技能 air-quality

📦

air-quality

Name: air-quality
Author: tahodev

內容修訂 r1 安全 🌐 網路存取📁 檔案系統存取⚙️ 外部命令

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.

支援: Claude Codex Code(CC)

🥉 77 銅級

Maintain this Skill? Claim it 🥉 77 銅級

使用我的智慧代理安裝

將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。

智能體請求

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.

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/tahodev-air-quality.md 已簽名 manifest GET /api/skills/tahodev-air-quality/manifest 已簽名 lockfile GET /api/skills/tahodev-air-quality/lockfile

測試它

正在使用「air-quality」。 Show the documented Amagasaki PM2.5 example from September 26, 2026.

預期結果:

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.

正在使用「air-quality」。 Summarize the documented daily sample for station 13101010.

預期結果:

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.

安全審計

安全

v1 • 2026/10/13 開啟版本化報告

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.

已掃描檔案

223

分析行數

複核項目

已忽略的誤報

風險因素

🌐 網路存取 (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

📁 檔案系統存取 (7)

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

⚙️ 外部命令 (31)

最近一次完成的靜態與語意稽核未偵測到已確認的安全發現。這並不能證明該 Skill 沒有副作用。

審計單位: codex

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

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

Markdown 徽章

[![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 徽章

<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>

嵌入卡片

<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>

學術引用 (APA · BibTeX · CFF)

APA 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 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 評分

評分依據證據置信度: 中

架構

可維護性

內容

社群

規範符合度

你可以打造什麼

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.

試試這些提示

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.

最佳實務

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.

避免

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.