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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.
Passed 15 of 19 test cases with the skill, and 8 of 19 without it.
and 12 more in the full report
10 fixed8 passed either way3 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
If an enrichment run executes both primary Sixtyfour lead and company calls successfully without triggering any Apollo fallbacks, what is the exact cost calculation and value in meta.total_cost?
| Checked | Without skill | With skill |
|---|---|---|
| Calculates total_cost as $0.20 when both primary calls execute without fallbacks. | Missed | Passed |
The instructions your model reads when the skill loads.
Read your credentials from ~/.gooseworks/credentials.json:
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")
If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login
All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"
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)
Required:
[email protected])Optional:
Extract the domain from the email address. Example: [email protected] -> domain: acme.com
Fire both calls simultaneously. These are the primary data sources.
Enrich Lead ($0.10):
test cases passed, vs 8 / 19 without
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