📦

air-quality

Name: air-quality
Author: tahodev

コンテンツリビジョン r1 安全 🌐 ネットワークアクセス📁 ファイルシステムへのアクセス⚙️ 外部コマンド

Check Air Quality Across Japan

Japanese air quality measurements are spread across station snapshots, histories, and advisory feeds. This skill retrieves public AEROS data and reports provisional readings with station names and measurement times.

対応: Claude Codex Code(CC)

🥉 77 ブロンズ

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自分のエージェントでインストール

このリクエストをエージェントにコピーしてください。正規の Skill ページとマニフェストが含まれています。

エージェントリクエスト

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

エージェントは引き続き計画を提示し、セキュリティポリシーで必要な確認を求める必要があります。

エージェントが読めるリソース

AI エージェント、クローラー、スクリプトがページ全体ではなく整理されたコンテキストを必要とする場合は、これらのリンクを使ってください。

Markdown詳細 GET /skills/tahodev-air-quality.md 署名済み manifest GET /api/skills/tahodev-air-quality/manifest 署名済み lockfile GET /api/skills/tahodev-air-quality/lockfile

テストする

「air-quality」を使用しています。 Show the documented Amagasaki PM2.5 example from September 26, 2026.

期待される結果:

Documented sample, not a current lookup: September 26, 2026, 01:00 JST.
National Amagasaki Motor Vehicle Traffic Environmental Monitoring Station: PM2.5 was 28 micrograms per cubic meter.
Station type: roadside vehicle-emission monitoring. This reading is provisional.
Source: AEROS nationwide hourly CSV described in the skill documentation.

「air-quality」を使用しています。 Summarize the documented daily sample for station 13101010.

期待される結果:

Documented sample, not a current lookup: September 26, 2026, 01:00 JST, station 13101010.
PM2.5: 7 micrograms per cubic meter. Temperature: 24 degrees Celsius. Humidity: 73 percent.
Source: the AEROS station daily CSV example. Values are provisional, not finalized statistics.

セキュリティ監査

安全

v1 • 2026/10/13 バージョン別レポートを開く

All 70 static findings are false positives after reviewing their source context. Network requests retrieve public AEROS data, and filesystem operations maintain a dedicated CSV cache. No evidence found of credential access, data exfiltration, executable obfuscation, or prompt injection.

スキャンされたファイル

223

解析済み行数

レビュー項目

誤検知を無視

リスク要因

🌐 ネットワークアクセス (17)

lookup.py:17 lookup.py:23 lookup.py:32 lookup.py:12 lookup.py:18 lookup.py:19 lookup.py:14 SKILL.md:20 SKILL.md:21 SKILL.md:22 SKILL.md:23 SKILL.md:24 SKILL.md:25 SKILL.md:30 SKILL.md:54 SKILL.md:64 SKILL.md:76

📁 ファイルシステムへのアクセス (7)

lookup.py:9 lookup.py:15 lookup.py:9 lookup.py:15 lookup.py:35 lookup.py:28 SKILL.md:83

⚙️ 外部コマンド (31)

最新の完了済み静的・セマンティック監査では、確認済みのセキュリティ検出事項は見つかりませんでした。これは、スキルに副作用がないことを証明するものではありません。

監査者: codex

このレポートを共有・引用

バージョン付き評価レポート、中立的なバッジ、埋め込みカード、引用を共有できます。Skillstore は証拠を報告しますが、この Skill が安全かどうかは判断しません。

バージョン別レポートを開く

レポートリンクをコピー

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

Markdownバッジ

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

HTMLバッジ

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

埋め込みカード

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

学術引用 (APA · BibTeX · CFF)

APA形式の引用

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

BibTeX形式の引用

@techreport{tahodev-tahodev-air-quality-2026,
  author = {tahodev},
  title = {air-quality security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/tahodev-air-quality/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: "air-quality security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "tahodev"
date-released: "2026-10-13"
url: "https://skillstore.io/skills/tahodev-air-quality/audits/1"
identifiers:
  - type: other
    value: "skillstore:tahodev-air-quality:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore スコア

このスコアの理由証拠の信頼度: 中

アーキテクチャ

保守性

コンテンツ

コミュニティ

仕様準拠

作成できるもの

Review Local Station Readings

Find recent PM2.5 readings for a municipality and distinguish roadside stations from general ambient monitoring stations.

Compare Provisional Station Histories

Retrieve daily, seven-day, or monthly station records for a defined region while preserving missing readings and reporting delays.

Prepare a Sourced Air Quality Update

Summarize station measurements and published PM2.5 advisories with timestamps, source references, and clear provisional-data labels.

これらのプロンプトを試す

Check a Municipality

Show the latest PM2.5 readings for [municipality]. Include station names, station types, and measurement times in JST.

Rank Prefecture Stations

List the five highest measured [pollutant] readings in prefecture [code]. Label provisional values and identify missing readings separately.

Summarize a Station History

Retrieve the last seven days for station [code]. Summarize PM2.5 changes, report missing hours, and preserve measurement timestamps.

Review Monthly Data and Advisories

Retrieve monthly PM2.5 records for [prefecture code] and [month]. Summarize station coverage, missing data, reporting delays, and current official PM2.5 advisories.

ベストプラクティス

Include the station name, station type, measurement time in JST, and provisional-data label with every reported reading.
Use published metadata for the latest hourly URL and verify the CSV header before interpreting a successful HTTP response.
Keep missing readings explicit and narrow historical requests by station, measurement, and period.

回避

Treating hourly provisional readings as finalized statistics or personal medical guidance.
Replacing missing readings with guessed values or presenting delayed station records as current.
Downloading an entire monthly prefecture dataset when a latest-hour snapshot or daily station CSV meets the request.