Extract speaker names, titles, companies, and bios from conference websites. Supports direct HTML scraping and Apify web scraper fallback for JS-heavy sites. Use for pre-event research and outreach targeting.
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Extract speaker names, titles, companies, and bios from conference website /speakers pages. Supports direct HTML scraping with multiple extraction strategies, plus Apify fallback for JS-heavy sites.
No API key needed for direct scraping mode.
# Scrape speakers from a conference page
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py \
--url "https://example.com/speakers"
# Use Apify for JS-heavy sites
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py \
--url "https://example.com/speakers" --mode apify
# Custom conference name (otherwise inferred from URL)
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py \
--url "https://example.com/speakers" --conference "Sage Future 2026"
# Output formats
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py --url URL --output json # default
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py --url URL --output csv
python3 skills/conference-speaker-scraper/scripts/scrape_speakers.py --url URL --output summary
Fetches the page HTML and tries multiple extraction strategies in order, using whichever returns the most results:
<h2>/<h3> + <p> structures<script type="application/ld+json"> with speaker dataUses apify/cheerio-scraper actor with a custom page function that targets common speaker card selectors. Standard POST/poll/GET dataset pattern.
| Flag | Default | Description |
|---|---|---|
--url | required | Conference speakers page URL |
--conference | inferred | Conference name (otherwise inferred from URL domain) |
--mode | direct | direct (HTML scraping) or apify (Apify cheerio scraper) |
--output | json | Output format: json, csv, or summary |
--token | env var | Apify token (only needed for apify mode) |
--timeout | 300 | Max seconds for Apify run |
{
"name": "Jane Smith",
"title": "VP of Finance",
"company": "Acme Corp",
"bio": "Jane leads the finance transformation at...",
"linkedin_url": "https://linkedin.com/in/janesmith",
"image_url": "https://...",
"conference": "Sage Future 2026",
"source_url": "https://sagefuture2026.com/speakers"
}
apify/cheerio-scraper -- minimal Apify creditsHTML scraping is inherently fragile across conference sites. The multi-strategy approach maximizes coverage, but JS-heavy sites will require Apify mode. When direct scraping returns 0 results, try --mode apify.
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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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