Scrape blog posts via RSS feeds (free, no API key) with Apify fallback for JS-heavy sites. Use when you need to monitor competitor blogs, track industry content, or aggregate blog posts by keyword.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Scrape blog posts via RSS/Atom feeds (free) with optional Apify fallback for JS-heavy sites.
No API key needed for RSS mode.
# Scrape a blog's RSS feed
python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
--urls "https://example.com/blog" --days 30
# Multiple blogs with keyword filter
python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
--urls "https://blog1.com,https://blog2.com" --keywords "AI,marketing" --output summary
# Force Apify for JS-heavy sites
python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
--urls "https://example.com" --mode apify
<link rel="alternate"> tags/feed, /rss, /atom.xml, /feed.xml, /rss.xml, /blog/feed, /index.xmljupri/rss-xml-scraper (if token available)Note: The Apify fallback actor
jupri/rss-xml-scrapermay need updating -- it has not been verified recently. RSS mode works reliably without it.
Only tries RSS feeds, no Apify fallback.
Uses Apify actor directly, skipping RSS discovery.
| Flag | Default | Description |
|---|---|---|
--urls | required | Blog URL(s), comma-separated |
--keywords | none | Keywords to filter (comma-separated, OR logic) |
--days | 30 | Only include posts from last N days |
--max-posts | 50 | Max posts to return |
--mode | auto | auto (RSS + fallback), rss (RSS only), apify (Apify only) |
--output | json | Output format: json or summary |
--token | env var | Apify token (only needed for Apify mode/fallback) |
--timeout | 300 | Max seconds for Apify run |
jupri/rss-xml-scraper -- minimal Apify credits아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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