India Location Normalizer RE-India
by @vishalgojha
Normalize Indian real-estate location text into canonical city and locality fields (Mumbai and Pune v1) with confidence and unresolved flags. Use when leads...
clawhub install india-location-normalizerπ About This Skill
name: india-location-normalizer description: "Normalize Indian real-estate location text into canonical city and locality fields (Mumbai and Pune v1) with confidence and unresolved flags. Use when leads contain aliases like Goregaon, Andheri W, PCMC, Hinjewadi, Baner, or Wakad. Recommended chain position: lead-extractor then india-location-normalizer then sentiment-priority-scorer. Do not use for writes or outbound actions."
India Location Normalizer
Resolve messy India locality aliases into canonical location fields without side effects.
Quick Triggers
Scruz, Khar, Andheri W, Turner Road, Carter Road.Recommended Chain
message-parser -> lead-extractor -> india-location-normalizer -> sentiment-priority-scorer
Target KPI for production tuning: improve canonical Mumbai/Pune locality resolution versus extractor-only baseline.
Execute Workflow
1. Accept lead-location payload from Supervisor.
2. Validate input against references/location-normalizer-input.schema.json.
3. Use references/india-location-aliases-v1.json as the authoritative lookup map.
4. Match in this order:
- exact alias match (case-insensitive)
- token-normalized alias match (trim punctuation, collapse spaces)
- conservative fuzzy match only when clearly unambiguous
5. Return one normalized location record per input lead with:
- city
- locality_canonical
- micro_market
- matched_alias
- confidence
- unresolved_flag
6. Validate output against references/location-normalizer-output.schema.json.
Enforce Boundaries
Handle Ambiguity
1. If multiple localities match equally, set unresolved_flag: true.
2. If no confident match exists, preserve input in matched_alias and mark unresolved.
3. Prefer false-negative over false-positive for city/locality assignment.