nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Meta Ads Collector Skill
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install adarshvmore/meta-ads-collector-adarshExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Scans the Meta Ad Library API to find active advertisements for a given brand. Extracts the number of active ads, ad formats used, ad types, and the longest-running ad duration. This collector feeds into the Marketing Audit Pipeline to populate the Paid Ads Strategy section of the final report.
// Function signature
collectMetaAds(brandName: string, domain?: string): Promise<MetaAdsData>
// brandName: The brand name to search for in the Ad Library (e.g. "Gymshark")
// domain: Optional domain to refine search (e.g. "gymshark.com"). Used to filter
// results and improve relevance when the brand name is ambiguous.
interface MetaAdsData {
activeAds: number; // Total count of currently active ads
formatsUsed: string[]; // e.g. ["image", "video", "carousel"]
longestRunningAdDays: number; // Days the longest-running active ad has been live
adTypes: string[]; // e.g. ["POLITICAL_AND_ISSUE_ADS", "HOUSING_ADS", "OTHER"]
estimatedSpend?: string; // e.g. "$10,000 - $50,000" (if available from API)
error?: string; // Present only when collector fails
}
https://graph.facebook.com/v19.0/ads_archive
META_ACCESS_TOKEN environment variable (requires a Facebook App with Ad Library API access)META_APP_ID, META_APP_SECRET (used for token generation if needed)brandName and optional domain from the pipelinemetaAdsService.getMetaAds(brandName, domain) which queries the Ad Library APIMetaAdsData interface{
access_token: process.env...
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# Meta Ads Collector Skill
## Purpose
Scans the Meta Ad Library API to find active advertisements for a given brand. Extracts the number of active ads, ad formats used, ad types, and the longest-running ad duration. This collector feeds into the Marketing Audit Pipeline to populate the Paid Ads Strategy section of the final report.
## Input Schema
```typescript
// Function signature
collectMetaAds(brandName: string, domain?: string): Promise<MetaAdsData>
// brandName: The brand name to search for in the Ad Library (e.g. "Gymshark")
// domain: Optional domain to refine search (e.g. "gymshark.com"). Used to filter
// results and improve relevance when the brand name is ambiguous.
```
## Output Schema
```typescript
interface MetaAdsData {
activeAds: number; // Total count of currently active ads
formatsUsed: string[]; // e.g. ["image", "video", "carousel"]
longestRunningAdDays: number; // Days the longest-running active ad has been live
adTypes: string[]; // e.g. ["POLITICAL_AND_ISSUE_ADS", "HOUSING_ADS", "OTHER"]
estimatedSpend?: string; // e.g. "$10,000 - $50,000" (if available from API)
error?: string; // Present only when collector fails
}
```
## API Dependencies
- **API Name:** Meta Ad Library API
- **Endpoint:** `https://graph.facebook.com/v19.0/ads_archive`
- **Auth:** `META_ACCESS_TOKEN` environment variable (requires a Facebook App with Ad Library API access)
- **Additional env vars:** `META_APP_ID`, `META_APP_SECRET` (used for token generation if needed)
- **Cost estimate:** Free (no per-request charge)
- **Rate limits:** Subject to Meta's standard Graph API rate limits (~200 calls/hour)
## Implementation Pattern
### Data Flow
1. Receive `brandName` and optional `domain` from the pipeline
2. Call `metaAdsService.getMetaAds(brandName, domain)` which queries the Ad Library API
3. Process the returned ads array to extract metrics
4. Map processed data to the `MetaAdsData` interface
### API Query Parameters
```typescript
{
access_token: process.env...AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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