Search Hacker News stories and comments using the free Algolia API. No Apify token needed. Use when you need to find HN discussions, track mentions, discover Show HN launches, or monitor tech community sentiment.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Search Hacker News using the free Algolia HN Search API. No Apify token or API key needed.
Only dependency: pip install requests.
# Stories about AI content marketing in last week
python3 skills/hacker-news-scraper/scripts/search_hn.py \
--query "AI content marketing" --days 7
# Show HN posts in last month (summary view)
python3 skills/hacker-news-scraper/scripts/search_hn.py \
--query "" --tags show_hn --days 30 --output summary
# Comments mentioning a specific tool
python3 skills/hacker-news-scraper/scripts/search_hn.py \
--query "LangChain" --tags comment --days 14 --max-results 20
search_by_date endpoint)numericFilters=created_at_i>{unix_timestamp} for server-side date filtering| Flag | Default | Description |
|---|---|---|
--query | required | Search query |
--days | 7 | How many days back to search |
--tags | story | Item type: story, comment, ask_hn, show_hn |
--max-results | 50 | Max results to return |
--keywords | none | Additional filter keywords (comma-separated, OR logic) |
--output | json | Output format: json or summary |
{
"id": "12345678",
"title": "Show HN: My new tool",
"url": "https://example.com",
"author": "username",
"points": 42,
"num_comments": 15,
"created_at": "2026-02-18T12:00:00.000Z",
"hn_url": "https://news.ycombinator.com/item?id=12345678",
"text": ""
}
Free. No API key, no rate limits (within reason), no Apify credits.
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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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