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ajayjoshi-ciphersonic
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indexer

Score and rank B2B sales leads using LLM and rule-based logic.

1|Updated May 5, 2026
Search Tags:#lead scoring#sales automation#B2B marketing#lead ranking#AI sales
One-click install
npx skills add https://github.com/ajayjoshi-ciphersonic/octo-reach --skill indexer-ajayjoshi-ciphersonic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: indexer
Source: https://github.com/ajayjoshi-ciphersonic/octo-reach/tree/main/skills/indexer
Command: npx skills add https://github.com/ajayjoshi-ciphersonic/octo-reach --skill indexer-ajayjoshi-ciphersonic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires psycopg2-binary, beautifulsoup4, lxml, feedparser, requests, websocket-client, python-dotenv, slack_sdk, psycopg2, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates lead indexing, scoring, and ranking in B2B sales, streamlining the process and enabling personalized outreach.

Core Features & Use Cases

  • AI-Powered Scoring: Utilizes LLM intelligence for lead scoring, providing detailed insights into lead quality.
  • Lead Indexing & Ranking: Organizes and prioritizes leads based on ICP fit, intent, timing, authority, and composite score.
  • Outreach Generation: Generates personalized LinkedIn connection notes, DMs, and cold emails.
  • Use Case: Suppose you have a large list of potential leads. Use this Skill to automatically score and rank them, then generate tailored outreach messages for the top leads.

Quick Start

Use the indexer skill to score and rank leads in the database. Run 'indexer score --all' to score all leads.

Frequently Asked Questions about indexer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is AI-driven lead indexing and how does it prioritize B2B sales leads?▼

You need PostgreSQL to store and analyze lead data, and an LLM to power the AI scoring engine. The Skill uses these dependencies to process leads, requiring a configured database environment before running the scoring commands.

Do I need PostgreSQL and an LLM to run automated lead scoring?▼

Yes, you need PostgreSQL to store and analyze lead data, and an LLM to power the AI scoring engine. The Skill uses these dependencies to process leads, requiring a configured database environment before running the scoring commands.

How do I generate personalized outreach messages after ranking B2B leads?▼

After ranking leads, this Skill generates personalized LinkedIn connection notes, DMs, and cold emails automatically. It uses the AI scoring insights and lead data stored in PostgreSQL to tailor outreach messages for the top prioritized leads.

What's the best way to score a large list of B2B sales leads automatically?▼

The best way to score a large list of leads is to use an AI-driven indexing engine that processes your database in bulk. By running the scoring command, the Skill evaluates all stored leads against your criteria and outputs a ranked list.

Can I use Slack notifications with my B2B lead ranking workflow?▼

Yes, the Skill includes Slack SDK integration, allowing you to incorporate notifications into your B2B lead ranking workflow. This enables automated alerts when high-priority leads are scored and indexed in your PostgreSQL database.

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