スキル rsi-skill
📦

rsi-skill

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

Evaluate and Improve Prompts With Test Cases

Prompt changes can look useful without improving real results. This skill scores examples, proposes revisions, and retains candidates that improve training scores without lowering validation scores.

対応: Claude Codex Code(CC)
Maintain this Skill? Claim it 📊 72 十分

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 "rsi-skill" from https://skillstore.io/skills/lambdasec-rsi-skill.md and its manifest at https://skillstore.io/api/skills/lambdasec-rsi-skill/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

「rsi-skill」を使用しています。 Summarize the documented GSM8K optimization example.

期待される結果:

  • The bundled example reports a baseline of 5/28 training cases and 2/12 validation cases.
  • The retained final revision reached 27/28 training cases and 11/12 validation cases.
  • Two intermediate revisions were discarded. These are documented results from one run, not guaranteed outcomes.

「rsi-skill」を使用しています。 Initialize a new sentiment classification task.

期待される結果:

  • Created a starter prompt for positive, negative, and neutral sentiment.
  • Added three labeled examples for editing.
  • This starter set is too small for validation splitting. Add representative cases before optimization.

セキュリティ監査

中リスク

Most static alerts concern harmless formatting, arithmetic examples, documentation links, or intended backend configuration. Four HTTP request findings expose credentials through unrestricted redirects; a separate model-grading weakness can undermine evaluation integrity. No evidence found of malicious instructions, embedded credentials, or intentional data theft.

6
スキャンされたファイル
520
解析済み行数
4
レビュー項目
0
誤検知を無視

確認済みのセキュリティ上の懸念 (1)

中
Untrusted content can influence model-based grading
Judge mode concatenates task input and generated answers into a grading message. Attacker-controlled content can request PASS verdicts and undermine prompt selection.
The grader receives untrusted answer text without explicit data-only instructions and accepts a PASS prefix. Exploit success depends on the selected model.
機能レビュー項目 (4)

これらは、このスキルに期待される可能性のある実際のローカル機能であるため、レビューが必要ですが、確認済みの悪意ある動作としてはカウントされません。

高
Python HTTP libraries
req = urllib.request.Request(url, headers={"Authorization": f"Bearer {key}"} if key else {})
Authorization is attached as an ordinary request header before default urllib redirect handling. Cross-origin redirects can forward this bearer credential to another host.
高
Python HTTP libraries
with urllib.request.urlopen(req, timeout=timeout) as r:
Default urlopen follows redirects without restricting the credential-bearing request to its original origin. A local redirect-handler check confirmed Authorization forwarding.
高
Python HTTP libraries
req = urllib.request.Request(be["base"] + "/chat/completions", data=body, headers=headers)
The completion request includes Authorization in ordinary headers. A redirect accepted by urllib can send that credential to a different origin.
高
Python HTTP libraries
with urllib.request.urlopen(req, timeout=300) as r:
The completion request uses default redirect handling without an origin check. Accepted redirects can carry the bearer credential to an unintended host.
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
セキュリティ評価

レポートリンクをコピー

https://skillstore.io/skills/lambdasec-rsi-skill/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/lambdasec-rsi-skill/security.svg)](https://skillstore.io/skills/lambdasec-rsi-skill?utm_source=security_passport_badge)

HTMLバッジ

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

埋め込みカード

<iframe src="https://skillstore.io/embed/skills/lambdasec-rsi-skill.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

lambdasec. (2026). rsi-skill security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lambdasec-rsi-skill/audits/1

BibTeX形式の引用

@techreport{lambdasec-lambdasec-rsi-skill-2026, author = {lambdasec}, title = {rsi-skill security audit report (audit version 1)}, institution = {Skillstore}, year = {2026}, number = {1}, url = {https://skillstore.io/skills/lambdasec-rsi-skill/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: "rsi-skill security audit report (audit version 1)" version: "unspecified" type: report authors: - name: "lambdasec" date-released: "2026-10-28" url: "https://skillstore.io/skills/lambdasec-rsi-skill/audits/1" identifiers: - type: other value: "skillstore:lambdasec-rsi-skill: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
64
アーキテクチャ
85
保守性
87
コンテンツ
65
コミュニティ
91
仕様準拠

作成できるもの

Refine classification instructions

Compare a classifier prompt against labeled examples and inspect failures before accepting a revision.

Compare reasoning prompts

Use the bundled math cases to compare prompt variants with numeric checks and a reproducible validation split.

Test agent instruction changes

Evaluate instruction text against representative cases and review the proposed changes before applying them.

これらのプロンプトを試す

Create a first evaluation
Help me evaluate this prompt: [prompt]. Initialize a task directory and help me define expected answers. Confirm the backend before sending data.
Measure a baseline
Evaluate the prompt and cases in [directory] using [approved endpoint and model]. Report the baseline score, failed cases, and scoring methods without revising anything.
Run controlled optimization
Optimize [directory] for up to five rounds using [approved endpoint and model]. Keep the default validation split and copy rejection. Report scores and proposed changes.
Validate an instruction revision
Optimize [directory] using [evaluation model] and [proposal model] on [approved endpoint]. Reevaluate the best prompt on [independent test directory]. Review failures before requesting deployment approval.

ベストプラクティス

  • Confirm the resolved endpoint and model before inference. Obtain approval for private remote data and avoid untrusted redirects.
  • Use representative cases and deterministic checks where possible. Reserve a separate final test set beyond the reused validation split.
  • Review failures and prompt changes, repeat evaluation, and obtain approval before replacing production instructions.

回避

  • Sending confidential prompts or cases to an unapproved remote endpoint.
  • Treating validation gains from a small, repeatedly reused split as proof of general improvement.
  • Using model-judge verdicts as the only acceptance gate for adversarial or security-sensitive tasks.

よくある質問

Does this skill train model weights?
No. It revises instruction text and measures the resulting answers against test cases.
Which model services can it use?
It supports OpenAI-compatible chat endpoints, including OptiQ Cloud, local OptiQ servers, and explicitly configured services. API features vary by backend.
Can I keep evaluation data local?
Yes, with an approved local endpoint. Select it explicitly and verify backend resolution because cloud configuration takes priority over automatic local discovery.
How many cases should I provide?
The instructions recommend 10 to 40 representative cases. Fewer than six cases disable validation splitting.
How does it select a revision?
Training scores must improve, and validation scores must not decrease when a validation set exists. Complete copied test inputs are rejected by default.
Are improvements guaranteed and automatically deployed?
No. Results depend on the model, checks, and examples. The instructions require user approval before applying the saved best prompt.

開発者情報

作成者

lambdasec

ライセンス

Apache-2.0

Skillstore リビジョン

r1

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

87c9669baf85385959f9b0a344dc9dfc8d978f9c

メンテナンスの新しさ

2026/10/28

利用状況

0 ダウンロード · 0 閲覧

ファイル構成

lambdasec のその他のスキル

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testing-patterns

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コードベース全体で信頼性の高いテストを計画する

作成者 0xDarkMatter

このスキルは、テスト範囲を混在させずに、チームがテスト戦略、モック、データフィクスチャ、カバレッジゲート、CIパターンを選択するのに役立ちます。Claude、Codex、Claude Codeに、ユニット、統合、エンドツーエンド、TDD、パイプラインテストのための構造化されたガイダンスを提供します。

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