Affiliation Scrape

Scrape and document affiliations for: $ARGUMENTS

$ARGUMENTS should include:

• The person's full name (required): e.g. "Alan Hirsch"
• Optionally: their primary domain or website: e.g. --site alanhirsch.org
• Optionally: a category filter: --category publisher,speaking,media
• Optionally: an output path: --output content-library/affiliations/alan-hirsch.json
• Empty — ask the user for the person's name

Before Starting

1. Confirm you have browser tools available (mcp__chrome-devtools)
2. Check content-library/affiliations/ for any existing affiliation file for this person
3. If an existing file is found, load it and run in incremental mode — add new orgs, don't overwrite confirmed data

Research Pipeline

Stage 1 — Seed Searches

Run 6–8 targeted web searches to discover affiliations. Use these query templates (replace {name}):

1. "{name}" publisher site:amazon.com OR site:goodreads.com OR site:booksellers.com
2. "{name}" speaking bureau OR keynote speaker
3. "{name}" endorsed by OR "foreword by" OR "endorsed by"
4. "{name}" conference keynote OR "main stage" OR plenary
5. "{name}" podcast interview OR "appeared on" OR "featured in"
6. "{name}" denomination OR church OR network OR movement
7. "{name}" seminary OR theological school OR university OR faculty
8. "{name}" "ministry partner" OR "strategic partner" OR affiliate
9. "{name}" site:{their-domain} — read the About / Speaking / Partners page directly
10. "{name}" award OR recognition OR honorary

For each search result, record:

• URL
• Organization name
• Relationship signal (e.g., "keynote speaker," "published by," "board member")
• Confidence level (HIGH / MEDIUM / LOW)

Stage 2 — Direct Site Crawl

If a personal website is known (or discoverable), navigate to it and read:

1. /about or /about-us — bio, affiliations mentioned in prose
2. /speaking or /speaking-bureau — which bureaus are listed
3. /books or /resources — publishers, endorsers, co-authors
4. /partners or /ministry — partner organizations
5. /press or /media — outlets that have featured them
6. Footer — often contains partner logos or links

Use WebFetch or mcp__chrome-devtools__navigate_page to read each page.

Extract any logo images found (note URL, alt text, surrounding anchor link — this is logo strip gold).

Stage 3 — Classify Each Organization

For every discovered organization, classify it using these categories:

Stage 4 — Logo Research

For each confirmed organization, attempt to find a usable logo:

Priority order:

1. SVG from their website (/logo.svg, /images/logo.svg, footer SVG, <img> with logo in src)
2. High-res PNG from their website (look for @2x, 2x, hires, print variants)
3. Wikipedia/Wikimedia Commons SVG
4. Clearbit Logo API: https://logo.clearbit.com/{domain} (returns PNG)
5. Google Favicon as last resort (too small — flag as LOW quality)

For each logo found, record:

• logo_url — direct URL to the image
• logo_type — svg / png / favicon / not-found
• logo_quality — HIGH (SVG or hi-res PNG ≥ 200px) / MEDIUM (PNG 100–200px) / LOW (favicon or < 100px) / MISSING
• logo_background — transparent / white / dark / unknown (important for dark mode logo strips)

Stage 5 — Verify & Deduplicate

Before finalizing:

1. Remove duplicates (same org found via multiple searches)
2. Remove false positives (e.g., a conference where they attended but didn't speak)
3. Flag LOW confidence entries for user review
4. Check that every MEDIUM/HIGH confidence entry has a clear public record (URL as source)

Affiliation Data Schema

For each organization, produce a record matching this shape:

{
  "id": "baker-books",
  "name": "Baker Books",
  "short_name": "Baker",
  "category": "publisher",
  "website": "https://bakerpublishinggroup.com",
  "relationship_type": "author",
  "relationship_description": "Primary publisher for Alan Hirsch's books including The mDNA and 5Q. Baker is one of the leading evangelical academic publishers.",
  "relationship_start_year": 2006,
  "books_published": ["The mDNA", "The Permanent Revolution", "5Q"],
  "logo_url": "https://bakerpublishinggroup.com/images/baker-logo.svg",
  "logo_type": "svg",
  "logo_quality": "HIGH",
  "logo_background": "white",
  "source_urls": ["https://bakerpublishinggroup.com/authors/alan-hirsch"],
  "confidence": "HIGH",
  "include_in_strip": true,
  "strip_group": "publishers",
  "strip_label": "Published by",
  "prominence_score": 10,
  "notes": ""
}

Field definitions:

• id — kebab-case slug, unique
• name — full legal/official name
• short_name — abbreviated name for logo strip labels
• category — from the category table above
• relationship_type — author, speaker, faculty, board, partner, alumni, endorsed-by, media-featured
• relationship_description — 1–3 sentences: what is this relationship, why does it matter to the person's credibility
• relationship_start_year — approximate year the relationship began (omit if unknown)
• logo_url — direct URL to best available logo
• logo_type — svg / png / favicon / not-found
• logo_quality — HIGH / MEDIUM / LOW / MISSING
• logo_background — transparent / white / dark / unknown
• source_urls — array of URLs confirming the relationship
• confidence — HIGH (direct evidence) / MEDIUM (strongly implied) / LOW (inferred)
• include_in_strip — boolean, set to true for HIGH confidence orgs with usable logos
• strip_group — one of: publishers, speaking, media, networks, endorsers, academic
• strip_label — short label for the group header in the logo strip (e.g., "Published by", "As seen in")
• prominence_score — 1–10, how much credibility this relationship adds (10 = major publisher/conference)
• notes — any caveats, ambiguities, or things to verify

Output Format

Save results to content-library/affiliations/{person-slug}.json (create directory if needed).

Then print a report:

## Affiliation Scrape Report: [Person Name]

### Summary
- Organizations found: N
- HIGH confidence: X
- MEDIUM confidence: Y
- LOW confidence: Z (review needed)
- Strip-ready (include_in_strip = true): N

### By Category
| Category | Count | Strip-ready | Logo quality |
|----------|-------|-------------|-------------|
| publisher | 3 | 3 | HIGH |
| speaking-bureau | 2 | 2 | HIGH |
| conference | 8 | 5 | MEDIUM |
| media | 6 | 4 | MEDIUM |
| network | 4 | 3 | HIGH |
| academic | 2 | 1 | LOW |
| ministry-partner | 5 | 2 | MEDIUM |

### Strip Groups (ordered by prominence)
**publishers** — "Published by" — 3 orgs
**networks** — "Part of" — 3 orgs
**speaking** — "Available to speak" — 2 orgs
**media** — "As featured in" — 4 orgs
**conference** — "Keynote speaker" — 5 orgs

### Needs Review (LOW confidence or MISSING logo)
| Org | Issue | Action needed |
|-----|-------|--------------|
| Example Conference | LOW confidence — only indirect mention found | Verify via speaker page or personal site |
| Example Network | MISSING logo — no SVG or PNG found | Manual logo download needed |

### Next Steps
1. Run `/affiliation-audit {person-slug}` to evaluate strip readiness and grouping
2. Resolve flagged LOW confidence entries manually
3. Download any MISSING logos to `public/images/orgs/`
4. Run `/logo-strip-author {person-slug}` to generate the final section

Key Rules

• Public record required — Every HIGH/MEDIUM confidence entry must have a source_urls entry proving the relationship
• No fabrication — If you cannot verify an affiliation from public sources, set confidence to LOW and flag it
• Logos from authoritative sources only — Don't grab logos from third-party aggregators; prefer official org websites
• Note permissions ambiguity — Logo use for social proof is generally considered nominative fair use, but flag any org that may object
• Idempotent — If re-run, merge new findings with existing file; never overwrite confirmed HIGH confidence entries
• Prominence scoring — A major publisher (Baker, Zondervan) scores 10; a regional conference scores 3–5