create-image-fal
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent. image_urls…
Search and scrape Twitter/X posts using Apify. Use when you need to find tweets, track brand mentions, monitor competitors on Twitter, or analyze Twitter discussions. Uses Twitter native search syntax (since:/until:) for reliable date filtering.
$ npx -y skills add gooseworks-ai/goose-skills --skill twitter-mention-tracker --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/twitter-mention-trackerContext preview
The summary Claude sees to decide when to auto-load this skill.
Search and scrape Twitter/X posts using Apify. Use when you need to find tweets, track brand mentions, monitor competitors on Twitter, or analyze Twitter discussions. Uses Twitter native search syntax (since:/until:) for reliable date filtering.
name: twitter-mention-tracker description: > Search and scrape Twitter/X posts using Apify. Use when you need to find tweets, track brand mentions, monitor competitors on Twitter, or analyze Twitter discussions. Uses Twitter native search syntax (since:/until:) for reliable date filtering.
Search Twitter/X posts using the Apify `apidojo/tweet-scraper` actor.
Requires `APIFY_API_TOKEN` env var (or `--token` flag).
# Search with date range (recommended -- uses Twitter native since:/until: operators) python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "YourCompany" --since 2026-02-15 --until 2026-02-23 # Quick summary of recent mentions python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "@yourhandle" --max-tweets 20 --output summary # Search without date filtering python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "AI content marketing" --max-tweets 50
**Important:** The `apidojo/tweet-scraper` actor's built-in date parameters are unreliable. This script embeds `since:YYYY-MM-DD` and `until:YYYY-MM-DD` directly into the search query string, using Twitter's native advanced search syntax. This ensures date filtering works correctly server-side.
1. Builds a search term with the query quoted and date operators appended 2. Calls the Apify `apidojo/tweet-scraper` actor via REST API 3. Polls until the run completes, then fetches the dataset 4. Deduplicates by tweet ID/URL 5. Applies optional keyword filtering (client-side) 6. Sorts by likes (descending) and outputs JSON or summary
| Flag | Default | Description | |------|---------|-------------| | `--query` | *required* | Search query (quoted in Twitter search) | | `--since` | none | Start date YYYY-MM-DD (inclusive) | | `--until` | none | End date YYYY-MM-DD (exclusive) | | `--max-tweets` | 50 | Max tweets to scrape | | `--keywords` | none | Additional filter keywords (comma-separated, OR logic) | | `--output` | json | Output format: `json` or `summary` | | `--token` | env var | Apify token (prefer `APIFY_API_TOKEN` env var) | | `--timeout` | 300 | Max seconds to wait for the Apify run |
{
"searchTerms": ["\"YourCompany\" since:2026-02-15 until:2026-02-22"],
"maxTweets": 50,
"searchMode": "live"
}Tweets are returned as JSON array sorted by likes. Each tweet has:
{
"id": "...",
"text": "Tweet text...",
"fullText": "Full tweet text...",
"likeCount": 42,
"retweetCount": 5,
"replyCount": 3,
"viewCount": 1200,
"createdAt": "2026-02-18T12:00:00.000Z",
"author": {"userName": "handle", "name": "Display Name", ...},
"twitterUrl": "https://twitter.com/..."
}python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "CompetitorName" --since 2026-02-15 --until 2026-02-23 --output summary
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "@YourHandle OR \"YourBrand\"" --max-tweets 100
Put your AI agent on the growth team. Research customers and competitors, analyze what is working, create the next campaign, and learn from the result.
Repo: gooseworks-ai/goose-skills
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