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India Location Normalizer RE-India

Name: India Location Normalizer RE-India
Author: vishalgojha

作者 Vishal · GitHub ↗ · v1.0.1

cross-platform ✓ 安全检测通过

504

总下载

当前安装

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在 OpenClaw 中安装

/install india-location-normalizer

功能描述

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

使用说明 (SKILL.md)

India Location Normalizer

Resolve messy India locality aliases into canonical location fields without side effects.

Quick Triggers

Normalize Mumbai/Pune location aliases from extracted leads.
Map PCMC and Hinjewadi variants to canonical localities.
Resolve Mumbai shorthand like Scruz, Khar, Andheri W, Turner Road, Carter Road.
Standardize locality names before scoring or storage.

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

Accept lead-location payload from Supervisor.
Validate input against references/location-normalizer-input.schema.json.
Use references/india-location-aliases-v1.json as the authoritative lookup map.
Match in this order:
- exact alias match (case-insensitive)
- token-normalized alias match (trim punctuation, collapse spaces)
- conservative fuzzy match only when clearly unambiguous
Return one normalized location record per input lead with:
- city
- locality_canonical
- micro_market
- matched_alias
- confidence
- unresolved_flag
Validate output against references/location-normalizer-output.schema.json.

Enforce Boundaries

Never parse raw chat exports.
Never extract non-location entities.
Never write to Google Sheets, databases, or files.
Never send messages or trigger external channels.
Never auto-resolve low-confidence ambiguous aliases.

Handle Ambiguity

If multiple localities match equally, set unresolved_flag: true.
If no confident match exists, preserve input in matched_alias and mark unresolved.
Prefer false-negative over false-positive for city/locality assignment.

安全使用建议

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.

功能分析

Type: OpenClaw Skill Name: india-location-normalizer Version: 1.0.1 The OpenClaw AgentSkills bundle is classified as benign. The `SKILL.md` file clearly defines the skill's purpose as normalizing Indian real-estate location data and, critically, includes an 'Enforce Boundaries' section that explicitly forbids high-risk behaviors such as writing to files or databases, sending messages, or triggering external channels. All other files are either metadata, configuration, or static JSON data/schemas, with no executable code or instructions that suggest malicious intent or introduce vulnerabilities. The skill is designed to be a pure function, processing input against local reference data without side effects.

能力评估

✓ 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.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install india-location-normalizer
安装完成后，直接呼叫该 Skill 的名称或使用 /india-location-normalizer 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.1

Expanded Mumbai alias map with Scruz Khar Turner Road and related localities

v1.0.0

- Initial release of india-location-normalizer skill for standardizing Indian real-estate location text. - Canonicalizes city and locality fields for Mumbai and Pune, including common aliases (e.g., Goregaon, Baner, PCMC). - Adds confidence scoring and unresolved flags to each normalized result. - Follows strict input/output schema validation and limits scope to location normalization only. - Designed for integration after lead extraction and before sentiment scoring in workflow chains.

元数据

Slug india-location-normalizer

版本 1.0.1

许可证 —

累计安装 2

当前安装数 2

历史版本数 2

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