sentiment-priority-scorerScore normalized real-estate leads using sentiment, urgency, intent, recency, and record type to produce deterministic priority rankings and P1-P3 buckets. U...
Install via ClawdBot CLI:
clawdbot install vishalgojha/sentiment-priority-scorerProduce deterministic priority scores for leads without mutating any state.
india-location-normalizer -> sentiment-priority-scorer -> summary-generator
references/sentiment-priority-input.schema.json.sentiment_score in range [-1, 1]intent_score in range [0, 1]recency_score in range [0, 1]urgency_score from lead urgency (high=1.0, medium=0.6, low=0.3)record_type to avoid over-prioritizing generic bulk inventory:buyer_requirement: apply +0.10 intent lift (hard demand signal)inventory_listing: no lift unless high-action cues are presentintent_score when high-action cues exist in listing text:immediately, keys at office, one day notice, possession, inspection any timepriority_score on a 0-100 scale:priority_score = 100 (0.40urgency_score + 0.30intent_score + 0.20recency_score + 0.10*sentiment_risk)sentiment_risk = max(0, -sentiment_score)P1 for priority_score >= 75P2 for priority_score >= 50 and < 75P3 for < 50evidence tokens that explain the score, including record-type evidence.references/sentiment-priority-output.schema.json.Generated Mar 1, 2026
A real estate agency receives hundreds of leads daily from online portals. This skill automatically scores each lead based on sentiment, urgency, and intent to classify them into P1-P3 priority buckets, enabling agents to focus first on hot leads like buyers with immediate possession needs, improving conversion rates and reducing response times.
A property management company handles tenant inquiries via email and chat. By analyzing the sentiment and urgency in messages, this skill prioritizes tickets such as urgent maintenance requests or complaints, ensuring high-priority issues like leaks or safety concerns are addressed promptly while lower-priority ones are queued appropriately.
Real estate sales teams manage follow-ups for leads from open houses or referrals. The skill scores leads based on recency and intent signals like 'immediately available', helping agents prioritize follow-ups with prospects showing high buying intent, such as those requesting inspections, over generic inquiries to maximize sales efficiency.
A real estate marketing firm runs campaigns generating leads from ads. This skill evaluates lead quality by scoring sentiment and record type, distinguishing high-intent buyer requirements from bulk inventory listings, allowing the firm to allocate resources to promising leads and avoid wasting effort on low-priority contacts.
Brokerages use this skill to prioritize leads from multiple sources like websites and social media. By scoring leads into P1-P3 buckets, agents can focus on high-value prospects, increasing commission revenue from faster deal closures and reducing time spent on low-potential leads.
PropTech companies integrate this skill into their CRM platforms as a feature for clients. It helps users automate lead scoring without manual input, enhancing product value and enabling subscription-based revenue from real estate agencies seeking efficiency tools.
Call centers handling lead qualification for developers use this skill to rank inbound inquiries. By prioritizing leads with high urgency and intent, such as buyers ready for possession, they improve callback success rates and generate revenue through performance-based contracts with real estate firms.
💬 Integration Tip
Ensure input data is normalized using the india-location-normalizer skill first, and validate against the provided JSON schemas to avoid errors during scoring.
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