gtm-enrichment-deep
AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
Best use case
gtm-enrichment-deep is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
Teams using gtm-enrichment-deep should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/orthogonal-gtm-enrichment-deep/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gtm-enrichment-deep Compares
| Feature / Agent | gtm-enrichment-deep | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GTM Enrichment — Deep (Sixtyfour AI Agent)
Enrich a lead from an email address (+ optional name) using Sixtyfour's AI agents as the primary enrichment source. Returns person data, company data, funding history, and AI/B2B classification.
**Cost**: ~$0.20-$0.22 per lead
**Latency**: ~30-60s (Sixtyfour AI agents browse the web)
## Input
Required:
- **email** — the lead's email address (e.g., `jane@acme.com`)
Optional:
- **name** — full name if known (improves match rate)
## Workflow
### Step 1: Extract Domain
Extract the domain from the email address. Example: `jane@acme.com` -> domain: `acme.com`
### Step 2: Run Sixtyfour Enrichment (parallel)
Fire both calls simultaneously. These are the primary data sources.
**Enrich Lead** ($0.10):
```bash
orth run sixtyfour /enrich-lead --body '{
"lead_info": {
"email": "{email}",
"first_name": "{first_name_if_known}",
"last_name": "{last_name_if_known}",
"company": "{company_name_if_known}",
"domain": "{domain}"
},
"struct": {
"full_name": "Full legal name of this person",
"first_name": "First name",
"last_name": "Last name",
"title": "Current job title at their company",
"linkedin_url": "LinkedIn profile URL (full URL starting with https://linkedin.com/in/)",
"city": "City where the person is located",
"state": "State or region where the person is located",
"country": "Country where the person is located"
}
}'
```
**Enrich Company** ($0.10):
```bash
orth run sixtyfour /enrich-company --body '{
"target_company": {
"domain": "{domain}"
},
"struct": {
"company_name": "Official company name",
"description": "One-paragraph description of what the company does",
"linkedin_url": "LinkedIn company page URL (full URL starting with https://linkedin.com/company/)",
"hq_city": "Headquarters city",
"hq_state": "Headquarters state or region",
"hq_country": "Headquarters country",
"employee_count": "Approximate number of employees (number only)",
"founded_year": "Year the company was founded (number only)",
"total_funding_amount_usd": "Total funding raised in USD (number only, no $ sign)",
"latest_funding_date": "Date of most recent funding round (YYYY-MM-DD format)",
"latest_funding_stage": "Stage of most recent funding round (e.g., Series A, Series B, Seed)",
"latest_funding_amount_usd": "Amount raised in most recent round in USD (number only)",
"is_ai_company": "true or false - does this company build or primarily use AI/ML technology?",
"ai_evidence": "Brief explanation of why this is or is not an AI company",
"is_b2b_saas": "true or false - is this a B2B SaaS company?",
"b2b_evidence": "Brief explanation of why this is or is not B2B SaaS"
}
}'
```
Record the status, latency, and any errors for both calls.
### Step 3: Fallback — Apollo Person Match (conditional)
**ONLY run if Sixtyfour /enrich-lead did NOT return a LinkedIn URL.**
```bash
orth run apollo /api/v1/people/match --body '{
"email": "{email}",
"reveal_personal_emails": true
}'
```
Cost: $0.01. Extract `linkedin_url`, and also grab `name`, `title`, `organization` as cross-reference data.
### Step 4: Fallback — Apollo Organization Enrich (conditional)
**ONLY run if Sixtyfour /enrich-company did NOT return funding data** (total_funding_amount_usd is null/empty).
```bash
orth run apollo /api/v1/organizations/enrich -q 'domain={domain}'
```
Cost: $0.01. Extract funding events, total funding, latest funding stage, and latest funding amount.
### Step 5: Compile Results
Merge all data into the output format below. Apply these rules:
1. **Sixtyfour is primary** — use its data first for all fields
2. **Apollo is fallback** — only used to fill gaps Sixtyfour missed
3. **Source tracking** — for each field, note whether it came from `sixtyfour` or `apollo`
4. **Confidence**:
- `high` — Sixtyfour returned the field directly
- `medium` — Apollo fallback provided the field
- `low` — field was inferred or partially matched
## Output Format
Present the results as a JSON code block:
```json
{
"person": {
"full_name": "string",
"title": "string",
"linkedin_url": "string",
"location": {"city": "string", "state": "string", "country": "string"},
"email_verified": "unknown",
"confidence": "high | medium | low",
"source": "sixtyfour | apollo"
},
"company": {
"name": "string",
"domain": "string",
"linkedin_url": "string",
"description": "string",
"geo": {"city": "string", "state": "string", "country": "string"},
"employee_count": "number | null",
"founded_year": "number | null",
"funding": {
"total_amount": "number | null",
"total_amount_printed": "string | null",
"latest_round_date": "string | null",
"latest_round_stage": "string | null",
"latest_round_amount": "number | null",
"rounds": [],
"confidence": "high | medium | low"
},
"classification": {
"is_ai": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]},
"is_b2b_saas": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]}
},
"buying_signals": {
"has_enterprise_plan": null,
"has_self_serve": null,
"hiring_enterprise_reps": null,
"website_traffic_rank": null,
"github_stars": null,
"tech_stack": null
},
"confidence": "high | medium | low",
"source": "sixtyfour | apollo | merged"
},
"meta": {
"total_cost": "$0.XX",
"api_calls": [],
"phases_run": [1, 2],
"enrichment_timestamp": "ISO datetime"
}
}
```
## Error Visibility
Track EVERY API call in the `meta.api_calls` array:
```json
{
"api": "sixtyfour",
"endpoint": "/enrich-lead",
"status": "success | partial | error",
"cost": "$0.10",
"latency_ms": 35000,
"fields_returned": ["full_name", "title", "linkedin_url"],
"fields_missing": ["city"],
"error": null
}
```
**If an API call fails, returns empty data, or times out, include it in the api_calls array with status='error' and a clear error message. Never silently skip failures.**
## Cost Tracking
Sum all API call costs and report in `meta.total_cost`:
- Sixtyfour /enrich-lead: $0.10
- Sixtyfour /enrich-company: $0.10
- Apollo /api/v1/people/match: $0.01 (only if used)
- Apollo /api/v1/organizations/enrich: $0.01 (only if used)
## Example
**Input**: `jane@acme.com`
**Expected flow**:
1. Extract domain: `acme.com`
2. Fire Sixtyfour /enrich-lead and /enrich-company in parallel
3. Check if LinkedIn URL returned — if not, call Apollo /people/match
4. Check if funding data returned — if not, call Apollo /organizations/enrich
5. Compile and output JSON with all fields, error visibility, and cost
## Tips
- Sixtyfour takes 30-60s per call — be patient, do NOT timeout early
- If Sixtyfour returns partial data, still use what it returned and fill gaps with Apollo
- AI/B2B classification comes from Sixtyfour's web research — it reads the company website
- The `struct` field in Sixtyfour tells the AI agent exactly what to research — modify fields there if you need different data pointsRelated Skills
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