Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Align broker-group contracts: dataset_mode, record_type, and expanded summary/prioritization schemas.
What This Skill Does
Scores normalized real-estate leads across sentiment, urgency, intent, and recency dimensions to produce a deterministic 0-100 priority score and P1/P2/P3 bucket assignment. Operates read-only — no writes, messages, or outbound actions. Designed to sit in a multi-agent chain after lead normalization.
Deterministic weighted scoring with explicit evidence tokens makes priority decisions reproducible and auditable, unlike subjective or model-driven ranking that varies between runs.
When to use it
- Ranking a callback queue before the morning sales shift
- Triaging hundreds of inbound leads after a property launch campaign
- Classifying buyer requirements vs inventory listings by follow-up urgency
- Identifying P1 leads signaling immediate possession or same-day access
- Sorting a stale lead backlog when team bandwidth is limited
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Rank these 50 normalized leads by callback priority
- 1Validates input against sentiment-priority-input.schema.json
- 2Computes sentiment_score, intent_score, recency_score, and urgency_score for each lead
- 3Applies record-type logic: boosts intent_score for buyer_requirement records, checks for high-action cues in listing text
- 4Calculates priority_score using the weighted formula and assigns P1, P2, or P3 bucket
- 5Returns scored records with plain-language evidence tokens explaining each score
Ranked list of leads with priority_score, bucket (P1/P2/P3), and evidence tokens per record