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what is lead-enrichment-scoring fr?

rqcai200/lead-enrichment-scoring — explained in plain English

Analysis updated 2026-05-18

23PythonAudience · pm founderLicense

tl;dr

A guide and template for building a low-cost LinkedIn lead enrichment and scoring pipeline that writes results back into your CRM, instead of paying per-lead fees to tools like Clay.

vibe map

mindmap
  root((lead-enrichment-scoring))
    What it does
      Enrich leads
      Score leads
      Write back to CRM
      Refresh on schedule
    Tech stack
      Python
      Apify
      GitHub Actions
      REST API
    Use cases
      Sales lead scoring
      Creator sourcing
      CRM refresh
    Audience
      Founders
      Growth teams

Code map

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what do people make with this?

VIBE 1

Automatically enrich new inbound CRM leads with LinkedIn profile data at a fraction of the cost of paid enrichment tools.

VIBE 2

Score leads with your own custom formula based on title, company, and audience size.

VIBE 3

Refresh an existing CRM's contact records on a schedule to catch job changes or new activity.

VIBE 4

Identify active creators or prospects by checking recent posting activity before scoring them highly.

what's the stack?

PythonApifyGitHub ActionsREST API

how it stacks up fr

rqcai200/lead-enrichment-scoringaaravkashyap12/advise-project-approachabu-rayhan-alif/django-saas-kit
Stars232323
LanguagePythonPythonPython
Setup difficultyeasymoderate
Complexity2/53/5
Audiencepm founderdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

in plain english

This repository is a guide and template for building your own lead enrichment and scoring pipeline, aimed at businesses that pull in inbound sales leads and need to check them out and rank them automatically. The author built it after finding that a popular paid tool called Clay was costing over 700 dollars a month for only a few thousand leads, so this guide walks through recreating the same result for about 0.004 dollars per lead instead. The core idea is a four step pipeline. First, a new lead lands in your CRM and gets enriched by pulling their LinkedIn profile data through a scraping service called Apify, with a fallback lookup for leads that only have an email address. Second, the enriched profile is scored using a formula you write yourself, weighing things like job title, company, and audience size, with an optional extra check on recent posting activity for leads that score well. Third, the score and enriched data get written back to the CRM through its API. Fourth, an optional refresh loop re-runs the whole process periodically to catch changes like a new job or a viral post. To use the repository, you read the README and a companion build guide, fill in your own scoring formula in a template file, copy an example environment file and add your own account tokens, then hand the build guide to a coding agent, which is instructed to build the actual pipeline and set it up to run automatically on GitHub Actions. The project works with any CRM that has a REST API, such as Attio, HubSpot, Salesforce, Pipedrive, or Airtable, and requires an Apify account for the enrichment step. Two safeguards prevent mistakes: a blocklist for placeholder LinkedIn URLs, and a name matching check that flags a profile as belonging to the wrong person before it gets scored. The project is released under the MIT license.

prompts (copy fr)

prompt 1
Using this repo's BUILD.md, implement the enrich, score, and write-back pipeline against my Attio CRM.
prompt 2
Help me fill in the lead_score.template.py file with weights for job title, company size, and audience.
prompt 3
Set up the GitHub Actions workflow from this repo to run the lead enrichment pipeline on a daily cron schedule.
prompt 4
Explain how the name matching guard in this repo prevents enriching the wrong LinkedIn profile.

Frequently asked questions

what is lead-enrichment-scoring fr?

A guide and template for building a low-cost LinkedIn lead enrichment and scoring pipeline that writes results back into your CRM, instead of paying per-lead fees to tools like Clay.

What language is lead-enrichment-scoring written in?

Mainly Python. The stack also includes Python, Apify, GitHub Actions.

Who is lead-enrichment-scoring for?

Mainly pm founder.

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