name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
日本語の概要は準備中です。原文の説明を表示しています。
Automate advertising campaigns with AI. Create ads, buy media, manage ad budgets, discover ad inventory, run display ads, video ads, CTV campaigns, and optimize ad performance. Perfect for marketing automation, programmatic advertising, media buying, ad management, campaign optimization, creative management, and performance tracking. Launch Facebook ads, Google ads, display advertising, video marketing, and multi-channel campaigns using natural language. Supports ad targeting, audience segmentation, ROI tracking, and automated bidding.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Automate your advertising campaigns with AI. This skill enables OpenClaw agents to discover ad inventory, launch campaigns, manage creatives, and optimize performance across display, video, CTV, audio, and more - all through natural language commands.
No dashboards. No forms. No ad platform expertise required.
Marketing teams running Facebook ads, Google ads, and multi-channel campaigns
Media buyers managing programmatic ad spend across publishers
Agencies automating client campaign management and reporting
E-commerce brands launching product ads and retargeting campaigns
Startups running lean marketing with AI-powered automation
Skip the learning curve - No need to master complex ad platforms
Save time - Launch in 5 minutes vs. hours of manual setup
Spend smarter - AI automatically optimizes budgets to top performers
Scale faster - Manage unlimited campaigns through simple commands
Test risk-free - Public test agent included, no setup required
Official AdCP Repository: https://github.com/adcontextprotocol/adcp
Official AdCP Documentation: https://docs.adcontextprotocol.org
Complete Documentation Index: https://docs.adcontextprotocol.org/llms.txt
Trigger this skill when users ask about:
Campaign Management
Ad Discovery & Media Buying
Creative Management
Performance & Optimization
Targeting & Audiences
No setup required. Use the included test agent to try everything:
Step 1: Discover what's available
"Show me advertising capabilities"
Browse available channels, publishers, and formats.
Step 2: Find ad inventory
"Find display ads for a tech startup, budget $5000"
AI searches and shows matching products with pricing.
Step 3: Launch campaign
"Create campaign with Product prod_123, $5000 budget, targeting California tech professionals"
Campaign goes live instantly.
Step 4: Upload your ads
"Upload these banner images as creatives"
Drop files, get instant creative IDs.
Step 5: Monitor performance
"Show campaign metrics and ROI"
Real-time impressions, clicks, CTR, spend.
Quick campaign launch:
User: "I need to run display ads for my SaaS product"
Agent: [Discovers products] "Found 5 display packages. Want details?"
User: "Create campaign with Product 1, $10k budget, target CTOs"
Agent: [Creates campaign] "Campaign live! ID: mb_abc123"
Performance optimization:
User: "How are my video ads performing?"
Agent: [Shows metrics] "Package A: 2.3% CTR, Package B: 0.8% CTR"
User: "Move $5k from B to A"
Agent: [Reallocates] "Budget updated. Package A now $15k"
Multi-channel campaign:
User: "Launch omnichannel campaign: display in CA, video in NYC, $50k total"
Agent: [Creates packages] "3 packages created across display and video"
Speak naturally. The skill understands:
1. Discovery Phase
"Find video advertising for luxury brands"
↓ Agent searches inventory ↓ Shows matched products with pricing ↓ Explains targeting and formats
2. Campaign Creation
"Create campaign with Product 1, $25k, target professionals"
↓ Agent creates media buy ↓ Sets up targeting overlay ↓ Returns campaign ID and status
3. Creative Management
"Upload my banner ads"
↓ Agent syncs creatives ↓ Assigns to campaign ↓ Returns creative IDs
4. Monitoring & Optimization
"Show performance"
↓ Agent fetches delivery data
↓ Shows metrics by package/creative
↓ Suggests optimizations
const campaign = await testAgent.createMediaBuy({
buyer_ref: 'campaign-2026-q1',
brand_manifest: { url: 'https://acme.com' },
packages: [{ product_id: 'premium_display', budget: 10000 }]
});
await testAgent.syncCreatives({
creatives: [{
buyer_ref: 'banner-300x250',
url: 'https://cdn.acme.com/banner.jpg'
}]
});
const delivery = await testAgent.getMediaBuyDelivery({
media_buy_id: 'mb_abc123'
});
console.log(`CTR: ${delivery.totals.ctr}%, Spend: $${delivery.totals.spend}`);
See REFERENCE.md for complete API docs and EXAMPLES.md for detailed workflows.
AdCP provides 8 standardized tasks for the complete advertising lifecycle. Learn more in the Media Buy Protocol documentation.
Complete task reference: https://docs.adcontextprotocol.org/docs/media-buy/task-reference/
Brand context can be provided two ways:
URL reference (recommended - agent fetches brand info):
{
"brand_manifest": {
"url": "https://brand.com"
}
}
Inline manifest (full brand details):
{
"brand_manifest": {
"name": "Brand Name",
"url": "https://brand.com",
"tagline": "Brand tagline",
"colors": { "primary": "#FF0000" },
"logo": { "url": "https://cdn.brand.com/logo.png" }
}
}
Products support various pricing models:
For auction pricing, include bid_price in your package.
AdCP is not a real-time protocol. Operations may take:
Always check the status field in responses:
completed - Operation finished successfullypending - Awaiting approval or processingfailed - Operation failed (check error details)Apply targeting overlays to campaigns:
{
targeting_overlay: {
geo: {
included: ['US-CA', 'US-NY'], // DMA codes or regions
excluded: ['US-TX']
},
demographics: {
age_ranges: [{ min: 25, max: 44 }],
genders: ['M', 'F']
},
behavioral: {
interests: ['technology', 'gaming'],
purchase_intent: ['consumer_electronics']
},
contextual: {
keywords: ['innovation', 'design'],
categories: ['IAB19'] // Technology & Computing
}
}
}
// 1. Discover capabilities
const caps = await agent.getAdcpCapabilities({});
// 2. Find products
const products = await agent.getProducts({
brief: 'Q1 2026 brand awareness campaign for tech startup',
brand_manifest: { url: 'https://startup.com' },
filters: { channels: ['display', 'video'] }
});
// 3. Check creative formats
const formats = await agent.listCreativeFormats({
format_types: ['display', 'video']
});
// 4. Create campaign
const campaign = await agent.createMediaBuy({
buyer_ref: 'q1-2026-awareness',
brand_manifest: { url: 'https://startup.com' },
packages: [
{
buyer_ref: 'pkg-001',
product_id: products.products[0].product_id,
pricing_option_id: 'cpm-standard',
budget: 15000
}
],
start_time: { type: 'asap' },
end_time: '2026-03-31T23:59:59Z'
});
// 5. Upload creatives
await agent.syncCreatives({
creatives: [...], // Your creative assets
assignments: {
'creative_001': ['pkg-001']
}
});
// 6. Monitor performance
const delivery = await agent.getMediaBuyDelivery({
media_buy_id: campaign.media_buy_id
});
// Pause, adjust budget, and resume campaign
await agent.updateMediaBuy({
media_buy_id: 'mb_abc123',
updates: {
status: 'paused',
budget_change: 5000, // Add $5000
end_time: '2026-04-30T23:59:59Z'
}
});
// Resume after adjustments
await agent.updateMediaBuy({
media_buy_id: 'mb_abc123',
updates: { status: 'active' }
});
More workflow examples: See EXAMPLES.md for complete campaign scenarios including creative management, multi-channel campaigns, and optimization workflows.
For development and testing, use the public test agent:
Agent URL: https://test-agent.adcontextprotocol.org/mcp
Auth Token: 1v8tAhASaUYYp4odoQ1PnMpdqNaMiTrCRqYo9OJp6IQ
import { testAgent } from '@adcp/client/testing';
// No authentication needed for test agent
const result = await testAgent.getProducts({
brief: 'Test campaign',
brand_manifest: { url: 'https://example.com' }
});
Interactive testing available at: testing.adcontextprotocol.org
Common error patterns:
400 Bad Request - Invalid parameters:
{
"error": {
"code": "VALIDATION_ERROR",
"message": "budget must be greater than 0",
"field": "packages[0].budget"
}
}
401 Unauthorized - Missing or invalid auth:
{
"error": {
"code": "UNAUTHORIZED",
"message": "Invalid authentication token"
}
}
404 Not Found - Invalid ID reference:
{
"error": {
"code": "NOT_FOUND",
"message": "Product not found",
"resource": "product_id: premium_video_30s"
}
}
Always check for errors before processing responses:
if (result.error) {
console.error(`Error: ${result.error.message}`);
return;
}
Call get_adcp_capabilities first to understand what the agent supports before making other requests.
Use descriptive buyer_ref values for tracking:
'campaign-2026-q1-tech-launch''c1', 'test', 'abc'Check status field and implement polling for pending operations:
let status = 'pending';
while (status === 'pending') {
await sleep(5000); // Wait 5 seconds
const update = await agent.getMediaBuyDelivery({
media_buy_id: campaign.media_buy_id
});
status = update.status;
}
Better briefs lead to better product matches:
'Premium video inventory for luxury automotive brand targeting high-income professionals aged 35-54 in major metros. Focus on brand awareness with completion rates above 70%.''video ads', 'need advertising'Always check list_creative_formats to ensure your creatives meet requirements before uploading.
Regularly check delivery metrics to ensure campaigns are pacing properly:
const delivery = await agent.getMediaBuyDelivery({
media_buy_id: campaign.media_buy_id
});
const pacing = delivery.delivery.spend / delivery.delivery.budget;
console.log(`Budget pacing: ${(pacing * 100).toFixed(1)}%`);
pending statusget_adcp_capabilities firstFor help with AdCP:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
日本語の概要は準備中です。原文の説明を表示しています。
Use cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.
日本語の概要は準備中です。原文の説明を表示しています。
Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.
日本語の概要は準備中です。原文の説明を表示しています。
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
日本語の概要は準備中です。原文の説明を表示しています。
Find and complete paid tasks on the 0xWork decentralized marketplace (Base chain, USDC escrow). Use when: the agent wants to earn money/USDC by doing work, discover available tasks, claim a bounty, submit deliverables, check earnings or wallet balance, or set up as a 0xWork worker. Task categories: Writing, Research, Social, Creative, Code, Data. NOT for: posting tasks (use the website), managing the 0xWork platform, or frontend development.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and practices that dramatically accelerate development velocity. Covers parallel execution, automation, feedback loops, workflow optimization, and anti-pattern avoidance. Use when starting projects, planning sprints, optimizing workflows, or onboarding developers.
日本語の概要は準備中です。原文の説明を表示しています。