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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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
agentmail to automate initial outreach to newsletter operatorscompany-contact-finder when a newsletter's sponsorship contact is not publicly listedまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
日本語の概要は準備中です。原文の説明を表示しています。