
npm install @fal-ai/client

2. Install OpenClaw CLI

bash
npm install -g openclaw

3. Configure OpenClaw Gateway

bash
openclaw config set gateway.mode=local
openclaw doctor --generate-gateway-token

4. Start OpenClaw Gateway

bash
openclaw gateway start

Error Handling

  • FAL_KEY missing: Ensure the API key is set in environment
  • Image edit failed: Check prompt content and API quota
  • OpenClaw send failed: Verify gateway is running and channel exists
  • Rate limits: fal.ai has rate limits; implement retry logic if needed

Tips

  1. Mirror mode context examples (outfit focus):

    • "wearing a santa hat"
    • "in a business suit"
    • "wearing a summer dress"
    • "in streetwear fashion"
  2. Direct mode context examples (location/portrait focus):

    • "a cozy cafe with warm lighting"
    • "a sunny beach at sunset"
    • "a busy city street at night"
    • "a peaceful park in autumn"
  3. Mode selection: Let auto-detect work, or explicitly specify for control

  4. Batch sending: Edit once, send to multiple channels

  5. Scheduling: Combine with OpenClaw scheduler for automated posts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Clawra Selfie AI skill do?

Edit Clawra's reference image with Grok Imagine (xAI Aurora) and send selfies to messaging channels via OpenClaw

Why use Clawra Selfie on TypingMind?

Because you install it once and use it with any model. Clawra Selfie is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Clawra Selfie in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sumelabs/clawra/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Clawra Selfie?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Clawra Selfie?

As many as you like. As long as a model supports skills, you can use Clawra Selfie with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Clawra Selfie AI skill free?

It is published on GitHub by sumelabs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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