Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Sentiment Priority Scorer
Score normalized real-estate leads using sentiment, urgency, intent, recency, and record type to produce deterministic priority rankings and P1-P3 buckets. U...
skill openclawclawhub Free
Score normalized real-estate leads using sentiment, urgency, intent, recency, and record type to produce deterministic priority rankings and P1-P3 buckets. U...
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, agents/openai.yaml, references/sentiment-priority-input.schema.json, references/sentiment-priority-output.schema.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- vishalgojha
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Sentiment Priority Scorer
Produce deterministic priority scores for leads without mutating any state.
Quick Triggers
Rank leads by urgency and tone for callback priority. Classify leads into P1/P2/P3 queue. Score follow-up priority from normalized lead records.
Recommended Chain
india-location-normalizer -> sentiment-priority-scorer -> summary-generator
Execute Workflow
Accept input from Supervisor containing normalized leads. Validate input with references/sentiment-priority-input.schema.json. Score each lead with: sentiment_score in range [-1, 1] intent_score in range [0, 1] recency_score in range [0, 1] mapped urgency_score from lead urgency (high=1.0, medium=0.6, low=0.3) Use 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 present Boost intent_score when high-action cues exist in listing text: immediately, keys at office, one day notice, possession, inspection any time Compute priority_score on a 0-100 scale: priority_score = 100 * (0.40*urgency_score + 0.30*intent_score + 0.20*recency_score + 0.10*sentiment_risk) sentiment_risk = max(0, -sentiment_score) Assign buckets: P1 for priority_score >= 75 P2 for priority_score >= 50 and < 75 P3 for < 50 Produce plain-language evidence tokens that explain the score, including record-type evidence. Validate output with references/sentiment-priority-output.schema.json.
Enforce Boundaries
Never write to Google Sheets, databases, or files. Never send messages or trigger outbound channels. Never create reminders or execute actions. Never bypass Supervisor routing or approvals. Never replace upstream urgency; only derive scoring fields.
Handle Errors
Reject schema-invalid inputs. Return field-level reasons when scoring cannot be computed. Fail closed if required scoring features are missing.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Config1 Docs
- SKILL.md
- agents/openai.yaml
- references/sentiment-priority-input.schema.json
- references/sentiment-priority-output.schema.json