À propos
Cette compétence enrichit les fiches de contact HubSpot en récupérant les adresses e-mail, numéros de téléphone et postes manquants auprès de fournisseurs de données externes, puis les réécrit en toute sécurité. Sa caractéristique principale est un système d'adaptateurs modulables, utilisant par défaut l'agrégateur en cascade FullEnrich, mais incluant des adaptateurs pour Apollo, Hunter et Dropcontact, ainsi qu'un modèle pour des fournisseurs personnalisés. Utilisez-la pour automatiser la complétion des données de contact dans vos workflows HubSpot.
Installation rapide
Claude Code
Recommandénpx skills add TomGranot/hubspot-admin-skills -a claude-code/plugin add https://github.com/TomGranot/hubspot-admin-skillsgit clone https://github.com/TomGranot/hubspot-admin-skills.git ~/.claude/skills/waterfall-enrich-contactsCopiez et collez cette commande dans Claude Code pour installer cette compétence
Documentation
Waterfall-Enrich Contacts with External Providers
Fill missing emails, phone numbers, and job titles on HubSpot contacts using an external enrichment provider, then write results back with a full audit trail. The provider layer is pluggable: FullEnrich (a waterfall aggregator that queries 20+ upstream sources until one hits) is the default, with Apollo, Hunter, and Dropcontact adapters included and a template for whatever provider your team already pays for.
Why This Matters
The internal enrichment skills (/enrich-company-name, /enrich-industry, /backfill-geo-data) only move data the portal already has. When a contact's email, direct dial, or title simply isn't anywhere in HubSpot, external enrichment is the only fix — and it costs real money per lookup, which is why this skill is built around cost caps, previews, and typed confirmations.
Provider Landscape
| Provider | Adapter | Strength | Model |
|---|---|---|---|
| FullEnrich (default) | providers/fullenrich.py | Waterfall across 20+ sources — best hit rates for email + mobile | Credits per lookup, async bulk API |
| Apollo | providers/apollo.py | Large B2B database, titles + firmographics | Credits; personal-data reveals plan-gated |
| Hunter | providers/hunter.py | Email finding by name+domain, confidence scores | Requests per plan; email only |
| Dropcontact | providers/dropcontact.py | GDPR-first, algorithmic (no stored database) | Credits, async |
| Your provider | copy providers/_template.py | Whatever you already use | — |
| Mock (testing only) | providers/mock.py | Deterministic fake data for /sandbox-self-test and dry runs — no network | Free; never use on production |
| HubSpot Breeze Intelligence | (native, no adapter) | In-platform enrichment + form shortening | Credit add-on; programmatic API access is enterprise-gated — which is exactly why this skill defaults to provider-agnostic adapters |
Switch providers with one env var: ENRICHMENT_PROVIDER=apollo.
Prerequisites
- A HubSpot private app access token (
HUBSPOT_ACCESS_TOKENin.env) with contact read/write scopes - Python 3.10+ with
uv - An account + API key with your chosen provider (e.g.
FULLENRICH_API_KEYfrom FullEnrich dashboard > Settings > API) - A compliance check: enrichment sends contact names and company data to a third party and imports personal data (emails, phones). Confirm this fits your data processing agreements and the applicable privacy rules (GDPR/CCPA) before running.
Scripts
| Stage | Script | Run with |
|---|---|---|
| Before | scripts/before.py | uv run skills/waterfall-enrich-contacts/scripts/before.py |
| Execute | scripts/execute.py | uv run skills/waterfall-enrich-contacts/scripts/execute.py |
| After | scripts/after.py | uv run skills/waterfall-enrich-contacts/scripts/after.py |
Provider adapters live in scripts/providers/ — one module per provider implementing enrich(contacts) -> results (see _template.py for the contract).
Configuration
Everything is set in .env:
HUBSPOT_ACCESS_TOKEN=pat-na1-xxxxxxxx
ENRICHMENT_PROVIDER=fullenrich # fullenrich | apollo | hunter | dropcontact | mock | yours
FULLENRICH_API_KEY=... # the chosen provider's key
ENRICHMENT_TARGET_FIELD=phone # phone | email | jobtitle
ENRICHMENT_MAX_CONTACTS=100 # hard cap per run — credits cost money
ENRICHMENT_OVERWRITE=false # never overwrite existing values (default)
ENRICHMENT_CREDITS_PER_CONTACT=1 # for before.py's cost preview
Execution Pattern
Stage 1: Plan
- Choose the provider and the target field (a phone backfill and an email backfill are separate runs).
- Confirm the compliance check above with whoever owns data privacy.
- Confirm budget:
MAX_CONTACTS × credits-per-lookupis the per-run ceiling. Start with a small run (25-50) and inspect quality before scaling.
Stage 2: Before
uv run skills/waterfall-enrich-contacts/scripts/before.py
Counts candidates (contacts with first name + last name + company but missing the target field) and prints a cost ceiling. Read-only.
Stage 3: Execute
uv run skills/waterfall-enrich-contacts/scripts/execute.py
The script:
- Selects up to
MAX_CONTACTScandidates via the Search API - Asks for typed confirmation (
ENRICH) before spending credits - Calls the provider adapter (async providers poll until done)
- Computes writes — existing non-empty HubSpot values are never overwritten unless
ENRICHMENT_OVERWRITE=true; skipped values are still recorded in the audit CSV - Asks for a second typed confirmation (
WRITE) before touching HubSpot - Batch-updates contacts and writes the audit CSV (old value, new value, action, source per field)
Stage 4: After
uv run skills/waterfall-enrich-contacts/scripts/after.py
Compares candidate counts against the baseline, then spot-check 10-20 enriched contacts by hand — provider quality varies by segment, and the audit CSV tells you exactly what was written where.
Safety Mechanisms
| Mechanism | Detail |
|---|---|
| Per-run cap | MAX_CONTACTS (default 100) bounds credit spend per run. Deliberately low — raise it only after verifying quality. |
| No-overwrite default | Existing non-empty values are never replaced unless ENRICHMENT_OVERWRITE=true. Enrichment fills gaps; it does not correct data. |
| Double confirmation | Typed ENRICH before credits are spent; typed WRITE before HubSpot is touched. Aborting between the two costs credits but changes nothing. |
| CSV audit trail | Every field written (and every skip) recorded with old value, new value, and provider source. |
| Rollback data | The audit CSV's old column is the rollback: batch-update those values back to undo a run. |
Rollback
- The execute audit CSV records the previous value of every field it wrote. To undo, batch-update those contact/field pairs back to the
oldvalues (empty string clears a field). - Values are also individually recoverable from each contact's property history.
Technical Gotchas
- Verify adapter payloads against current provider docs. Provider APIs move fast; each adapter's docstring links the docs and flags what to check. The adapters fail loudly (clear
SystemExitmessages) on auth or credit errors before touching HubSpot. - Waterfall providers are asynchronous. FullEnrich and Dropcontact return results in seconds-to-minutes; the adapters poll. Don't kill the script mid-poll — credits are consumed at submission.
- Enriched emails are unverified senders' risk. A found email is not consent to market. New emails enter as non-marketing data points; your normal opt-in and deliverability rules apply before any sends.
- Match rates of 40-70% are normal. Providers can't find everyone. The audit CSV separates "provider found nothing" (absent) from "found but skipped" (existing value).
- Domain quality drives hit rates. Candidates whose email domain or company website is missing enrich poorly. Run
/enrich-company-namefirst — better identity inputs, better waterfall results. - Internal-data-first. If the value exists anywhere in the portal (associated company,
ip_country, form submissions), the free internal skills should fill it — save credits for data HubSpot genuinely doesn't have.
Dépôt GitHub
Frequently asked questions
What is the waterfall-enrich-contacts skill?
waterfall-enrich-contacts is a Claude Skill by TomGranot. Skills package instructions and resources that Claude loads on demand, so Claude can perform waterfall-enrich-contacts-related tasks without extra prompting.
How do I install waterfall-enrich-contacts?
Use the install commands on this page: add waterfall-enrich-contacts to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does waterfall-enrich-contacts belong to?
waterfall-enrich-contacts is in the Meta category, tagged ai.
Is waterfall-enrich-contacts free to use?
Yes. waterfall-enrich-contacts is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Compétences associées
Cette compétence propose une configuration éprouvée en production pour Content Collections, un outil axé sur TypeScript qui transforme des fichiers Markdown/MDX en collections de données typées de manière sûre avec une validation Zod. Utilisez-la lors de la création de blogs, de sites de documentation ou d'applications Vite + React riches en contenu pour garantir la sécurité de typage et la validation automatique du contenu. Elle couvre tout, de la configuration du plugin Vite et de la compilation MDX à l'optimisation des déploiements et la validation des schémas.
Cette compétence permet aux développeurs de créer des applications avec la plateforme de marchés prédictifs Polymarket, incluant l'intégration d'API pour le trading et les données de marché. Elle fournit également une diffusion de données en temps réel via WebSocket pour surveiller les transactions en direct et l'activité du marché. Utilisez-la pour mettre en œuvre des stratégies de trading ou pour créer des outils traitant les mises à jour de marché en direct.
Cette compétence aide les développeurs à créer des plugins OpenCode qui s'interconnectent avec plus de 25 types d'événements tels que les commandes, les fichiers et les opérations LSP. Elle fournit la structure du plugin, les spécifications de l'API événementielle et les modèles d'implémentation pour les modules JavaScript/TypeScript. Utilisez-la lorsque vous avez besoin d'intercepter, de surveiller ou d'étendre le cycle de vie de l'assistant IA OpenCode avec une logique personnalisée pilotée par les événements.
SGLang est un framework de service LLM haute performance spécialisé dans la génération rapide et structurée pour les workflows JSON, regex et agentiques grâce à son cache de préfixe RadixAttention. Il offre une inférence nettement plus rapide, particulièrement pour les tâches avec des préfixes répétés, ce qui le rend idéal pour les sorties complexes et structurées ainsi que les conversations multi-tours. Choisissez SGLang plutôt que des alternatives comme vLLM lorsque vous avez besoin d'un décodage contraint ou que vous construisez des applications avec un partage étendu de préfixes.
