gtm-enrichment-smart
Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.
npx skills add gooseworks-ai/goose-skills --skill gtm-enrichment-smart --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# GTM Enrichment — Smart (Multi-Provider Waterfall) ## Setup Read your credentials from ~/.gooseworks/credentials.json: ```bash export GOOSEWORKS_API_KEY=$(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 a waterfall strategy: start with cheap APIs ($0.01 each), cross-reference for confidence, then use expensive AI agents only for gaps. Spends proportionally to lead quality. **Cost**: $0.04 (best) to ~$0.12 (typical with buying signals) to ~$0.26 (worst, Sixtyfour fallback) **Latency**: ~5-15s typical, up to 60s if Sixtyfour fallback triggers ## Input Required: - **email** — the lead's email address (e.g., `jane@acme.com`) Optional: - **name** — full name if known (improves match rate) ## Workflow ### Step 0: Extract Domain + Free Email Check
- Setup
- Input
- Workflow
- Step 0: Extract Domain + Free Email Check
- PHASE 1 — Core (always run, parallel) — ~$0.03-$0.06
- PHASE 1 MERGE — Cross-Reference & Confidence
- PHASE 2 — Gap-Fill (conditional) — $0.00-$0.02
- PHASE 3 — Sixtyfour Fallback (conditional, expensive) — $0.00-$0.20
- PHASE 4 — Buying Signals (qualified leads only) — $0.00-$0.04
- PHASE 5 — Cheap/Free Signals — $0.00-$0.01
- FINAL — Compile & Output
- Output Format
- Error Visibility
- Cost Tracking
export GOOSEWORKS_API_KEY=$(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'))")
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
Extract org name from GitHub URL, e.g., https://github.com/ngrok -> ngrok
Use the GitHub public API (no auth needed for public repos):
curl -s "https://api.github.com/orgs/{org_name}/repos?sort=stars&per_page=5" | jq '[.[] | {name: .name, stars: .stargazers_count}]'What does the gtm-enrichment-smart skill do?
Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill gtm-enrichment-smart --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.