Data Enrichment

Enrich company and contact lists with public data — firmographics, technographics, social profiles, funding history, and employee count estimates. Clean, ded...

Security Scan

The skill's requirements and instructions are coherent with its stated purpose (public-data enrichment) and it makes no unexpected requests for credentials, installs, or system access.

Purpose & Capability

Name/description (company/contact enrichment, firmographics, technographics, scoring) matches the instructions and reference docs. The skill only uses public sources (LinkedIn, Crunchbase, BuiltWith/Wappalyzer, news), which is consistent with enrichment.

Instruction Scope

SKILL.md is instruction-only and stays focused on enrichment/cleaning/scoring tasks. It assumes the agent will fetch public web data but does not prescribe reading unrelated local files or environment secrets. However, instructions are fairly open-ended about how to collect public data (scraping vs API), which could lead to broad web queries or scraping behavior if not constrained.

Install Mechanism

No install spec and no code files are present, so nothing will be written to disk or executed by an installer. This is the lowest-risk pattern for a skill of this type.

Credentials

The skill requests no environment variables or credentials, which is proportionate for an instruction-only public-data enrichment skill. One caveat: some listed data sources (Crunchbase, BuiltWith, LinkedIn) often require API keys or paid access in practice; the SKILL.md does not request or document those credentials, so an implementer would likely need to supply them externally.

Persistence & Privilege

Skill is not always-enabled, does not require system-level changes, and contains no installation logic. It does not request elevated or persistent privileges.

Assessment

This skill is internally consistent with public-data enrichment, but consider the following before installing: (1) Several named data sources (Crunchbase, BuiltWith, LinkedIn) often require API keys or paid access and may impose rate limits — plan how you'll supply and protect any keys if you use them. (2) The instructions allow broad web lookups/scraping; ensure you have permission and are compliant with provider Terms of Service and privacy laws (e.g., GDPR) before enriching personal contact data. (3) Because it's instruction-only, the agent will decide how to gather data — restrict scope in prompts (use approved APIs, rate limits, and only public sources) and verify results before importing into CRMs. (4) If you need automated, reliable access to those third-party services, add explicit, minimal environment variables for needed API keys rather than letting the agent attempt ad-hoc scraping.

Data Enrichment Skill

Transform raw company/contact lists into enriched, scored, CRM-ready datasets.

What It Does

1. Company Enrichment — Add firmographic data (industry, size, revenue range, funding, tech stack)
2. Contact Enrichment — Find titles, social profiles, recent activity
3. Data Cleaning — Deduplicate, normalize, fix formatting issues
4. Lead Scoring — Score enriched records on custom criteria
5. Export — Output in CSV, JSON, or CRM-import format

Usage

Enrich a Company List

Enrich this list of companies:
1. Acme Corp
2. Widget Labs  
3. DataFlow Inc
4. CloudScale
5. GrowthLab

For each, find:
- Industry and sub-industry
- Employee count (estimated range)
- Funding status and last round
- Tech stack (if detectable)
- Key decision makers (CEO, CTO, CMO)
- LinkedIn company page
- Recent news (last 90 days)

Clean and Deduplicate

Clean this CSV:
[Paste CSV or provide file]

Tasks:
- Remove exact and fuzzy duplicates
- Normalize company names (Inc/LLC/Ltd variations)
- Fix email formatting issues
- Flag incomplete records
- Standardize phone number format
- Fill missing fields where possible from public data

CRM Enrichment

I'm importing these contacts into [HubSpot/Salesforce/Pipedrive]:

[Paste contact list]

Enrich each record with:
- Company info (size, industry, revenue)
- Contact title and seniority level
- LinkedIn profile URL
- Lead score (1-100 based on: company size 10-500, SaaS industry, recent funding)
- Tag: hot/warm/cold

Output as CSV with CRM-compatible column headers.

Technographic Profiling

For these companies, identify their tech stack:
- What CMS do they use? (WordPress, Shopify, custom)
- What analytics? (GA4, Mixpanel, Amplitude)
- What email platform? (Mailchimp, SendGrid, HubSpot)
- What payment processor? (Stripe, PayPal, Square)
- Any AI/automation tools visible?

Companies: [list]

Output Format

Enriched Company Record

## [Company Name]
| Field | Value |
|-------|-------|
| Industry | [Industry / Sub-industry] |
| Employees | [Range estimate] |
| Revenue | [Range estimate] |
| Founded | [Year] |
| Funding | [Total raised / Last round] |
| Location | [HQ city, country] |
| Website | [URL] |
| LinkedIn | [URL] |
| Tech Stack | [Detected tools] |
| Recent News | [Last 90 days highlights] |

### Key Contacts
| Name | Title | LinkedIn | Seniority |
|------|-------|----------|-----------|
| [Name] | [Title] | [URL] | [C-level/VP/Director/Manager] |

CSV Export Format

company_name,industry,employees,revenue_range,funding,location,website,linkedin,tech_stack,news,contact_name,contact_title,contact_linkedin,lead_score,tag

Data Sources

• Public websites and About pages
• LinkedIn (public profiles)
• Crunchbase (public funding data)
• BuiltWith / Wappalyzer (tech stack)
• News aggregators (recent activity)
• Job postings (growth signals)

Best Practices

• Provide clean input data (company names, domains, or LinkedIn URLs)
• Specify which fields matter most — enrichment is faster when focused
• For large lists (100+), process in batches of 25
• Always verify enriched data before importing to CRM
• Pair with lead-gen-research for full qualification + enrichment pipeline

References

• references/field-definitions.md — What each enrichment field means
• references/scoring-model.md — Default lead scoring weights