Orthogonal 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):

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:
   • Scrape the company's website (Step 3b)
   • Apollo lookup for each founder (Step 3c) — multiple calls if multiple founders, all in parallel
   • Perplexity market analysis (Step 3d)
3. 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
4. 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,
        "bio": null
      }
    ],
    "website_url": "https://arzana.ai",
    "location": "San Francisco, CA, US",
    "sectors": ["Artificial Intelligence", "Manufacturing"],
    "team_size": 4,
    "founding_year": 2025
  }
}

Critical parsing rules:

1. Filter out YC partners: Entries with title "Primary Partner" or "Group Partner" are YC staff (e.g. Tom Blomfield, Harj Taggar), NOT founders. Exclude them.

2. Website field name varies: Check website_url, company_website_url, and company_website — Scrapegraph returns different field names depending on the page.

3. Sectors field name varies: Check sectors, sector_tags, and tags. Prefer the individual page's sectors over the batch page's tags — individual pages return specific sectors like "Artificial Intelligence" vs generic tags like "B2B".

4. LinkedIn URL may be null: Some founders don't have LinkedIn listed on YC. Use Apollo fallback (Step 3c) with name + company search.

5. Twitter/X URL may be null: Only populate if present, don't invent URLs.

3b. Scrape the company's own website (~$0.03 each)

Always run this step. Company websites have product details, pricing, customer logos, testimonials, and hiring signals that no other source provides.

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://{company_website}",
  "user_prompt": "Extract: what the product does, target customer, pricing model, key features, traction signals (customer logos, metrics, testimonials), hiring signals. Be specific about what you find."
}'

Expected response — varies by company, but typically includes:

{
  "result": {
    "product_description": "...",
    "target_customer": "..." or ["...", "..."],
    "pricing": "Not disclosed" or {"model": "...", "price": "..."},
    "key_features": ["...", "..."],
    "traction_signals": {
      "customer_logos": ["Heineken", "Toyota", "..."],
      "metrics": ["99.7% accuracy", "25K patients/day"],
      "testimonials": [{"author": "...", "quote": "..."}]
    },
    "hiring_signals": ["Careers page present"]
  }
}

Note: The response structure varies significantly between companies. The traction_signals field is sometimes called traction. Customer logos may be actual names or just "logos displayed but not identified". Pricing may be a string, object, or array. Parse flexibly.

If the scrape fails (404, timeout, empty), put "Website not available or pre-launch" in the Website Analysis column — never leave it blank.

3c. Apollo — founder work history (~$0.01 per founder)

Use the LinkedIn URL from the YC page to get full employment history. Run one call per founder.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/apollo/people/match \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "linkedin_url": "{founder_linkedin_url}",
  "reveal_personal_emails": true
}'

Key fields in the Apollo response:

{
  "person": {
    "name": "William Alexander",
    "headline": "Co-Founder & CEO, Arzana | Stanford Economics + CS",
    "city": "San Francisco",
    "state": "California",
    "employment_history": [
      {
        "organization_name": "Arzana",
        "title": "Co-Founder",
        "start_date": "2025-06-01",
        "end_date": null,
        "current": true
      },
      {
        "organization_name": "Previous Company",
        "title": "Engineer",
        "start_date": "2022-01-01",
        "end_date": "2025-05-01",
        "current": false
      }
    ]
  }
}

What to extract:

• person.employment_history[] — the key input for Founder Background and Founder-Company Fit. Look at organization_name, title, and dates.
• person.city + person.state — use as location fallback if the YC page didn't have a location.
• person.headline — often has a concise summary of their background.

No LinkedIn URL on YC page? Fallback — match by name + company:

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/apollo/people/match \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "first_name": "{first_name}",
  "last_name": "{last_name}",
  "organization_name": "{company_name}",
  "reveal_personal_emails": true
}'

Note: Use people/match with first_name, last_name, and organization_name as the fallback. This usually returns their employment history even without a LinkedIn URL.

3d. Perplexity — market context (~$0.005 each)

Include rich context in the prompt — company description and founder bios. Generic prompts like "tell me about {company}" return generic results.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"perplexity","path":"/chat/completions"}'
  "model": "sonar",
  "messages": [{"role": "user", "content": "{company_name} ({website}) is a YC {batch} startup: {description}. Founders: {founder_names_and_bios}. Answer concisely:
1. Market size and opportunity?
2. Top 3 competitors?
3. What makes the founders uniquely qualified?
4. Any traction, press, or notable mentions?
5. Red flags or concerns?"}]
}'

Response parsing: The answer is in choices[0].message.content as a text string. Extract the key points for the Market/Competitors column.

Step 4: Compile and Update Each Row

As each company's research completes, immediately update its row. The values array MUST have exactly 12 elements in this exact order:

values: [[
  C: sectors,           // e.g. "AI, Manufacturing" (from YC page)
  D: location,          // e.g. "San Francisco, CA" (from YC page, short)
  E: website,           // e.g. "https://arzana.ai" (plain URL)
  F: founders,          // e.g. "William Alexander (CEO)
Marshall Kools (COO)"
  G: linkedin_urls,     // e.g. "https://linkedin.com/in/william--alexander/
https://linkedin.com/in/marshallkools/"
  H: twitter_urls,      // e.g. "https://x.com/alexisaftalion" or ""
  I: background,        // Founder work history from Apollo
  J: fit_rating,        // "Strong — ..." or "Moderate — ..." or "Weak — ..."
  K: website_analysis,  // Summary of company website scrape
  L: market,            // Market size + competitors from Perplexity
  M: overall,           // "High Priority — ..." or "Interesting — ..." or "Pass — ..."
  N: ""                 // Priority Rank — leave blank, filled in Step 5
]]

CRITICAL: Exactly 12 values starting at column C. Do NOT include extra fields like company description, founding year, or team size — those go in the wrong columns and misalign the entire row.

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": "C{row_number}",
  "valueInputOption": "USER_ENTERED",
  "values": [["{sectors}", "{location}", "{website}", "{founders}", "{linkedins}", "{twitters}", "{background}", "{fit}", "{website_analysis}", "{market}", "{overall}", ""]]
}'

This updates columns C through N for a single row. One call per company — this creates the live-fill demo effect.

Formatting rules

Links — plain URLs, not HYPERLINK formulas

Google Sheets cannot have multiple =HYPERLINK() formulas in one cell — extras evaluate to FALSE. Instead:

• Website (E): Plain URL (e.g. https://arzana.ai). Sheets auto-linkifies it.
• Founder LinkedIn (G): One plain URL per line. If multiple founders, separate with newlines ( ).
• Founder Twitter/X (H): Same — one plain URL per line.

Founders (F): List as "Name (Title), Name (Title)". Example: "William Alexander (CEO), Marshall Kools (COO)"

Founder Background (I): One line per founder with key career highlights from Apollo employment history. Example:

"William Alexander: Stanford Econ+CS, previously founded Athluence and Lost in the Sauce Pizza. Marshall Kools: Stanford MS Engineering, D1 wrestler, previously at [company]."

Location (D): Use YC page location. If blank, use Apollo person.city, person.state from the first founder.

Sectors (C): Use individual YC page sectors (e.g. "Artificial Intelligence, Manufacturing"). If unavailable, fall back to batch tags.

Founder-Company Fit (J) — Strong / Moderate / Weak

Assess whether the founders' backgrounds make them uniquely suited to build THIS specific company. YC selects well, so most fits will be decent — but be specific about what makes it strong or where there are gaps.

Strong — Direct domain expertise or deep work history in the problem they're solving.

"Strong — CEO spent 5 years at Stripe building payment APIs, now building payment infrastructure. Deep domain match." "Strong — manufacturing nerd from Iowa, Stanford Econ+CS, building AI for manufacturing. Direct domain background."

Moderate — Strong technical background but limited domain experience, or relevant adjacent experience.

"Moderate — both founders are strong engineers (Google, Amazon) but no direct healthcare experience for a healthcare product." "Moderate — strong ML background from MIT/Caltech, but building for educators which is a different domain."

Weak — No relevant background for the space, or very thin visible track record.

"Weak — general engineering background with no visible agent infrastructure or protocol experience for an agent communication platform." "Weak — first-time founders, Apollo shows minimal work history, no clear domain expertise for the space."

Website Analysis (K)

Summarize what you found from the company website scrape. Be specific and include:

• What the product actually does (may be more detailed than YC one-liner)
• Target customer
• Pricing if disclosed
• Traction signals: customer logos by name, metrics, testimonials
• Hiring signals
• How mature the site/product looks

Examples from real scrapes:

"Strong product site. AI for manufacturing office — quoting, estimating, purchasing, CRM. $2.5-7K/mo pricing. 9 customer logos (Tier1, Tecton, Koike, Zeiss, Schneider). TTQ reduced 5 days to 1 day. Testimonial from VP Sales. Hiring." "AI voice agents focused on MENA/Arabic dialects. Batch calling, knowledge base, call transfer. 100+ users. 8 testimonials. Hiring via Zoho Recruit." "Developer email service — email to webhook as JSON. Free for unlimited domains. Minimal traction signals — very early stage."

Market/Competitors (L)

Extract from Perplexity response: market size, top competitors, any press/traction mentions. Keep it to 2-3 sentences.

Overall Assessment (M) — High Priority / Interesting / Pass

Consider: founder-company fit, market size, competitive landscape, team completeness, product/website maturity, traction signals.

If the investor provided a thesis, weight the assessment heavily toward their focus areas.

"High Priority — strong founder-market fit, clear product with paying customers, large TAM in manufacturing automation." "Interesting — impressive tech (26ms forks) but very early, single founder, no customers yet." "Pass — minimal traction, thin founder backgrounds for the space, crowded market."

Priority Rank (N)

Assigned in Step 5 after all companies are researched.

Step 5: Rank, Re-sort, and Summary

This step is NOT optional. You MUST sort the sheet after all research is complete. The investor expects the best companies at the top.

After ALL companies are researched and their rows updated:

1. Assign ranks 1 to N based on overall quality:

   • With thesis: Rank by thesis alignment (stage, sector, geography fit).
   • Without thesis: Rank on general investment quality = founder-company fit × market size × traction signals × team strength.

2. Write ranks to column N for all companies.

3. Re-sort the entire sheet by Priority Rank. This is critical — the sheet must end with rank #1 at the top.

# Step 5a: Read all current data
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":"/get-values"}'
  "spreadsheet_id": "{spreadsheet_id}",
  "ranges": ["Sheet1!A2:N{last_row}"]
}'

# Step 5b: Sort the rows by column N (Priority Rank) ascending, then rewrite ALL rows
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": "A2",
  "valueInputOption": "USER_ENTERED",
  "values": [{rows_sorted_by_column_N_ascending}]
}'

Do not skip the sort. Writing rank numbers without reordering the rows defeats the purpose. The investor opens the sheet and should see the top-ranked companies first.

4. Output a summary to the user:

Evaluated {N} companies from YC {batch}.
Sheet: {sheet_url}

Top Priority:
1. {Company} — {one-line why}
2. {Company} — {one-line why}
3. {Company} — {one-line why}

Worth a Look:
- {Company} — {one-line why}
- {Company} — {one-line why}

Skip:
- {Company} — {one-line why}

Cost Estimate

For a batch of ~22 companies with ~40 founders:

Tips

• Parallel, then row-by-row updates: Research ALL companies in parallel phases, but UPDATE the sheet one row at a time as each company's data arrives. The live-fill effect is the point.
• Never leave Website Analysis blank: Either summarize what you found or note "Website not available / pre-launch".
• Filter out YC partners: Tom Blomfield, Harj Taggar, etc. appear with title "Primary Partner" or "Group Partner" on company pages. They are YC staff, not founders — exclude them.
• Check multiple field names: Scrapegraph returns varying field names. Always check website_url / company_website_url / company_website for websites; sectors / sector_tags / tags for sectors.
• Apollo for missing data: Use person.city/person.state as location fallback. Use people/match with first_name/last_name/organization_name as fallback when no LinkedIn URL on YC page. The mixed_people/search endpoint is NOT available.
• Plain URLs, not HYPERLINK formulas: Multiple =HYPERLINK() in one cell → FALSE. Plain URLs auto-linkify in Sheets.
• Rich Perplexity prompts: Include company description and founder bios. "Tell me about {company_name}" gets generic results. Specific context gets useful answers.
• Be honest and specific: Reference actual founder backgrounds, not generic assessments. If the market is tiny, say so. Investors value honesty over hype.
• Skip gracefully: If a company website 404s or Apollo returns nothing, still fill what you have. Partial data > empty row.