Api Enrichment logo

Api Enrichment

Organization
LeadMagic
api-enrichment

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.

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill nameapi-enrichment
Stars
72
Forks
18
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeadMagic on GitHub. Read the source before you install it.

Installation

Install the Api Enrichment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeadMagic/gtm-skills/tree/main/skills/automation/api-enrichment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/automation/api-enrichment .claude/skills/api-enrichment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Api Enrichment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Api Enrichment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Api Enrichment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 timestamps. A title from two years ago should not override a title returned yesterday. Freshness and source priority are part of the data model.

Webhook Integration Patterns

Async jobs should call back when complete instead of forcing the client to poll. Webhooks need signature verification, retry handling, and event deduplication.

Prerequisites

  • Canonical record ID for every contact/account
  • Source-of-truth CRM fields
  • Provider API keys
  • Rate limits and pricing model for each provider
  • Field-level priority rules
  • Error logging destination
  • Webhook endpoint if processing async jobs

Step-by-Step Process

Phase 1: Define the Enrichment Contract

Field TypeExamplesOwner
Identityname, email, phoneEnrichment pipeline
Companydomain, size, industry, fundingEnrichment pipeline
Sales contextlifecycle stage, owner, next stepCRM / rep
Compliancesuppression, opt-out, consentCompliance system / CRM

Never let enrichment overwrite human-owned sales fields without explicit rules.

Phase 2: Build the Request Model

For every request, include external ID, input fields, provider selected, requested field set, idempotency key, timestamp, and callback URL if async. Use deterministic idempotency keys such as record_id + provider + field_set + date_bucket.

Phase 3: Design the Provider Waterfall

StepUse WhenStop Condition
Primary providerBest cost/coverage for segmentRequired fields returned
Secondary providerPrimary misses critical fieldCritical fields returned
Specialist providerRegion/persona/company-type gapSpecialist field returned
Manual reviewHigh-value account still incompleteHuman decides

Do not call every provider for every record. Waterfall only when the previous step misses.

Phase 4: Implement Reliability Controls

Minimum controls: exponential backoff, dead-letter queue, rate-limit budget, cost ceiling, field source attribution, hash-based dedupe before CRM write, webhook signature verification.

Phase 5: Monitor the Pipeline

Track coverage by field, match rate by provider, cost per enriched record, error rate by provider, latency p50/p95, CRM write success rate, and duplicate rate.

Output Format

markdown
# API Enrichment Pipeline Plan

## Fields Owned
| Field | Source Priority | Freshness Rule | CRM Write Rule |
|---|---|---|---|

## Provider Waterfall
| Step | Provider Type | Trigger | Stop Condition | Cost Guardrail |
|---|---|---|---|---|

## Request Contract
- Required inputs:
- Idempotency key:
- Retry policy:
- Webhook policy:

## Monitoring
- Coverage:
- Cost:
- Errors:
- Latency:

Quality Check

Before delivering, verify:

  • Every field has source attribution
  • Retry logic is idempotent
  • Provider calls stop when required fields are found
  • CRM writes cannot duplicate records
  • Rate limits and cost ceilings are defined
  • Webhooks include verification and dedupe
  • Monitoring covers coverage, cost, errors, latency, and CRM writes

Common Pitfalls

  1. Looping over contacts without idempotency. Retries create duplicates. Fix: idempotency keys and upserts.
  2. Calling all providers every time. Costs explode. Fix: waterfall by stop condition.
  3. Overwriting rep-owned fields. Enrichment corrupts active deals. Fix: field ownership rules.
  4. Silent failures. Missing data looks like no match. Fix: explicit error states and dead-letter queue.
  5. No freshness rule. Old data overwrites newer CRM data. Fix: compare timestamps and source priority.
  6. Polling huge jobs. Polling wastes compute and API calls. Fix: signed webhooks with retry handling.

Execution Artifacts

  • references/framework-notes.md — named frameworks, citation anchors, and operating assumptions
  • templates/output-template.md — copy-paste deliverable structure for the user
  • scripts/check-output.py — local checklist validator for required sections This skill includes lightweight artifacts the agent can load on demand: Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.

Related Skills

  • waterfall-enrichment — multi-provider enrichment design
  • data-enrichment-strategy — source selection and data quality
  • crm-integration — CRM writes and lifecycle fields
  • campaign-analytics — measuring enrichment impact on pipeline

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Api Enrichment AI skill do?

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.

Why use Api Enrichment on TypingMind?

Because you install it once and use it with any model. Api Enrichment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Api Enrichment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/automation/api-enrichment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Api Enrichment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Api Enrichment?

As many as you like. As long as a model supports skills, you can use Api Enrichment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Api Enrichment AI skill free?

Yes. It is published on GitHub by LeadMagic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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