lead-enrichment
Skill9 starsChanged 6 months agoMITView on GitHub
Enrich leads with email, phone, company data using Hunter, Sixtyfour, and Fiber. Use when you have a name/company and need full contact profile. For just email, see find-email-by-name. For building prospect lists from scratch, see sales-prospecting.
12 other projects use a version of this.
Adopted by
Most recently taken up by
- vijaysuperpositionist/skillstheir own version, first seen Nov 1, 2026
- runxhq/runxtheir own version, first seen Oct 13, 2026
- SELAT-AI/selat-skillstheir own version, first seen Oct 4, 2026
- omnimay/skillstheir own version, first seen Sep 18, 2026
- 0xMassi/webclaw-skilltheir own version, first seen Sep 14, 2026
- superagnt/leveragetheir own version, first seen Sep 11, 2026
Other versions12 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 12
- runxhq/runx★ 97same name
- chadboyda/agent-gtm-skills★ 77same name
- kevinbadi/ai-os-skills★ 69same name
- superagnt/leverage★ 6same name
- 0xMassi/webclaw-skill★ 2same name
- omnimay/skills★ 2same name
- SELAT-AI/selat-skills★ 2same name
- SkillMedev/b2b-prospecting-engine★ 1same name
- SpiderIQ/skills★ 1same name
- Typeless-Git/skills★ 1same name
2 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository78 other files it gives its agents.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 9
Before helping, check MEMORY.md for current ICP and outbound targets.
- Distinctive: line 33
Only if score < 70 or you need high confidence before outreach.
- Distinctive: line 45
If LinkedIn URL known:
- Distinctive: line 73
Ask the user what level they need if unclear.
- Distinctive: line 79
Always verify email before cold outreach
“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
[YOUR_PRODUCT] Context
Before helping, check MEMORY.md for current ICP and outbound targets.
Output in Russian (names, emails, titles in original).
For chat platforms: compact profile card, not raw JSON.
Lead Enrichment
Enrich a lead from partial info (name + company) into a full contact profile with email, phone, LinkedIn, and company data.
Workflow
Step 1: Find Email (Hunter)
orth run hunter /v2/email-finder -q domain=COMPANY_DOMAIN -q first_name=FIRST -q last_name=LASTIMPORTANT: Use separate -q flags. Never join with &.
Step 2: Verify Email (Hunter)
orth run hunter /v2/email-verifier -q email=found@email.comOnly if score < 70 or you need high confidence before outreach.
Step 3: Full Profile (Sixtyfour)
orth run sixtyfour /enrich-lead -d '{"lead_info": {"first_name": "FIRST", "last_name": "LAST", "company": "COMPANY"}, "struct": {"email": "Work email", "phone": "Phone number", "title": "Job title"}}'Returns AI-researched profile with email, phone, title.
Step 4: LinkedIn Data (Fiber)
If LinkedIn URL known:
orth run fiber /v1/linkedin-live-fetch/profile/single -d '{"identifier": "https://linkedin.com/in/USERNAME"}'Step 5: Company Context (Hunter)
orth run hunter /v2/companies/find -q domain=COMPANY_DOMAINOutput Format
**[Full Name]** — [Title] @ [Company]
Email: [email] ([verified/unverified], score [X]%)
Phone: [phone or "not found"]
LinkedIn: [url]
Company: [size] employees, [industry], [location]Quick Mode vs Full Mode
- Quick (1 call, cheap): Just Step 1 — email only
- Standard (2-3 calls): Steps 1 + 3 — email + profile
- Full (4-5 calls): All steps — complete dossier
Ask the user what level they need if unclear.
Tips
- LinkedIn URL dramatically improves match rates
- Sixtyfour is AI-powered — slower but finds more data
- Always verify email before cold outreach
- Cache results — don't re-enrich the same lead
Related Skills
- pipeline-manager — stores enrichment results in pipeline.json
- signal-scanner — triggers enrichment for newly detected signals
- find-email-by-name — just email lookup (cheaper, faster)
- person-lookup — background research on a person
- sales-prospecting — find new leads matching ICP
- cold-email — write outreach after enrichment
- outreach-sender — send and track outreach
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

