Company Research Skill for Claude

Company Research

Discover and deeply research companies to sell to. Uses Browserbase Search API for discovery and a Plan→Research→Synthesize pattern for deep enrichment — outputting a scored research report and CSV.

Required: BROWSERBASE_API_KEY env var and browse CLI installed.

First-run setup: On the first run you'll be prompted to approve browse cloud fetch, browse cloud search, cat, mkdir, sed, etc. Select "Yes, and don't ask again for: browse cloud fetch:\*" (or equivalent) for each to auto-approve for the session. To permanently approve, add these to your ~/.claude/settings.json under permissions.allow:

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

Path rules: Always use the full literal path in all Bash commands — NOT ~ or $HOME (both trigger "shell expansion syntax" approval prompts). Resolve the home directory once and use it everywhere. When constructing subagent prompts, replace {SKILL_DIR} with the full literal path.

Output directory: All research output goes to ~/Desktop/{company_slug}_research_{YYYY-MM-DD}/. This directory contains one .md file per researched company plus a final .csv. The user gets both the scored spreadsheet and the full research files on their Desktop.

CRITICAL — Tool restrictions (applies to main agent AND all subagents):

• 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.

CRITICAL — Anti-hallucination rules (applies to main agent AND all subagents):

• 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.

CRITICAL — Minimize permission prompts:

• 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

Follow these 5 steps in order. Do not skip steps or reorder.

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

Step 0: Setup Output Directory

Before starting, create the output directory on the user's Desktop:

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

Replace {company_slug} with the user's company name (lowercase, hyphenated) and {YYYY-MM-DD} with today's date. Pass {OUTPUT_DIR} (as a full literal path, not with ~) to all subagent prompts so they write research files there.

Also clean up discovery batch files from prior runs:

rm -f /tmp/company_discovery_batch_*.json

Step 1: Deep Company Research

This is the most important step. The quality of everything downstream depends on deeply understanding the user's company.

• Ask the user for their company name or URL

• 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.

• 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):
• browse cloud fetch --allow-redirects "{company website}/sitemap.xml" — sitemap is small, raw browse cloud fetch is fine
• Scan for URLs with keywords: customer, case-stud, pricing, about, use-case, industry, solution
• Optionally also fetch /llms.txt for page descriptions
• 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.

• Present the profile to the user for confirmation. Do not proceed until confirmed.

• Save the confirmed profile to {SKILL_DIR}/profiles/{company-slug}.json

• 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

| Mode | Research per company | Best for |

|------|---------------------|----------|

| quick | Homepage + 1-2 searches | ~100 companies, broad scan |

| deep | 2-3 sub-questions, 5-8 tool calls | ~50 companies, solid research |

| deeper | 4-5 sub-questions, 10-15 tool calls | ~25 companies, full intelligence |

Step 3: Discovery

Formula: ceil(requested_companies / 35) search queries needed. Over-discover by ~2-3x because filtering typically drops 50-70%.

Generate search queries with these patterns:

• 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")

Process:

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

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

• After all waves complete, deduplicate: node {SKILL_DIR}/scripts/list_urls.mjs /tmp
• 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.

See references/workflow.md for subagent prompt templates and wave management.

Step 4: Deep Research & Scoring

Launch subagents to research companies in parallel. See references/workflow.md for the enrichment subagent prompt template. See references/research-patterns.md for the full research methodology.

Process:

• Split filtered URLs into groups per subagent (quick: ~10, deep: ~5, deeper: ~2-3)
• Launch ALL enrichment subagents at once (up to ~6 per message)
• 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.

• Subagents write ALL markdown files in a SINGLE Bash call using chained heredocs to {OUTPUT_DIR}/
• After ALL subagents complete, proceed to Step 5

Critical: Include the confirmed ICP description verbatim in every subagent prompt. Pass the full literal {OUTPUT_DIR} path to every subagent.

Step 5: Report & CSV

• Generate HTML report + CSV (opens overview in browser automatically):

   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

• 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

• 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 |

• 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.

Offer to dig deeper into specific companies, adjust scoring criteria, or re-run discovery with different queries.