Claude Skill

newsletter-sponsorship-finder

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

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Download gooseworks-ai-goose-skills-skills_ads_capabilities_newsletter-sponsorship-finder-e1592ee.zip · 4 KB
gooseworks-ai/goose-skills 1222 208 forks MIT Updated 3d ago
Part of gooseworks-ai/goose-skills — 44 skills

Install

skills CLI npx skills add https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/newsletter-sponsorship-finder
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install gooseworks-ai-goose-skills@llmmart
Git git clone https://github.com/gooseworks-ai/goose-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole gooseworks-ai/goose-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Newsletter Sponsorship Finder

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.

Quick Start

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.

Inputs

  • Target audience description (required) — e.g., "CTOs and DevOps engineers at startups"
  • Industry keywords (required) — e.g., "cloud, AWS, DevOps, infrastructure, FinOps"
  • Budget range (optional) — for filtering newsletters by sponsorship cost
  • Geographic focus (optional) — e.g., "US", "Europe", "Global"
  • Output path (optional) — where to save results, defaults to clients/<client>/leads/newsletter-sponsorships-YYYY-MM-DD.md

Cost

Free — all discovery is WebSearch-based. No API keys required.

Dependencies

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

Process

Phase 1: Define Target

Accept from user:

  • Target audience description (e.g., "CTOs and DevOps engineers at startups")
  • Industry keywords (e.g., "cloud, AWS, DevOps, infrastructure, FinOps")
  • Budget range (optional, for filtering)
  • Geographic focus (optional)

Phase 2: Discovery (run searches in parallel)

A) Direct newsletter search (WebSearch)

Run these searches:

  • "[industry] newsletter"
  • "[industry] weekly newsletter developer"
  • "best newsletters for [target audience]"
  • "[industry] newsletter sponsorship"
  • "advertise in [industry] newsletter"

B) Newsletter directory search

Search Swapstack/Paved/SparkLoop for relevant newsletters:

  • "site:swapstack.co [industry]"
  • "site:paved.com [industry]"
  • WebFetch on directory result pages to find listings in the target niche

C) Industry-specific discovery

  • Search for "[industry] blog" and "[industry] content creator" to find people who likely also have newsletters
  • Search for "[industry] newsletter" site:linkedin.com posts
  • Search for Substack newsletters: "site:substack.com [industry keywords]"
  • Optionally run the helper script: python3 skills/newsletter-sponsorship-finder/scripts/search_newsletters.py --keywords "[keywords]" --output json

D) Competitor sponsorship research

  • Search "[competitor name] sponsor newsletter" or "[competitor name] advertise"
  • Check competitor websites for "As seen in" or press pages
  • This reveals which newsletters competitors already sponsor (proven audience match)

Phase 3: Enrich Each Newsletter

For each discovered newsletter, use WebFetch to visit the newsletter page and try to find:

  1. Name — Newsletter name
  2. Author/Organization — Who runs it
  3. URL — Signup page or archive
  4. Estimated audience — subscriber count (often mentioned on sponsorship pages or About pages)
  5. Topic focus — What it covers
  6. Frequency — Daily, weekly, monthly
  7. Sponsorship info — Rates, format (dedicated send, banner, classified), contact
  8. Audience quality — Is the audience primarily decision-makers or junior folks?
  9. Social proof — Notable sponsors, testimonials

Phase 4: Score & Rank

Score each newsletter (0-10):

  • Audience overlap with target ICP (+3 max)
  • Audience size (+2 for 10K+, +1 for 5K+)
  • Sponsorship availability confirmed (+2)
  • Reasonable pricing for budget (+1)
  • High engagement signals — open rates mentioned, active community (+1)
  • Competitors sponsor it — proven audience match (+1)

Phase 5: Output

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

Tips

  • Run once per client to establish a sponsorship pipeline
  • Refresh quarterly as new newsletters launch frequently
  • Check competitor sponsorships monthly — if a competitor starts sponsoring a newsletter, it validates the audience
  • Combine with agentmail to automate initial outreach to newsletter operators
  • Use company-contact-finder when a newsletter's sponsorship contact is not publicly listed
  • Newsletters with 5K-50K subscribers often offer the best ROI for B2B sponsorships — large enough audience, small enough for personal touch
Files (goose-skills)
  • scripts
    • search_newsletters.py 5.1 KB 
      #!/usr/bin/env python3
"""
search_newsletters.py — Helper script for the newsletter-sponsorship-finder skill.

Searches the Substack directory for newsletters matching given keywords and
returns structured results. This is a supplementary tool; the main discovery
is done by the agent using WebSearch and WebFetch.

Usage:
    python3 search_newsletters.py --keywords "cloud,AWS,DevOps,infrastructure" --output json
    python3 search_newsletters.py --keywords "fintech,banking" --output table
"""

import argparse
import json
import sys

try:
    import requests
except ImportError:
    print(
        json.dumps(
            {
                "error": "requests library not installed. Run: pip3 install requests",
                "results": [],
            }
        )
    )
    sys.exit(1)


SUBSTACK_SEARCH_URL = "https://substack.com/api/v1/publication/search"

HEADERS = {
    "User-Agent": (
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
        "AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
    ),
    "Accept": "application/json",
}


def search_substack(keyword: str, limit: int = 20) -> list[dict]:
    """Search Substack for newsletters matching a keyword."""
    try:
        resp = requests.get(
            SUBSTACK_SEARCH_URL,
            params={"query": keyword, "page": 0, "limit": limit},
            headers=HEADERS,
            timeout=15,
        )
        resp.raise_for_status()
        data = resp.json()

        results = []
        publications = data if isinstance(data, list) else data.get("results", data.get("publications", []))

        for pub in publications:
            if isinstance(pub, dict):
                results.append(
                    {
                        "name": pub.get("name", "Unknown"),
                        "author": pub.get("author_name", pub.get("author", {}).get("name", "Unknown")),
                        "description": pub.get("description", pub.get("hero_text", "")),
                        "url": pub.get("custom_domain") or pub.get("custom_domain_optional") or f"https://{pub.get('subdomain', 'unknown')}.substack.com",
                        "subscribers": pub.get("subscriber_count", pub.get("rankingDetail", {}).get("subscribers", "N/A")),
                        "type": pub.get("type", "newsletter"),
                        "keyword": keyword,
                    }
                )
        return results

    except requests.exceptions.HTTPError as e:
        if e.response is not None and e.response.status_code == 403:
            return [{"note": f"Substack blocked the request for '{keyword}'. Try WebSearch instead.", "keyword": keyword, "results": []}]
        return [{"error": f"HTTP error searching for '{keyword}': {e}", "keyword": keyword, "results": []}]
    except requests.exceptions.RequestException as e:
        return [{"error": f"Request failed for '{keyword}': {e}", "keyword": keyword, "results": []}]
    except (json.JSONDecodeError, KeyError, TypeError) as e:
        return [{"error": f"Failed to parse Substack response for '{keyword}': {e}", "keyword": keyword, "results": []}]


def format_table(results: list[dict]) -> str:
    """Format results as a readable table."""
    if not results:
        return "No results found."

    lines = [
        f"{'Name':<40} {'Author':<25} {'Subscribers':<15} {'URL'}",
        "-" * 120,
    ]
    for r in results:
        if "error" in r or "note" in r:
            lines.append(r.get("error", r.get("note", "")))
            continue
        name = (r.get("name", "Unknown"))[:38]
        author = (r.get("author", "Unknown"))[:23]
        subs = str(r.get("subscribers", "N/A"))[:13]
        url = r.get("url", "")
        lines.append(f"{name:<40} {author:<25} {subs:<15} {url}")

    return "\n".join(lines)


def main():
    parser = argparse.ArgumentParser(
        description="Search Substack for newsletters matching keywords."
    )
    parser.add_argument(
        "--keywords",
        required=True,
        help="Comma-separated keywords to search for (e.g., 'cloud,AWS,DevOps')",
    )
    parser.add_argument(
        "--output",
        choices=["json", "table"],
        default="json",
        help="Output format: json or table (default: json)",
    )
    parser.add_argument(
        "--limit",
        type=int,
        default=20,
        help="Max results per keyword (default: 20)",
    )
    args = parser.parse_args()

    keywords = [k.strip() for k in args.keywords.split(",") if k.strip()]

    all_results = []
    seen_urls = set()

    for kw in keywords:
        results = search_substack(kw, limit=args.limit)
        for r in results:
            url = r.get("url", "")
            if url and url not in seen_urls:
                seen_urls.add(url)
                all_results.append(r)
            elif "error" in r or "note" in r:
                all_results.append(r)

    if args.output == "json":
        print(json.dumps({"keywords": keywords, "total": len(all_results), "results": all_results}, indent=2))
    else:
        print(f"\nSubstack Newsletter Search: {', '.join(keywords)}")
        print(f"Found {len(all_results)} results\n")
        print(format_table(all_results))


if __name__ == "__main__":
    main()
  • SKILL.md 6 KB 
    ---
name: newsletter-sponsorship-finder
description: >
  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.
---

# Newsletter Sponsorship Finder

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.

## Quick Start

```
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.
```

## Inputs

- **Target audience description** (required) — e.g., "CTOs and DevOps engineers at startups"
- **Industry keywords** (required) — e.g., "cloud, AWS, DevOps, infrastructure, FinOps"
- **Budget range** (optional) — for filtering newsletters by sponsorship cost
- **Geographic focus** (optional) — e.g., "US", "Europe", "Global"
- **Output path** (optional) — where to save results, defaults to `clients/<client>/leads/newsletter-sponsorships-YYYY-MM-DD.md`

## Cost

Free — all discovery is WebSearch-based. No API keys required.

## Dependencies

```
pip3 install requests
```

Optional helper script for Substack directory search:

```bash
python3 skills/newsletter-sponsorship-finder/scripts/search_newsletters.py --keywords "cloud,AWS,DevOps" --output json
```

## Process

### Phase 1: Define Target

Accept from user:
- Target audience description (e.g., "CTOs and DevOps engineers at startups")
- Industry keywords (e.g., "cloud, AWS, DevOps, infrastructure, FinOps")
- Budget range (optional, for filtering)
- Geographic focus (optional)

### Phase 2: Discovery (run searches in parallel)

#### A) Direct newsletter search (WebSearch)

Run these searches:
- `"[industry] newsletter"`
- `"[industry] weekly newsletter developer"`
- `"best newsletters for [target audience]"`
- `"[industry] newsletter sponsorship"`
- `"advertise in [industry] newsletter"`

#### B) Newsletter directory search

Search Swapstack/Paved/SparkLoop for relevant newsletters:
- `"site:swapstack.co [industry]"`
- `"site:paved.com [industry]"`
- WebFetch on directory result pages to find listings in the target niche

#### C) Industry-specific discovery

- Search for `"[industry] blog"` and `"[industry] content creator"` to find people who likely also have newsletters
- Search for `"[industry] newsletter" site:linkedin.com` posts
- Search for Substack newsletters: `"site:substack.com [industry keywords]"`
- Optionally run the helper script: `python3 skills/newsletter-sponsorship-finder/scripts/search_newsletters.py --keywords "[keywords]" --output json`

#### D) Competitor sponsorship research

- Search `"[competitor name] sponsor newsletter"` or `"[competitor name] advertise"`
- Check competitor websites for "As seen in" or press pages
- This reveals which newsletters competitors already sponsor (proven audience match)

### Phase 3: Enrich Each Newsletter

For each discovered newsletter, use WebFetch to visit the newsletter page and try to find:

1. **Name** — Newsletter name
2. **Author/Organization** — Who runs it
3. **URL** — Signup page or archive
4. **Estimated audience** — subscriber count (often mentioned on sponsorship pages or About pages)
5. **Topic focus** — What it covers
6. **Frequency** — Daily, weekly, monthly
7. **Sponsorship info** — Rates, format (dedicated send, banner, classified), contact
8. **Audience quality** — Is the audience primarily decision-makers or junior folks?
9. **Social proof** — Notable sponsors, testimonials

### Phase 4: Score & Rank

Score each newsletter (0-10):
- Audience overlap with target ICP (+3 max)
- Audience size (+2 for 10K+, +1 for 5K+)
- Sponsorship availability confirmed (+2)
- Reasonable pricing for budget (+1)
- High engagement signals — open rates mentioned, active community (+1)
- Competitors sponsor it — proven audience match (+1)

### Phase 5: Output

Save results to the specified output path as markdown:

```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
```

## Tips

- Run once per client to establish a sponsorship pipeline
- Refresh quarterly as new newsletters launch frequently
- Check competitor sponsorships monthly — if a competitor starts sponsoring a newsletter, it validates the audience
- Combine with `agentmail` to automate initial outreach to newsletter operators
- Use `company-contact-finder` when a newsletter's sponsorship contact is not publicly listed
- Newsletters with 5K-50K subscribers often offer the best ROI for B2B sponsorships — large enough audience, small enough for personal touch
  • skill.meta.json 283 B 
    {
  "slug": "newsletter-sponsorship-finder",
  "category": "capabilities",
  "tags": [
    "monitoring"
  ],
  "installation": {
    "base_command": "npx goose-skills install newsletter-sponsorship-finder",
    "supports": [
      "claude",
      "cursor",
      "codex"
    ]
  }
}

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