gh-gtmagents-gtm-agents-plugins-sales-prospecting/commands/qualify-lead.md

name, description, usage

name	description	usage
qualify-lead	Evaluate prospect fit and intent to determine next-best action for sales teams.	/sales-prospecting:qualify-lead --company "Acme" --persona "VP Revenue" --signals intent.json

Qualify Lead Command

Score leads against ICP, intent signals, and buying triggers to decide whether to route, recycle, or nurture.

Command Syntax

/sales-prospecting:qualify-lead \
  --company "<name>" \
  --persona "<title>" \
  --firmographics firmo.json \
  --technographics tech.json \
  --signals intent.json \
  --threshold 75

Parameters

• --company: Account name or domain.
• --persona: Primary buyer role under evaluation.
• --firmographics: JSON/CSV input of company attributes.
• --technographics: Technology stack details.
• --signals: Intent data (product usage, web visits, 3rd-party intent).
• --threshold: Minimum composite score to pass to sales (default 70).
• --stage: Lead stage (MQL, PQL, recycled) to influence scoring weights.

Workflow

1. Data Normalization – clean and map firmographic, technographic, and behavior fields.
2. Fit Scoring – apply weighted ICP model (industry, size, geography, use case, tech stack compatibility).
3. Intent Scoring – incorporate behavioral data (content engagement, trials, intent providers, product telemetry).
4. Timing Assessment – evaluate trigger events (funding, hires, tech churn) plus buying cycle alignment.
5. Recommendation Engine – produce route/recycle/nurture guidance with rationale, next steps, and owner.

Output Schema

{
  "company": "Acme Corp",
  "fit_score": 82,
  "intent_score": 76,
  "composite_score": 79,
  "decision": "route-to-ae",
  "urgency": "high",
  "rationale": [
    "ICP match: SaaS, 200 employees, US",
    "High technographic overlap",
    "Recent product trial and pricing page visits"
  ],
  "next_steps": [
    "Assign to AE Sarah Lee",
    "Send enterprise case study",
    "Schedule discovery call within 48h"
  ]
}

Best Practices

• Keep scoring models transparent so SDRs/RevOps can adjust weights.
• Blend qualitative notes (call transcripts) with quantitative data.
• Auto-sync outcomes to CRM and track model drift monthly.
• Use different thresholds for inbound vs outbound vs product-led leads.

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