Content Opportunity Intelligence

Find timely content ideas, public trend signals, research signals, and evidence links for your editorial plan. Start with Google News, then add Hacker News, Stack Overflow, Bluesky, or curated publisher feeds when you need technical, community, and publisher angles. Receive ranked opportunities for content research, newsletters, blogs, and marketing planning.

How it works in 30 seconds

1. Enter a topic, such as AI agents for customer support.
2. Choose the country, time range, and public sources to include.
3. Run the Actor and review the ranked ideas, source links, and opportunity scores in the dataset.

The default input searches Google News for AI agents over the last seven days and returns up to five opportunities. Add optional sources or raise the limit after reviewing the first dataset when you want a wider technical or research view.

Input guide

Input	What it means
Topic or keyword	What you want content ideas about.
News country	The country used to localize Google News results. Other sources are global.
Lookback period	How far back to search, from 1 to 30 days.
Sources	Google News, Hacker News, Stack Overflow, Bluesky posts, curated publisher feeds, or any combination.
Maximum opportunities	The maximum number of ranked ideas to return.
Show only stronger opportunities (optional)	Leave at 0 to see all results. Raise it to hide lower-priority ideas.

Start with this input

{
  "query": "AI agents for customer support",
  "country": "US",
  "lookbackDays": 7,
  "sources": ["googleNews"],
  "maxItems": 5,
  "minTrendScore": 0
}

What you get

Each row is one content opportunity with a title, source, publication date, evidence link, short source summary, and opportunity score.

{
  "itemType": "content_opportunity",
  "title": "Encore AI raises $30M to build AI agents that learn from customer calls - TechCrunch",
  "sourceName": "TechCrunch",
  "publishedAt": "2026-07-29T14:41:06.000Z",
  "url": "https://news.google.com/rss/articles/CBMiqAFBVV95cUxQZVM2M2g4bFd4X0VlNzNTY3Z2TE9hVEsxSk1Rd2o0VFQ5M1VNb25VNVRsd0pMbUNPNVowbGQxa2R4VU5QYWxhREE4WlF2ajJ3SlJaSVlTSV9LMTh5N0ViZkJ2V2JDcFZpcUxOQUU1UzFYd0JfMXBmOXlLb283dXcycldnSUl1VUVZWkd3WGpPR0pPaFhMcnlKVnl4Y1QzNDdzVGZTVkd1R20?oc=5",
  "summary": "Encore AI raises $30M to build AI agents that learn from customer calls TechCrunch",
  "trendScore": 86.04,
  "matchedQuery": "AI agents for customer support"
}

How to read the opportunity score

The opportunity score helps you decide what to inspect first. It favors newer content and uses source-relative engagement when a source provides it. It is a prioritization signal, not a prediction of traffic, revenue, or virality.

This verified example came from the private 0.2.1 candidate. For integrations, each row also includes canonicalUrl, dedupeKey, scoreVersion, scoreComponents, and contributingSources. These fields help with deduplication and automation; most users can focus on the default dataset view.

Best use cases

• Find timely newsletter, blog, and social-content ideas.
• Monitor category, competitor, or product coverage.
• Build a lightweight trend-discovery or editorial-planning dashboard.
• Collect evidence links before writing a content brief.

Sources, data use, and limitations

This version queries the public Google News RSS search feed, Hacker News Algolia API, Stack Overflow API, Bluesky public AppView, and a small allowlist of publisher RSS or Atom feeds. It keeps source links and attribution so you can verify each opportunity. Stack Overflow results are technical questions; Bluesky text is limited to 500 characters; publisher feeds are filtered by your topic and time range. It does not fetch destination articles, access paywalls, use browser sessions, or predict business outcomes.

Source availability and coverage can change. If one selected source is unavailable, the run returns available results from the other source and records the warning in the OUTPUT run summary. If every selected source fails, the run fails with an actionable diagnostic. An empty result is reported clearly as no_results. Optional research and publisher sources favour precise matches, so they may return no results for a narrow topic rather than add loosely related items.

This is an unofficial tool and is not affiliated with, endorsed by, or sponsored by Google, Hacker News, Algolia, Stack Overflow, Stack Exchange, Bluesky, BBC, The Verge, TechCrunch, or their respective owners.

Pricing and support

The Actor uses straightforward pay-per-event pricing: $0.002 when a run starts and $0.0002 for each content opportunity returned ($0.20 per 1,000 opportunities). The five-opportunity default costs up to approximately $0.003. Check the Store estimate before starting a run, especially after raising the result limit or adding sources. Platform usage is included in these prices, so there is no separate usage charge from this Actor.

For questions, feedback, or source-correction requests, use the Actor's Apify Issues tab. Release notes are maintained with each published build.

Python API

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("thescrapelab/content-opportunity-intelligence").call(run_input={
    "query": "AI agents for customer support",
    "country": "US",
    "lookbackDays": 7,
    "sources": ["googleNews"],
    "maxItems": 5,
    "minTrendScore": 0,
})

items = client.dataset(run["defaultDatasetId"]).list_items().items
for item in items:
    print(item["title"], item["trendScore"])