crm-lead-manager_skill

This skill optimizes lead intake, scoring, routing, and follow-up cadences for ad-driven pipelines across Meta, Google, TikTok, and YouTube.
  • Python

2.6k

GitHub Stars

3

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill crm-lead-manager

  • _meta.json297 B
  • metadata.json68 B
  • SKILL.md3.9 KB

Overview

This skill manages ad-generated leads and pipeline routing across Meta, Google Ads, TikTok, and YouTube. It focuses on lead scoring, routing SLAs, follow-up cadence, and actionable recovery steps to protect conversion velocity and ad spend. The goal is measurable, channel-aware recommendations that drive faster responses and higher-quality handoffs.

How this skill works

The skill validates incoming lead payloads against required fields, applies explicit scoring rules (intent, fit, urgency), and classifies leads into priority buckets. It then generates routing payloads with SLA targets, follow-up cadences, and pipeline risk alerts, plus remediation actions when leakage or policy/billing risks appear. All recommendations are channel-specific and include rollback/stop-loss conditions when spend or compliance risk is detected.

When to use it

  • When ad performance ties directly to revenue, ROAS, CPA, or budget efficiency decisions.
  • When you need platform-level lead handling for Meta, Google Ads, TikTok, or YouTube.
  • When lead quality drops, SLA breaches occur, or pipeline leakage increases.
  • When mixing regions, languages, or multiple ad channels into a single intake.
  • When defining handoff rules between marketing automation and sales teams.

Best practices

  • Require minimum lead_payload_fields and validate schema before processing.
  • Score leads with transparent, additive rules (intent + fit + urgency) and publish thresholds.
  • Implement same-day response SLA for high-intent leads; route incomplete records to an enrichment queue.
  • Keep channel-aware playbooks: prioritize creative testing for Meta/TikTok and intent capture for Google/YouTube.
  • Always include a rollback or stop-loss condition for spend increases or uncertain tracking changes.

Example use cases

  • Lead quality drop from Meta and Google: update scoring thresholds, tighten routing, and feedback targeting adjustments to ad teams.
  • SLA breach where sales delays responses: enforce escalation payloads, notify managers, and adjust SLA_hours to restore conversion velocity.
  • Multi-market routing: map US/EU ownership, set language-based queues, and automate fallback enrichment for mismatched leads.
  • High-risk spend increase: add stop-loss at campaign level, pause creative cohorts, and trigger validation checklist for tracking and billing.

FAQ

Provide lead_source, lead_payload_fields (validated schema), and a clear qualification_goal; optional routing_rules and response_sla improve accuracy.

How does the skill handle missing critical fields?

Missing critical fields route the lead to an enrichment queue and generate a minimal input request that lists only the fields needed to continue processing.

How are channel differences preserved in recommendations?

Recommendations are channel-aware: creative cadence for Meta/TikTok, query-intent prioritization for Google/YouTube, and separate routing/playbooks per channel with measurable actions.

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