yc-batch-evaluator

YC Batch Evaluator

Setup

Read your credentials from ~/.gooseworks/credentials.json:

export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login

All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"

Scrape a YC batch, research every company and founder, assess founder-company fit, and export a live-updating Google Sheet with priority rankings. Designed for investors evaluating YC companies.

IMPORTANT: Do NOT ask clarifying questions. Just start immediately.

All inputs are optional. If the user said a batch, use it. If they didn't specify sectors or thesis, process ALL companies. Always create a new Google Sheet — never ask for an existing spreadsheet ID. Start scraping immediately — do not ask "which batch?", "any sector filters?", or "should I create a sheet?". This is designed for live demos where speed and visual impact matter.

"Spring 2026" is a real YC batch (also called "X26"). It exists and has ~22 companies.

Input

• batch (optional) — defaults to "Spring 2026". Examples: "Winter 2026", "Summer 2025"
• sectors (optional) — filter to specific sectors (e.g. "AI", "fintech", "infrastructure"). If not provided, process ALL companies.
• thesis (optional) — investor's focus areas for tailored scoring and ranking. If not provided, rank on general investment quality.

Step 1: Scrape the YC Batch Directory

The YC companies page is JavaScript-rendered (Algolia-powered). Scrapegraph handles the JS rendering.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://www.ycombinator.com/companies?batch={batch_url_encoded}",
  "user_prompt": "Extract every company listed: company name, one-line description, sector/tags, location, and URL slug for each company page (e.g. /companies/orthogonal). Return as a structured list."
}'

Batch URL encoding: "Spring 2026" → Spring%202026, "Winter 2026" → Winter%202026.

If the investor specified sectors, filter the list. Otherwise process all companies.

Expected response structure:

{
  "result": {
    "companies": [
      {
        "name": "Indexable",
        "description": "sandbox infrastructure for AI agents",
        "tags": ["B2B", "Infrastructure"],
        "location": "San Francisco, CA, USA",
        "url_slug": "/companies/indexable"
      }
    ]
  }
}

Note: The batch page returns tags (e.g. "B2B", "Infrastructure"), NOT detailed sectors. Individual YC pages (Step 3a) return richer sectors (e.g. "Artificial Intelligence", "Manufacturing"). Always prefer individual page data when available.

Step 2: Create Google Sheet and Share Link Immediately

Before any research, create the sheet and populate it with company names + descriptions from the batch scrape. Share the link so the investor can watch results fill in live.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"google-sheets","path":"/create-spreadsheet","body":{"title":"YC {batch} Batch Evaluation"}}'

Column layout (A through N):

| Col | Header | Source | |-----|--------|--------| | A | Company | Batch scrape | | B | Description | Batch scrape | | C | Sector | Individual YC page (sectors) — overwrite batch tags | | D | Location | Individual YC page → Apollo fallback | | E | Website | Individual YC page (website_url) | | F | Founders | Individual YC page (names + titles) | | G | Founder LinkedIn(s) | Individual YC page (linkedin_url) | | H | Founder Twitter/X | Individual YC page (twitter_url) | | I | Founder Background | Apollo employment history + YC page bios | | J | Founder-Company Fit | Your assessment | | K | Website Analysis | Company website scrape | | L | Market/Competitors | Perplexity | | M | Overall Assessment | Your assessment | | N | Priority Rank | Your ranking |

Write header row + all company rows (research columns blank):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"google-sheets","path":"/update-values"}'
  "spreadsheet_id": "{spreadsheet_id}",
  "sheet_name": "Sheet1",
  "first_cell_location": "A1",
  "valueInputOption": "USER_ENTERED",
  "values": [
    ["Company", "Description", "Sector", "Location", "Website", "Founders", "Founder LinkedIn(s)", "Founder Twitter/X", "Founder Background", "Founder-Company Fit", "Website Analysis", "Market/Competitors", "Overall Assessment", "Priority Rank"],
    ["{company_name}", "{description}", "{tags}", "{location}", "", "", "", "", "", "", "", "", "", ""]
  ]
}'

Populate Sector (C) and Location (D) from the batch scrape initially — they'll be overwritten with richer data from the individual YC pages.

Share the sheet link with the user immediately so they can watch it fill in.

Step 3: Research Each Company — Row by Row

Parallelization Strategy

The demo effect matters. Rows filling in one-by-one on the spreadsheet is the visual payoff. Optimize for a steady stream of rows appearing — not for dumping everything at once.

Process companies in batches of 3-5 at a time. Within each batch, all companies' research runs in parallel. But update each row individually the moment that company's research completes — do NOT wait for the whole batch to finish before writing.

For each batch of companies (run all in parallel):

1. Scrape all YC company pages in the batch simultaneously (Step 3a)
2. As each YC page returns, immediately launch its downstream calls in parallel:
3. Scrape the company's website (Step 3b)
4. Apollo lookup for each founder (Step 3c) — multiple calls if multiple founders, all in parallel
5. Perplexity market analysis (Step 3d)
6. As each company's full research set completes, compile and update its row (Step 4) immediately — one update-values call per row, don't batch them
7. Move to the next batch

Key: each row gets its own sheet update call. This creates the live-fill effect where the investor watches rows appear every 3-5 seconds. Never batch multiple rows into a single sheet write — that kills the visual cadence.

With batches of 5, a 22-company batch completes in ~2-3 minutes with rows streaming in throughout.

3a. Scrape the YC company page (~$0.03 each)

YC company pages are server-rendered and contain rich data: founders with LinkedIn, Twitter/X, bios, team size, sectors, website.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://www.ycombinator.com/companies/{company_slug}",
  "user_prompt": "Extract: full company description, all founders (full name, title, LinkedIn URL, Twitter/X URL, bio), company website URL, team size, location, sectors, founding year."
}'

Expected response structure:

{
  "result": {
    "founders": [
      {
        "full_name": "William Alexander",
        "title": "Founder",
        "linkedin_url": "https://www.linkedin.com/in/william--alexander/",
        "twitter_url": null,
        "bio": "Manufacturing nerd from Iowa. Stanford Econ + CS."
      },
      {
        "full_name": "Tom Blomfield",
        "title": "Primary Partner",
        "linkedin_url": null,
        "twitter_url": null,