api-enrichment

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Build API-first enrichment pipelines — provider selection, batching, retries, rate limits, idempotency, webhooks, cost tracking, and CRM writes. Use when integrating enrichment APIs directly into GTM systems or replacing manual data workflows with programmatic pipelines.

AI & Automation 49 stars 14 forks Updated 4 days ago MIT

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Skill Content

# API Enrichment ## Overview API enrichment fails when teams treat it like a simple loop over contacts. The hard parts are not the HTTP calls — they are data quality, deduplication, retry safety, rate limiting, cost controls, and knowing when not to enrich. This skill builds a production-ready enrichment pipeline that can process leads, contacts, and accounts without silent failures or uncontrolled vendor spend. ## When to Use Use this skill when the user asks to: "build an enrichment API pipeline", "integrate enrichment directly into our app", "set up automated enrichment via API", "replace manual enrichment with API calls", "batch enrich contacts", "add enrichment to inbound routing", "push enriched data to CRM", or "handle enrichment retries and webhooks". Do not use this for tool-specific no-code workflows. Use `clay-automation` or `n8n-automation` when the user wants a no-code platform. ## Authoritative Foundations ### REST API Best Practices — Idempotency and Retries Any bulk enrichment job needs idempotent writes and retry-safe requests. A retry should never create duplicate contacts, duplicate CRM notes, or duplicate sequence enrollment. ### DAMA-DMBOK — Data Quality Dimensions Provider waterfalls improve coverage and cost control by trying the best-fit source first, then falling back only when needed. This avoids paying multiple providers for the same field. ### Data Quality Management — Source Priority and Freshness Fields need source attribution and times...

Details

Author
LeadMagic
Repository
LeadMagic/gtm-skills
Created
3 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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