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.
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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.
apidojo/tweet-scraper actor via REST API| 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
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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