Find newsletters relevant to a target audience/industry for sponsorship opportunities. Discovers newsletters through web search, newsletter directories, and industry research. Returns newsletter name, author, estimated audience, topic focus, sponsorship rates (if available), and contact info.
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Find and rank newsletters for sponsorship opportunities targeting a specific ICP. Uses web search, newsletter directories, and competitor intelligence to build a prioritized list of sponsorship targets.
Find newsletter sponsorship opportunities for [client]. Target audience: [description]. Industry keywords: [keywords].
Or with optional filters:
Find newsletter sponsorship opportunities for [client].
Target audience: CTOs and DevOps engineers at startups.
Industry keywords: cloud, AWS, DevOps, infrastructure, FinOps.
Budget: $500-2000/placement.
Geographic focus: US.
clients/<client>/leads/newsletter-sponsorships-YYYY-MM-DD.mdFree — all discovery is WebSearch-based. No API keys required.
pip3 install requests
Optional helper script for Substack directory search:
python3 skills/newsletter-sponsorship-finder/scripts/search_newsletters.py --keywords "cloud,AWS,DevOps" --output json
Accept from user:
Run these searches:
"[industry] newsletter""[industry] weekly newsletter developer""best newsletters for [target audience]""[industry] newsletter sponsorship""advertise in [industry] newsletter"Search Swapstack/Paved/SparkLoop for relevant newsletters:
"site:swapstack.co [industry]""site:paved.com [industry]""[industry] blog" and "[industry] content creator" to find people who likely also have newsletters"[industry] newsletter" site:linkedin.com posts"site:substack.com [industry keywords]"python3 skills/newsletter-sponsorship-finder/scripts/search_newsletters.py --keywords "[keywords]" --output json"[competitor name] sponsor newsletter" or "[competitor name] advertise"For each discovered newsletter, use WebFetch to visit the newsletter page and try to find:
Score each newsletter (0-10):
Save results to the specified output path as markdown:
# Newsletter Sponsorship Opportunities
**Target audience:** [description]
**Industry:** [keywords]
**Date:** YYYY-MM-DD
## Tier 1: Must-Sponsor (Score 8+)
| Newsletter | Author | Est. Audience | Frequency | Sponsorship Rate | Contact | Score |
|-----------|--------|--------------|-----------|-----------------|---------|-------|
## Tier 2: Strong Fit (Score 5-7)
| Newsletter | Author | Est. Audience | Frequency | Sponsorship Rate | Contact | Score |
|-----------|--------|--------------|-----------|-----------------|---------|-------|
## Tier 3: Worth Exploring (Score 3-4)
| Newsletter | Author | Est. Audience | Frequency | Sponsorship Rate | Contact | Score |
|-----------|--------|--------------|-----------|-----------------|---------|-------|
## Competitor Sponsorship Intel
| Competitor | Newsletters They Sponsor | Notes |
|-----------|------------------------|-------|
## Next Steps
1. Reach out to Tier 1 newsletters for rate cards
2. Request media kits from Tier 2 newsletters
3. Set calendar reminder to refresh this list quarterly
4. Monitor competitor sponsorships monthly
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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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