Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: exploiting-vulnerabilities

英語版ディレクトリ

Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more

36K
Stars
86/100
信頼
カテゴリ: devops監査

Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.

29K
Stars
87/100
信頼
カテゴリ: security監査

Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks.

14K
Stars
88/100
信頼
カテゴリ: agent-skills監査

Prevent cloud misconfigurations and find vulnerabilities during build-time in infrastructure as code, container images and open source packages with Checkov by Bridgecrew.

8.8K
Stars
85/100
信頼
カテゴリ: development監査

A PHP static analysis tool for finding errors and security vulnerabilities in PHP applications

5.9K
Stars
86/100
信頼
カテゴリ: development監査

Runs Trivy as GitHub action to scan your Docker container image for vulnerabilities

1.4K
Stars
78/100
信頼
カテゴリ: github-automation監査

Horusec is an open source tool that improves identification of vulnerabilities in your project with just one command.

1.3K
Stars
77/100
信頼
カテゴリ: development監査

Appshark is a static taint analysis platform to scan vulnerabilities in an Android app.

1.7K
Stars
83/100
信頼
カテゴリ: development監査

OpenSCA is an open source software supply chain security solution that supports the detection of open source dependencies, vulnerabilities and license compliance with a widely noticed accuracy by the community.

1.1K
Stars
77/100
信頼
カテゴリ: development監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査

Find and fix vulnerabilities by chatting with AI

589
Stars
67/100
信頼
カテゴリ: agent-frameworks監査
Pyt71

A Static Analysis Tool for Detecting Security Vulnerabilities in Python Web Applications

2.2K
Stars
71/100
信頼
カテゴリ: development監査