Company Research

BROWSERBASE_API_KEY

browse

browse cloud fetch

browse cloud search

cat

mkdir

sed

~/.claude/settings.json

permissions.allow

"Bash(browse:*)", "Bash(bunx:*)", "Bash(bun:*)", "Bash(node:*)",
"Bash(cat:*)", "Bash(mkdir:*)", "Bash(sed:*)", "Bash(head:*)", "Bash(tr:*)", "Bash(rm:*)"

~

$HOME

{SKILL_DIR}

~/Desktop/{company_slug}_research_{YYYY-MM-DD}/

.md

.csv

• All web searches: use

  browse cloud search

  . NEVER use WebSearch.
• All page content extraction: use

  node {SKILL_DIR}/scripts/extract_page.mjs "<url>"

  . This script fetches via

  browse cloud fetch --output

  , parses title + meta tags + visible body text, and automatically falls back to

  browse get markdown

  when fetch fails or returns thin JS-rendered content. NEVER hand-roll a

  browse cloud fetch | sed

  pipeline — it strips meta tags and doesn't parse the stdout JSON envelope. NEVER use WebFetch.
• All research output: subagents write one markdown file per company to

  {OUTPUT_DIR}/{company-slug}.md

  using bash heredoc. NEVER use the Write tool or

  python3 -c

  . See

  references/example-research.md

  for the file format.
• Report + CSV compilation: use

  node {SKILL_DIR}/scripts/compile_report.mjs {OUTPUT_DIR} --open

  — generates HTML report and CSV in one step, opens overview in browser.
• URL deduplication: use

  node {SKILL_DIR}/scripts/list_urls.mjs /tmp

  after discovery.
• Subagents must use ONLY the Bash tool. No other tools allowed.
• Main agent NEVER reads raw discovery JSON batch files. Use

  list_urls.mjs

  for dedup.

• NEVER infer

  product_description

  ,

  industry

  , or

  target_audience

  from a site's fonts, framework (Framer/Next.js/React), design system, or typography. These are cosmetic and say nothing about what the company sells.
• NEVER let the user's own ICP leak into a target's description. If you don't know what the target does, write

  Unknown

  — do not pattern-match them onto the ICP.

• product_description

  MUST quote or paraphrase a specific phrase from

  extract_page.mjs

  output (TITLE, META_DESCRIPTION, OG_DESCRIPTION, HEADINGS, or BODY). If none of those fields yield a recognizable product statement, write

  Unknown — homepage content not accessible

  .
• If

  product_description

  is

  Unknown

  , cap

  icp_fit_score

  at 3 and set

  icp_fit_reasoning

  to

  Insufficient evidence — homepage returned no readable content

  .

• Subagents MUST batch ALL file writes into a SINGLE Bash call using chained heredocs. One Bash call = one permission prompt.
• Batch ALL searches and ALL fetches into single Bash calls using

  &&

  chaining.

Pipeline Overview

1. Company Research — Deeply understand the user's company, product, and who they sell to
2. Depth Mode Selection — Choose research depth based on how many targets they want
3. Discovery — Find target companies using diverse search queries
4. Deep Research & Scoring — Research each company, score ICP fit
5. Report & CSV — Present findings, compile scored CSV

Step 0: Setup Output Directory

OUTPUT_DIR=~/Desktop/{company_slug}_research_{YYYY-MM-DD}
mkdir -p "$OUTPUT_DIR"

{company_slug}

{YYYY-MM-DD}

{OUTPUT_DIR}

~

rm -f /tmp/company_discovery_batch_*.json

Step 1: Deep Company Research

1. Ask the user for their company name or URL
2. Check for an existing profile:
   • List files in

     {SKILL_DIR}/profiles/

     (ignore

     example.json

     )
   • If a matching profile exists → load it, present to user: "I have your profile from {researched_at}. Still accurate?" If yes → skip to Step 2.
   • If no profile exists → proceed with deep research below.
3. Run a full deep research on the user's company using the Plan→Research→Synthesize pattern. See

   references/research-patterns.md

   for sub-question templates and research methodology.
   Key research steps:
   • Search:

     browse cloud search "{company name}" --num-results 10

   • Fetch homepage:

     node {SKILL_DIR}/scripts/extract_page.mjs "{company website}"

   • Discover site pages via sitemap (do NOT hardcode paths like

     /about

     or

     /customers

     ):

     1. browse cloud fetch --allow-redirects "{company website}/sitemap.xml"

        — sitemap is small, raw

        browse cloud fetch

        is fine
     2. Scan for URLs with keywords:

        customer

        ,

        case-stud

        ,

        pricing

        ,

        about

        ,

        use-case

        ,

        industry

        ,

        solution

     3. Optionally also fetch

        /llms.txt

        for page descriptions
     4. Pick 3-5 most relevant URLs and extract with

        extract_page.mjs

        (NOT raw

        browse cloud fetch

        )
   • Search for external context and competitors
   • Accumulate findings with confidence levels
   Synthesize into a profile: Company, Product, Existing Customers, Competitors, Use Cases. Do NOT include ICP or sub-verticals — those are per-run decisions.
4. Present the profile to the user for confirmation. Do not proceed until confirmed.
5. Save the confirmed profile to

   {SKILL_DIR}/profiles/{company-slug}.json

6. Ask clarifying questions using

   AskUserQuestion

   with checkboxes:
   • "Which segments are you targeting?" with options derived from the company research
   • "Company stage?" — Startups, Mid-market, Enterprise, All
   • "How many companies / depth?" — Quick (~100), Deep (~50), Deeper (~25)
   • This is the ONLY user interaction. After this, execute silently until results are ready.

Step 2: Depth Mode Selection

quick

deep

deeper

Step 3: Discovery

ceil(requested_companies / 35)

• Industry + company stage + geography ("fintech startups series A Bay Area")
• Technology stack + use case ("companies using Selenium for web scraping")
• Competitor adjacency ("alternatives to {known company in ICP}")
• Buyer persona + pain point ("engineering teams struggling with browser automation")

1. Launch ALL discovery subagents at once (up to ~6 per message). Each runs its queries in a SINGLE Bash call:
   bash

   browse cloud search "{query}" --num-results 25 --output /tmp/company_discovery_batch_{N}.json

2. After all waves complete, deduplicate:

   node {SKILL_DIR}/scripts/list_urls.mjs /tmp

3. Filter the URL list — remove:
   • Blog posts, news articles (globenewswire.com, techcrunch.com, etc.)
   • Directories/aggregators (tracxn.com, crunchbase.com, g2.com)
   • The user's own competitors and existing customers (from profile) Keep only company homepages.

references/workflow.md

Step 4: Deep Research & Scoring

references/workflow.md

references/research-patterns.md

1. Split filtered URLs into groups per subagent (quick: ~10, deep: ~5, deeper: ~2-3)
2. Launch ALL enrichment subagents at once (up to ~6 per message)
3. Each subagent uses ONLY Bash — for each company:
   Phase A — Plan (skip in quick mode): Decompose into 2-5 sub-questions based on ICP and enrichment fields.
   Phase B — Research Loop: Search and fetch pages, extract findings. Respect step budget (quick: 2-3, deep: 5-8, deeper: 10-15).
   Phase C — Synthesize: Score ICP fit 1-10 with evidence. Fill enrichment fields from findings.
4. Subagents write ALL markdown files in a SINGLE Bash call using chained heredocs to

   {OUTPUT_DIR}/

5. After ALL subagents complete, proceed to Step 5

{OUTPUT_DIR}

Step 5: Report & CSV

1. Generate HTML report + CSV (opens overview in browser automatically):
   bash

   node {SKILL_DIR}/scripts/compile_report.mjs {OUTPUT_DIR} --open

   This generates:

   • {OUTPUT_DIR}/index.html

     — overview page with scored table (opens in browser)

   • {OUTPUT_DIR}/companies/*.html

     — individual company pages (linked from overview)

   • {OUTPUT_DIR}/results.csv

     — scored spreadsheet for import into sheets/CRM
2. Present a summary in chat too:

## Company Research Complete

- **Total companies researched**: {count}
- **Depth mode**: {mode}
- **Score distribution**:
  - Strong fit (8-10): {count}
  - Partial fit (5-7): {count}
  - Weak fit (1-4): {count}
- **Report opened in browser**: ~/Desktop/{company_slug}_research_{date}/index.html

1. Show the top companies sorted by ICP score in a table:

| Company | Score | Product | Industry | Fit Reasoning |
|---------|-------|---------|----------|---------------|
| Acme | 9 | AI inventory management | E-commerce SaaS | Series A, uses Selenium, expanding to EU |

1. For the top 3-5 companies, show a brief research summary — key findings, why they're a good fit, and what specific angle to approach them with.