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commands/qualify-lead.md

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+---
+name: qualify-lead
+description: Evaluate prospect fit and intent to determine next-best action for sales teams.
+usage: /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
+```bash
+/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
+```json
+{
+  "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.
+
+---