Keinsaasforever Gtm Pipeline Skills Gtm Pipeline Skills

Company Enrichment

Enrich a company list with structured data and score against ICP. Returns enriched CSV + ICP scores.

Read ~/.claude/skills/gtm-pipeline/_shared/conventions.md before executing.

When to Use

• After company-search — raw list needs domains, revenue, headcount, etc.
• Before signal-search — ICP scoring determines which companies to invest signal credits on
• Before people-search — enriched domains required for most people search providers

Inputs

Two Phases

Phase 1: Data Enrichment

Add structured company data. Choose provider based on what's available:

Ask the user which provider to use. Default: PB SN Scraper if SN URLs available, otherwise Parallel Task Group.

Phase 2: ICP Scoring

Score enriched companies against client ICP definition.

Phase 1 Execution

PhantomBuster SN Account Scraper

Agent: Sales Navigator Account Scraper (config key: PB_AGENT_SN_ACCOUNT in _shared/local.md, or look up via PhantomBuster MCP)

Read _shared/phantombuster.md for the full API pattern. Use the /phantombuster skill to generate the script:

/phantombuster "Sales Navigator Account Scraper" -- scrape SN company data from <csv_file>, column <sn_url_column>

Input: SN company URLs (one per row in CSV) Output per company: name, industry, headcount by dept, location, LinkedIn URL, SN URL, revenue range, growth metrics (6m / 1y / 2y), median tenure

Parallel Task Group (Custom Enrichment)

Endpoint: POST https://api.parallel.ai/v1/tasks Auth: x-api-key: $PARALLEL_API_KEY

Always ask which processor to use: core, core2x, pro, ultra

Design an output schema matching the data points needed for ICP scoring. Example:

{
  "company_website": {"type": "string"},
  "linkedin_company_url": {"type": "string"},
  "estimated_revenue": {"type": "string"},
  "employee_count": {"type": "integer"},
  "industry": {"type": "string"},
  "founded_year": {"type": "integer"},
  "tech_stack_indicators": {"type": "array", "items": {"type": "string"}}
}

Always include company_website and linkedin_company_url in the schema.

Check latest docs via context7 (libraryName: parallel-web).

SimilarWeb via Apify

Actor: curious_coder/similarweb-scraper Env var: APIFY_API_KEY

Input: list of domains Output: monthly visits, traffic sources, bounce rate, pages per visit

Output

Write to csv/intermediate/companies_enriched.csv. Preserve all original columns, add enrichment columns.

Phase 2: ICP Scoring

How It Works

1. Read company rows (status=new, or all unscored)
2. Load ICP definition from context/icp.md
3. LLM scores each company → icp_score (0–100) + icp_rationale
4. Write score back to CSV
5. Process in batches (~10–20 companies per loop)

LLM: Ask the user which model to use (any LLM with structured output / JSON mode works).

Input Fields Used for Scoring

From the enriched company data:

• name, industry, description, employee count, location, website, LinkedIn URL
• Revenue (min/max), department headcounts (engineering, sales, ops, IT, BD, marketing)
• Growth metrics (6m, 1y, 2y), median tenure, year founded

ICP Scoring Prompt

The LLM receives each company row + the ICP definition and returns a structured score.

Output schema per company:

{
  "icp_score": 85,
  "icp_rationale": "DACH-based B2B SaaS company in target revenue range. Experiencing rapid growth with lean tech team, indicating clear need for external automation support rather than in-house development."
}

Per-Client Customization

• ICP doc: Maintain context/icp.md with the client's ICP definition — industry, size, location, revenue, tech profile, exclusions
• Score threshold: Adjust the gate based on selectivity (default: ≥70). The gate is optional — useful for credit savings on downstream steps, not a hard requirement.

Optional Gate for Next Step (Signal Search)

To save credits on downstream signal-search, gate by:

• icp_score >= 70
• website not empty
• type in [Startup, Scaleup] (if type field available)

Skip the gate if you want to score signals on the full enriched set.

Output

Write to csv/intermediate/companies_scored.csv with added columns:

icp_score, icp_rationale, scoring_status

All original + enrichment columns preserved.

Full Output

Updated CSV with all columns:

company_name, company_domain, company_linkedin_url,
company_industry, company_hq_location, company_hq_country,
company_employee_count, company_employee_range,
revenue_range, growth_6m, growth_1y, growth_2y,
headcount_engineering, headcount_sales, headcount_operations, headcount_IT,
icp_score, icp_rationale, enrichment_source

What's Missing (To Document)

• Pipe0 company data pipe: test and document endpoint
• Parallel Task Group for company enrichment: test with specific output schema
• SimilarWeb via Apify: document the full actor setup and output field mapping
• LLM API integration patterns for standalone ICP scoring (outside orchestration platforms)

Source: keinsaasforever/gtm-pipeline-skills — distributed by TomeVault.