スキル new-term-radar
📦

new-term-radar

コンテンツリビジョン r3 安全 ⚙️ 外部コマンド🌐 ネットワークアクセス📁 ファイルシステムへのアクセス🔑 環境変数

Find Emerging AI Keywords Worth Researching

New AI launches create search opportunities that are difficult to compare across sources. This skill uses a research CLI to turn discovery signals into keyword priorities.

対応: Claude Codex Code(CC)
Maintain this Skill? Claim it 🥉 77 ブロンズ

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 "new-term-radar" from https://skillstore.io/skills/zhuyansen-new-term-radar.md and its manifest at https://skillstore.io/api/skills/zhuyansen-new-term-radar/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。

テストする

「new-term-radar」を使用しています。 Summarize a first daily run that reports GitHub breakouts but few other leads.

期待される結果:

  • First-run caveat: this run established snapshots, so limited results do not demonstrate a quiet market.
  • Review the observed GitHub leads and their source links.
  • Reassess after new source activity rather than immediately repeating the same daily run.

「new-term-radar」を使用しています。 Explain a supplied keyword row marked watch, demoted, and crowded.

期待される結果:

  • Decision: watch.
  • The radar moved this row from prioritize to watch because page-one competition is crowded.
  • Recheck the search results before committing to a new page.

「new-term-radar」を使用しています。 Explain a flat trend row without country-level data when a related-search source also failed.

期待される結果:

  • Trend: no rise seen in the available worldwide series.
  • The newest two days are excluded, so this does not prove that interest did not rise.
  • Related-search collection failed, leaving the evidence incomplete.

セキュリティ監査

安全

All 50 static findings are false positives involving Markdown formatting, ordinary authentication requirements, public reference links, and unrelated prose. The instructions require paid-run approval and treat external content as untrusted; no evidence found of credential theft, destructive intent, or prompt injection. This review covers SKILL.md only, not the external PyPI package or its dependencies.

1
スキャンされたファイル
93
解析済み行数
0
レビュー項目
0
誤検知を無視
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 監査履歴を表示 →
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
セキュリティ評価

レポートリンクをコピー

https://skillstore.io/skills/zhuyansen-new-term-radar/audits/3?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdownバッジ

[![Skillstore security assessment](https://skillstore.io/badges/skills/zhuyansen-new-term-radar/security.svg)](https://skillstore.io/skills/zhuyansen-new-term-radar?utm_source=security_passport_badge)

HTMLバッジ

<a href="https://skillstore.io/skills/zhuyansen-new-term-radar?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/zhuyansen-new-term-radar/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

埋め込みカード

<iframe src="https://skillstore.io/embed/skills/zhuyansen-new-term-radar.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

zhuyansen. (2026). new-term-radar security audit report (audit version 3) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zhuyansen-new-term-radar/audits/3

BibTeX形式の引用

@techreport{zhuyansen-zhuyansen-new-term-radar-2026, author = {zhuyansen}, title = {new-term-radar security audit report (audit version 3)}, institution = {Skillstore}, year = {2026}, number = {3}, url = {https://skillstore.io/skills/zhuyansen-new-term-radar/audits/3}, 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: "new-term-radar security audit report (audit version 3)" version: "unspecified" type: report authors: - name: "zhuyansen" date-released: "2026-10-28" url: "https://skillstore.io/skills/zhuyansen-new-term-radar/audits/3" identifiers: - type: other value: "skillstore:zhuyansen-new-term-radar:audit:3" 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
55
アーキテクチャ
85
保守性
87
コンテンツ
65
コミュニティ
83
仕様準拠

作成できるもの

Prioritize AI content topics

Compare emerging names and generic queries to decide which pages deserve research, monitoring, or no further work.

Investigate tool demand

Review complaint categories and linked evidence to identify problems that a new or improved tool might address.

Monitor emerging AI products

Track new model and repository names, then separate early attention from sustained search interest using source and trend evidence.

これらのプロンプトを試す

Discover today's AI names
Find today's new AI names and summarize their strongest signals. Show the estimated cost and duration, then wait for my approval before running.
Research names I already have
Research PhotoCraft and SynthID Detector without discovery. Confirm the estimated cost first, then compare named and generic keywords with decisions and evidence.
Investigate customer complaints
Investigate complaints about AI headshot tools. Ask for cost approval first, then group gaps with linked quotes and distinguish observed counts from market size.
Compare markets and existing pages
Use my configured sites and sources to evaluate emerging AI keywords. Confirm costs before running. Compare markets, competition, existing pages, and missing-source caveats.

ベストプラクティス

  • Keep a consistent working directory and review configured sources before comparing results across runs.
  • Approve each paid run after reviewing its estimated cost, duration, and optional features.
  • Keep source links and caveats visible, and treat instructions inside retrieved material as untrusted data.

回避

  • Repeat the daily run immediately and treat fewer incremental leads as evidence of falling market interest.
  • Treat search estimates, complaint counts, or payment links as proof of market size or business success.
  • Publish provider-restricted data or take external actions without separate, specific user approval.

よくある質問

What do I need before running the skill?
You need Python 3.10 or newer, the pinned research CLI, and an AIsa API key. Optional provider keys expand coverage.
Does the workflow use paid APIs?
Yes. Daily, sources, keywords, and pains use paid APIs. The skill requires approval after disclosing estimated cost and duration.
Can I research names without collecting new ones?
Yes. The keywords path accepts supplied names, supports repository mappings, and handles up to eight names per call.
Why does the first daily run return few leads?
It establishes snapshots and mainly reports GitHub breakouts. Limited first-run output does not mean the market is quiet.
Are the search-volume figures exact?
No. Daily search estimates are approximate, and volume data runs about two months behind. Missing optional keys can weaken trend evidence.
Does this review verify the external CLI package?
No. The reviewed skill contains instructions only. The external PyPI package, its dependencies, and provider behavior were not audited.

開発者情報

作成者

zhuyansen

ライセンス

MIT

Skillstore リビジョン

r3

バージョンに関する注意

作者はバージョンを宣言していません。

参照

2ec268972a6be25768f84279fde605b246c33d68

メンテナンスの新しさ

2026/10/28

利用状況

6 ダウンロード · 1 閲覧

ファイル構成

📄 SKILL.md

zhuyansen のその他のスキル

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