Compétences address-normalize

📦

address-normalize

Name: address-normalize
Author: tahodev

Révision du contenu r1 Risque faible 🌐 Accès réseau📁 Accès au système de fichiers⚙️ Commandes externes

Normalize Japanese Addresses with Public ABR Data

Japanese addresses contain inconsistent numerals and omitted administrative names. This skill uses public ABR masters to identify address components and codes while preserving the remaining text.

Prend en charge: Claude Codex Code(CC)

📊 74 Adéquat

Maintain this Skill? Claim it 📊 74 Adéquat

Installer avec mon Agent

Copiez cette demande dans votre Agent. Elle contient la page Skill de référence et le manifeste.

Demande d’agent

Review the Skillstore skill "address-normalize" from https://skillstore.io/skills/tahodev-address-normalize.md and its manifest at https://skillstore.io/api/skills/tahodev-address-normalize/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Votre Agent doit toujours présenter son plan et demander toute confirmation exigée par la politique de sécurité.

Ressources lisibles par les agents

Utilisez ces liens lorsqu’un Agent IA, un crawler ou un script a besoin d’un contexte propre au lieu de lire toute la page.

Détail Markdown GET /skills/tahodev-address-normalize.md manifest signé GET /api/skills/tahodev-address-normalize/manifest lockfile signé GET /api/skills/tahodev-address-normalize/lockfile

Tester

Utilisation de « address-normalize ». Normalize 東京都千代田区内幸町1丁目1-1.

Résultat attendu:

Prefecture: Tokyo. Municipality: Chiyoda.
Town: Uchisaiwaicho, chome 1. Municipality code: 131016. Town identifier: 0001001.
Remaining text: 1-1. The administrative and town components matched; the street number was not validated.

Utilisation de « address-normalize ». Normalize 新宿区歌舞伎町一丁目4-1.

Résultat attendu:

Prefecture: Tokyo, inferred from Shinjuku municipality.
Town: Kabukicho, chome 1. The kanji numeral matched the town master.
Remaining text: 4-1. Street-level validation is outside this result.

Utilisation de « address-normalize ». Normalize 東京都千代田区内幸町 without a chome.

Résultat attendu:

Prefecture: Tokyo. Municipality: Chiyoda.
No town match was selected because the candidate records require a chome.
Remaining text: 内幸町. Review the missing chome before accepting the address.

Audit de sécurité

Risque faible

v1 • 13/10/2026 Ouvrir le rapport versionné

Most static alerts are false positives from Markdown, public data URLs, and scoped cache access. Three findings identify one unbounded download and ZIP decompression risk; a semantic finding identifies abolished-record acceptance. No evidence found of prompt injection, credential access, address exfiltration, or malicious intent.

Fichiers analysés

320

Lignes analysées

Éléments à examiner

Faux positifs ignorés

Préoccupations de sécurité confirmées (1)

Faible

lookup.py:31-32 lookup.py:65-88 SKILL.md:40

Abolished records remain eligible for current-address matches

The documentation requires excluding records with ablt_date, but the helper loads and matches every row. Retired administrative or town records can receive successful matches.

The loader and matching code never check ablt_date, although the documentation explicitly requires it. This establishes a data-integrity defect, not malicious intent.

Éléments d’évaluation des capacités (3)

Il s’agit de capacités locales réelles qui peuvent être attendues pour cette compétence ; elles nécessitent donc un examen, mais ne sont pas comptabilisées comme comportement malveillant confirmé.

Moyen

lookup.py:28

Fetch API call

z = zipfile.ZipFile(io.BytesIO(fetch(url)))

This loads the entire downloaded archive into memory without a byte cap. An oversized upstream response could exhaust memory despite the request timeout.

Moyen

lookup.py:20

Python HTTP libraries

with urllib.request.urlopen(req, timeout=timeout) as r:

The response is immediately consumed through unrestricted r.read() on line 21. A timeout does not bound memory consumption from an oversized response.

Moyen

lookup.py:28

Python archive libraries

z = zipfile.ZipFile(io.BytesIO(fetch(url)))

The archive member is decompressed through unrestricted z.read(inner) on line 30. A highly compressed oversized member could exhaust memory or disk.

Facteurs de risque

🌐 Accès réseau (11)

lookup.py:18 lookup.py:28 lookup.py:12 lookup.py:19 lookup.py:20 lookup.py:14 SKILL.md:20 SKILL.md:21 SKILL.md:22 SKILL.md:25 SKILL.md:26

📁 Accès au système de fichiers (13)

lookup.py:9 lookup.py:15 lookup.py:9 lookup.py:15 lookup.py:30 lookup.py:24 lookup.py:27 lookup.py:28 SKILL.md:23 SKILL.md:24 SKILL.md:66 SKILL.md:62 SKILL.md:62

⚙️ Commandes externes (28)

Audité par: codex

Partager et citer ce rapport

Partagez le rapport d’évaluation versionné, le badge neutre, la carte intégrable et les citations. Skillstore présente les preuves sans décider si ce Skill est sûr.

Ouvrir le rapport versionné

Copier le lien du rapport

https://skillstore.io/skills/tahodev-address-normalize/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Badge Markdown

[![Skillstore security assessment](https://skillstore.io/badges/skills/tahodev-address-normalize/security.svg)](https://skillstore.io/skills/tahodev-address-normalize?utm_source=security_passport_badge)

Badge HTML

<a href="https://skillstore.io/skills/tahodev-address-normalize?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/tahodev-address-normalize/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Carte intégrable

<iframe src="https://skillstore.io/embed/skills/tahodev-address-normalize.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Citations académiques (APA · BibTeX · CFF)

Citation APA

tahodev. (2026). address-normalize security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/tahodev-address-normalize/audits/1

Citation BibTeX

@techreport{tahodev-tahodev-address-normalize-2026,
  author = {tahodev},
  title = {address-normalize security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/tahodev-address-normalize/audits/1},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "address-normalize security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "tahodev"
date-released: "2026-10-13"
url: "https://skillstore.io/skills/tahodev-address-normalize/audits/1"
identifiers:
  - type: other
    value: "skillstore:tahodev-address-normalize:audit:1"
    description: "Skillstore immutable audit report identifier"

Score Skillstore

Pourquoi ce score Niveau de confiance des preuves: Moyen

Architecture

Maintenabilité

Contenu

Communauté

Conformité aux spécifications

Ce que vous pouvez créer

Prepare customer address records

Separate administrative components and identifiers before reviewing unmatched or ambiguous customer addresses.

Explore municipal town masters

List towns for a selected prefecture and compare keyword results with the published ABR records.

Prototype an address import workflow

Use the Python helper to extract structured address fields before adding current-record filtering and application-specific validation.

Essayez ces prompts

Parse one address

Normalize 東京都千代田区内幸町1丁目1-1 with this skill. Present the prefecture, municipality, town, identifiers, and remaining text in plain language.

Compare numeral variants

Compare 新宿区歌舞伎町一丁目4-1 and 新宿区歌舞伎町１丁目4-1. Show whether the town identifiers match and retain each remaining address portion.

Find town candidates

List Tokyo town records with prefecture code 13 and keyword 歌舞伎. Present municipality codes, town identifiers, and labels without claiming delivery validation.

Review an address import

Review these addresses: [address list]. Check ambiguous municipalities, abolished records, missing chome, and cache age. Separate reliable matches from unresolved entries.

Bonnes pratiques

Refresh masters when freshness matters, then independently exclude abolished records and verify ambiguous municipality matches.
Review unmatched results and remaining text before importing records or using them for operational decisions.
Attribute the Digital Agency's Address Base Registry and follow the documented data licensing terms.

Éviter

Treating a successful town match as proof that a street number exists or that an address is deliverable.
Assuming --pref constrains address parsing or that omitted prefectures always produce an unambiguous municipality.
Using cached or abolished records as current authoritative addresses without reviewing master dates and status fields.

Foire aux questions

Which assistants can use this skill?

The report lists Claude, Codex, and Claude Code. Running the included helper requires an environment with Python 3.

Does it require an API key or account?

No. The helper downloads public ABR master files without credentials.

Are input addresses sent to a remote service?

The helper matches addresses locally. Its requests download public masters and do not include the input address.

Can it work offline?

Yes, when all required master CSV files are already cached. Initial downloads and requested refreshes require network access.

Does it validate street numbers or return coordinates?

No. Street numbers and building details remain outside its normalization scope. Geocoding requires a separate tool.

Does it guarantee a current, unambiguous address match?

No. The helper does not exclude abolished records or reject ambiguous municipalities. The --pref option selects town-list data but does not constrain address parsing.