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Adcp Advertising 1.0.1

作者 dujch · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install adcp-advertising-1-0-1
功能描述
Automate advertising campaigns with AI. Create ads, buy media, manage ad budgets, discover ad inventory, run display ads, video ads, CTV campaigns, and optim...
使用说明 (SKILL.md)

Ad Context Protocol (AdCP) Advertising Skill

Overview

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.

What You Can Do

  • 🎯 Launch campaigns in minutes - "Create a $10k display campaign targeting tech professionals in California"
  • 🔍 Discover ad inventory instantly - "Find premium video placements for luxury brands"
  • 🎨 Upload ads with ease - "Upload these banner images as creatives"
  • 📊 Track ROI in real-time - "Show me campaign performance and CTR by creative"
  • 🎛️ Auto-optimize spend - "Reallocate budget to top-performing packages"
  • 🌐 Target precisely - Demographics, behaviors, interests, locations, devices, times

Perfect For

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

Why Choose This Skill?

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

When to Use This Skill

Trigger this skill when users ask about:

Campaign Management

  • "Create a display ad campaign"
  • "Launch Facebook ads for my product"
  • "Set up a $5000 video advertising campaign"
  • "Pause my underperforming campaigns"

Ad Discovery & Media Buying

  • "Find advertising inventory for luxury brands"
  • "Show me CTV ad placements in major cities"
  • "What display ad options are available?"
  • "Buy media for a tech startup"

Creative Management

  • "Upload these banner images"
  • "Which creative is performing best?"
  • "Add video ads to my campaign"
  • "Manage my ad library"

Performance & Optimization

  • "How is my campaign performing?"
  • "Show me ROI by channel"
  • "Optimize my ad spend"
  • "Reallocate budget to top performers"
  • "Track impressions and click-through rates"

Targeting & Audiences

  • "Target professionals in California"
  • "Set up demographic targeting"
  • "Create a retargeting campaign"
  • "Target by device type and time of day"

Quick Start

Launch Your First Campaign (5 Minutes)

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.

Real-World Usage Examples

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"

How It Works

Natural Language Understanding

Speak naturally. The skill understands:

  • Budgets: "$5000", "five thousand dollars", "5k budget"
  • Locations: "California", "major US cities", "New York and LA"
  • Audiences: "tech professionals", "age 25-45", "high income"
  • Goals: "brand awareness", "drive conversions", "increase sales"

Progressive Workflow

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

Core Operations

Create Campaign

const campaign = await testAgent.createMediaBuy({
  buyer_ref: 'campaign-2026-q1',
  brand_manifest: { url: 'https://acme.com' },
  packages: [{ product_id: 'premium_display', budget: 10000 }]
});

Upload Creatives

await testAgent.syncCreatives({
  creatives: [{ 
    buyer_ref: 'banner-300x250',
    url: 'https://cdn.acme.com/banner.jpg'
  }]
});

Monitor Performance

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.

Core Concepts

The 8 Media Buy Tasks

AdCP provides 8 standardized tasks for the complete advertising lifecycle. Learn more in the Media Buy Protocol documentation.

  1. get_adcp_capabilities - Discover agent features and portfolio (~1s)
  2. get_products - Find inventory using natural language (~60s)
  3. list_creative_formats - View creative specifications (~1s)
  4. create_media_buy - Launch campaigns (minutes-days, may require approval)
  5. update_media_buy - Modify campaigns (minutes-days)
  6. sync_creatives - Upload creative assets (minutes-days)
  7. list_creatives - Query creative library (~1s)
  8. get_media_buy_delivery - Track performance (~60s)

Complete task reference: https://docs.adcontextprotocol.org/docs/media-buy/task-reference/

Brand Manifest

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" }
  }
}

Pricing Models

Products support various pricing models:

  • CPM (Cost Per Mille/Thousand) - Fixed price per 1000 impressions
  • CPM-Auction - Bid-based pricing for impressions
  • CPCV (Cost Per Completed View) - Video completions
  • Flat-Fee - Fixed campaign cost
  • CPP (Cost Per Point) - Percentage of audience reached

For auction pricing, include bid_price in your package.

Asynchronous Operations

AdCP is not a real-time protocol. Operations may take:

  • ~1 second - Simple lookups (formats, creative lists)
  • ~60 seconds - AI/inference operations (product discovery)
  • Minutes to days - Operations requiring human approval (campaign creation)

Always check the status field in responses:

  • completed - Operation finished successfully
  • pending - Awaiting approval or processing
  • failed - Operation failed (check error details)

Targeting Capabilities

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
    }
  }
}

Common Workflows

Workflow 1: Campaign Discovery to Launch

// 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
});

Workflow 2: Update Running Campaign

// 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.

Test Agent

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

Error Handling

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;
}

Best Practices

1. Always Start with Capabilities

Call get_adcp_capabilities first to understand what the agent supports before making other requests.

2. Use Clear Buyer References

Use descriptive buyer_ref values for tracking:

  • Good: 'campaign-2026-q1-tech-launch'
  • Avoid: 'c1', 'test', 'abc'

3. Handle Async Operations

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;
}

4. Write Detailed Briefs

Better briefs lead to better product matches:

  • Good: '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%.'
  • Avoid: 'video ads', 'need advertising'

5. Validate Creative Formats

Always check list_creative_formats to ensure your creatives meet requirements before uploading.

6. Monitor Budget Pacing

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)}%`);

Additional Resources

Official AdCP Documentation

This Skill's Documentation

Key Reminders

  1. AdCP is asynchronous - Operations may take minutes to days
  2. Human approval may be required - Check for pending status
  3. Start with capabilities - Always call get_adcp_capabilities first
  4. Brand context matters - Provide detailed brand manifests for better results
  5. Targeting is additive - Product targeting + your overlay = final targeting
  6. Creative formats are strict - Always validate against format specifications
  7. Monitor performance - Regular delivery checks ensure campaign success

Support

For help with AdCP:

安全使用建议
This skill is documentation-heavy and appears to implement an advertising agent that calls remote AdCP endpoints. Before installing: (1) be prepared for the agent to make outbound network requests to the test and production AdCP endpoints (the README includes a public test token and test URLs); (2) do not store production bearer tokens in plaintext in your workspace — use secure env/secret managers; (3) if you want to avoid any external calls, do not enable autonomous invocation or disable the skill until you review how your agent runtime implements the 'agent' client calls; (4) verify you trust the AdCP endpoints (docs.adcontextprotocol.org / test-agent.adcontextprotocol.org) and the repository source before providing any production credentials; (5) if you have an internal security policy, have the skill reviewed by your network/security team and monitor the agent's outbound traffic during initial use.
功能分析
Type: OpenClaw Skill Name: adcp-advertising-1-0-1 Version: 1.0.0 The adcp-advertising skill bundle provides a comprehensive set of instructions and documentation for an AI agent to interact with the Ad Context Protocol (AdCP) for managing advertising campaigns. The files (SKILL.md, REFERENCE.md, EXAMPLES.md, etc.) contain detailed API specifications, targeting strategies, and creative management guides. While the bundle includes a hardcoded authentication token (1v8tAhASaUYYp4odoQ1PnMpdqNaMiTrCRqYo9OJp6IQ), it is explicitly documented as a public credential for a sandbox test environment (test-agent.adcontextprotocol.org). No evidence of malicious intent, data exfiltration, or harmful prompt injection was found; the code logic is consistent with the stated purpose of marketing automation.
能力评估
Purpose & Capability
The name/description (advertising, campaign creation, media buying, creative management) match the SKILL.md and example code. The skill's docs and examples consistently show AdCP API calls, creative uploads, product discovery, and create/update campaign flows — all expected for an advertising automation skill.
Instruction Scope
The SKILL.md and companion docs instruct the agent to call external AdCP endpoints (test and production agent URLs), use an AdCP client, upload creatives by URL, and create/modify campaigns. The instructions do not ask the agent to read local system files, unrelated environment variables, or private credentials beyond auth tokens for AdCP. Note: examples import '@adcp/client/testing' and reference an 'agent' object — the skill assumes client libraries or a runtime tool surface are available, which is an implementation assumption rather than an explicit dependency.
Install Mechanism
No install spec or code to download — it is instruction-only. No archives, URLs, or binary installs are present in the package metadata.
Credentials
The skill declares no required env vars or credentials. The documentation includes a public test-agent URL and a shared test bearer token (explicitly labeled 'intentionally public') for development/testing. That is plausible for an API-centric skill, but you should be aware the skill encourages using that public token and will make network calls. Production operations do require bearer tokens per the docs; those would be sensitive and should be stored securely by the user.
Persistence & Privilege
The skill does not request 'always: true' and makes no claims about modifying other skills or system-wide settings. Autonomous model invocation is allowed (platform default) but not excessive by itself.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install adcp-advertising-1-0-1
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /adcp-advertising-1-0-1 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of AdCP Advertising skill. - Automate ad campaigns with AI using natural language commands. - Launch and manage display, video, CTV, and multi-channel campaigns across multiple platforms. - Discover ad inventory, target audiences, and automatically optimize ad spend. - Upload creatives and track performance metrics like ROI and CTR in real time. - Suitable for marketing teams, agencies, media buyers, e-commerce brands, and startups.
元数据
Slug adcp-advertising-1-0-1
版本 1.0.0
许可证 MIT-0
累计安装 3
当前安装数 3
历史版本数 1
常见问题

Adcp Advertising 1.0.1 是什么?

Automate advertising campaigns with AI. Create ads, buy media, manage ad budgets, discover ad inventory, run display ads, video ads, CTV campaigns, and optim... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 174 次。

如何安装 Adcp Advertising 1.0.1?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install adcp-advertising-1-0-1」即可一键安装,无需额外配置。

Adcp Advertising 1.0.1 是免费的吗?

是的,Adcp Advertising 1.0.1 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Adcp Advertising 1.0.1 支持哪些平台?

Adcp Advertising 1.0.1 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Adcp Advertising 1.0.1?

由 dujch(@dujch)开发并维护,当前版本 v1.0.0。

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