lead-management

Name: lead-management
Availability: InStock
Author: Yoodaddy

Designs lead scoring models, nurture sequences, and MQL-to-SQL handoff workflows for CRM pipelines.

3|2|Updated Feb 13, 2026

Search Tags:#crm #lead scoring #sql #pipeline #lead qualification #bant #mql

Ready to run

npx skills add https://github.com/Yoodaddy0311/artibot --skill lead-management-yoodaddy0311

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lead-management
Source: https://github.com/Yoodaddy0311/artibot/tree/main/plugins/artibot/skills/lead-management
Command: npx skills add https://github.com/Yoodaddy0311/artibot --skill lead-management-yoodaddy0311

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Sales and marketing teams lose revenue when leads stall between stages, scoring models drift out of calibration, and MQL-to-SQL handoffs lack clear SLAs and qualification criteria. ## Core Features & Use Cases - Lead Scoring Models: Build explicit (fit) and implicit (engagement) scoring systems with point values, decay rules, and threshold-based stage gates. - Lifecycle & Handoff Design: Define Raw Lead through Customer stages with owners, SLAs, and BANT-based MQL-to-SQL qualification processes. - Nurture & Routing Workflows: Plan nurture sequences by segment and routing rules (round-robin, territory, score-based) with pipeline velocity metrics. - Use Case: A B2B SaaS team notices only 8% of MQLs convert to SQL. Use this Skill to audit the scoring thresholds, recalibrate against closed-won data, and redesign the BDR handoff SLA. ## Quick Start Ask the AI to design a lead scoring model with MQL and SQL thresholds for your B2B sales pipeline.

Frequently Asked Questions about lead-management

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

FAQPage Schema

How do I build a lead scoring model for B2B sales?▼

Combine explicit fit signals (title, company size, industry) with implicit engagement signals (page views, email clicks, demo requests), assign point values to each, and set thresholds that map score ranges to stages like MQL, SAL, and SQL.

What is the difference between MQL and SQL in a sales pipeline?▼

An MQL meets marketing's scoring threshold and enters nurture, while an SQL has passed sales qualification (typically BANT: Budget, Authority, Need, Timeline) and is ready for an account executive to work as an active opportunity.

How fast should sales respond to a new MQL?▼

Respond within 5 minutes when possible, since conversion rates drop sharply after that window. Define SLAs per stage, such as under 5 minutes for hot leads and under 1 hour for warm leads, with escalation rules for stalls.

When should lead scoring models be recalibrated?▼

Recalibrate monthly or quarterly by comparing win rates across score bands against targets. If conversion by band deviates more than about 10%, adjust point values, thresholds, or signal weights based on closed-won analysis.

When is lead scoring not the right approach?▼

Lead scoring does not fit product analytics or user retention work that lacks a sales pipeline, CRM handoff, or qualification stage. For those scenarios, use product analytics or marketing analytics approaches instead.