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India Location Normalizer RE-India

v1.0.1

Normalize Indian real-estate location text into canonical city and locality fields (Mumbai and Pune v1) with confidence and unresolved flags. Use when leads...

⭐ 0· 457·2 current·2 all-time
byVishal@vishalgojha
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
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Benign
high confidence
✓
Purpose & Capability
The name/description (India locality normalization for Mumbai and Pune) match the included artifacts: a city/locality alias JSON and input/output JSON schemas. There are no unrelated env vars, binaries, or install steps requested.
✓
Instruction Scope
SKILL.md gives a narrow, well-defined runtime flow: accept a lead payload, validate input schema, consult the included aliases JSON, apply exact/normalized/fuzzy matching rules, produce a single normalized record validated against the included output schema, and avoid side effects. The instructions do not reference external endpoints, unrelated files, or open-ended data collection.
✓
Install Mechanism
No install spec and no code files are present — the skill is instruction-only and uses only local reference files bundled with the skill. This is the lowest-risk install profile.
✓
Credentials
The skill requests no environment variables, credentials, or config paths. That matches the described functionality (local lookup + validation) and is proportionate.
✓
Persistence & Privilege
always is false and the skill does not request system-wide persistence or modification of other skills. Autonomous invocation is allowed (platform default) but the skill's instructions explicitly forbid external side effects, keeping its privilege footprint minimal.
Assessment
This skill is internally consistent and low-risk: it only uses bundled lookup and schema files to normalize Mumbai/Pune location aliases and asks for no credentials or installs. Before enabling in production, verify (1) the full alias JSON included in the bundle is the expected, up-to-date authoritative list (the excerpt in the listing was truncated), (2) your Supervisor/enforcer actually prevents outbound actions (the SKILL.md says 'never write' or 'never send', but enforcement depends on your runtime policies), and (3) run representative test leads to confirm the conservative fuzzy-match behavior and confidence scoring meet your tolerance for false negatives. If you need normalization for other cities, expect the skill to require additional, explicit alias data.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

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