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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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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