Token导航 LogoToken导航

Agent Skill · 研究检索

shannon-ai-pentester(香农艾彭斯特)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

shannon-ai-pentester 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 5,236。

总安装 26,928周安装 1,072GitHub Stars 39下载 8,624核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:shannon-ai-pentester(香农艾彭斯特)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/shannon-ai-pentester
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill shannon-ai-pentester
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aradotso/trending-skills --skill shannon-ai-pentester

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

安装方式:github,安装命令:npx skills add https://github.com/aradotso/trending-skills --skill shannon-ai-pentester。

来源仓库:https://github.com/aradotso/trending-skills/tree/main/skills/shannon-ai-pentester。适用宿主:Codex、Claude、Cursor、Gemini CLI。

来源仓库内路径:skills/shannon-ai-pentester

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/aradotso/trending-skills --skill shannon-ai-pe…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

shannon-ai-pentester 怎么安装?

在终端执行 npx skills add https://github.com/aradotso/trending-skills --skill shannon-ai-pentester。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

shannon-ai-pentester 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

shannon-ai-pentester 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

shannon-ai-pentester 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

shannon-ai-pentester 用的人多吗?

公开数据是累计安装 26,928 次,近一周安装 1,072 次,下载 8,624 次,GitHub 39 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

shannon-ai-pentester 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
shannon-ai-pentester

香农艾彭斯特 · 本页

26,92839接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Shannon AI Pentester

Skill by ara.so — Daily 2026 Skills collection.

Shannon is an autonomous, white-box AI pentester for web applications and APIs. It reads your source code to identify attack vectors, then executes real exploits (SQLi, XSS, SSRF, auth bypass, authorization flaws) against a live running application — only reporting vulnerabilities with a working proof-of-concept.

How It Works

  1. Reconnaissance — Nmap, Subfinder, WhatWeb, and Schemathesis scan the target
  2. Code Analysis — Shannon reads your repository to map attack surfaces
  3. Parallel Exploitation — Concurrent agents attempt live exploits across all vulnerability categories
  4. Report Generation — Only confirmed, reproducible findings with copy-paste PoCs are included

Installation & Prerequisites

  • Docker (required — Shannon runs entirely in containers)
  • An Anthropic API key, Claude Code OAuth token, AWS Bedrock credentials, or Google Vertex AI credentials
git clone https://github.com/KeygraphHQ/shannon.git
cd shannon

Quick Start

# Option A: Export credentials
export ANTHROPIC_API_KEY="sk-ant-..."
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000

# Option B: .env file
cat > .env << 'EOF'
ANTHROPIC_API_KEY=sk-ant-...
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000
EOF

# Run a pentest
./shannon start URL=https://your-app.example.com REPO=/path/to/your/repo

Shannon builds containers, starts the workflow in the background, and returns a workflow ID.

Key CLI Commands

# Start a pentest
./shannon start URL=https://target.example.com REPO=/path/to/repo

# Start with explicit workspace name (for resuming)
./shannon start URL=https://target.example.com REPO=/path/to/repo WORKSPACE=my-audit-2024

# Monitor live progress (tail logs)
./shannon logs <workflow-id>

# Check status of a running pentest
./shannon status <workflow-id>

# Resume an interrupted pentest
./shannon resume WORKSPACE=my-audit-2024

# Stop a running pentest
./shannon stop <workflow-id>

# View the final report
./shannon report <workflow-id>

Configuration

Environment Variables

# Required (choose one auth method)
ANTHROPIC_API_KEY=sk-ant-...           # Anthropic direct
CLAUDE_CODE_OAUTH_TOKEN=...            # Claude Code OAuth

# Recommended
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000   # Increase output window for large reports

# AWS Bedrock (alternative to Anthropic direct)
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
AWS_DEFAULT_REGION=us-east-1
SHANNON_AI_PROVIDER=bedrock
SHANNON_BEDROCK_MODEL=anthropic.claude-3-7-sonnet-20250219-v1:0

# Google Vertex AI (alternative to Anthropic direct)
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
SHANNON_AI_PROVIDER=vertex
SHANNON_VERTEX_PROJECT=your-gcp-project
SHANNON_VERTEX_REGION=us-east5

.env File Example

# .env (place in the shannon project root)
ANTHROPIC_API_KEY=sk-ant-...
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000

# Optional: target credentials for authenticated testing
TARGET_USERNAME=admin@example.com
TARGET_PASSWORD=supersecret
TARGET_TOTP_SECRET=BASE32TOTPSECRET   # Shannon handles 2FA automatically

Usage Examples

Basic Web App Pentest

# Point Shannon at a running local app with its source code
./shannon start \
  URL=http://localhost:3000 \
  REPO=$(pwd)/../my-express-app

Testing Against OWASP Juice Shop (Demo)

# Pull and run Juice Shop
docker run -d -p 3000:3000 bkimminich/juice-shop

# Run Shannon against it
./shannon start \
  URL=http://localhost:3000 \
  REPO=/path/to/juice-shop

Authenticated Testing with 2FA

export TARGET_USERNAME="admin@yourapp.com"
export TARGET_PASSWORD="$ADMIN_PASSWORD"
export TARGET_TOTP_SECRET="$TOTP_BASE32_SECRET"

./shannon start URL=https://staging.yourapp.com REPO=/path/to/repo

AWS Bedrock Provider

export AWS_ACCESS_KEY_ID="$AWS_ACCESS_KEY_ID"
export AWS_SECRET_ACCESS_KEY="$AWS_SECRET_ACCESS_KEY"
export AWS_DEFAULT_REGION=us-east-1
export SHANNON_AI_PROVIDER=bedrock
export SHANNON_BEDROCK_MODEL=anthropic.claude-3-7-sonnet-20250219-v1:0

./shannon start URL=https://target.example.com REPO=/path/to/repo

Google Vertex AI Provider

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
export SHANNON_AI_PROVIDER=vertex
export SHANNON_VERTEX_PROJECT=my-gcp-project
export SHANNON_VERTEX_REGION=us-east5

./shannon start URL=https://target.example.com REPO=/path/to/repo

Workspace and Resume Pattern

Workspaces allow you to pause and resume long-running pentests:

# Start with a named workspace
./shannon start \
  URL=https://target.example.com \
  REPO=/path/to/repo \
  WORKSPACE=sprint-42-audit

# Later, resume from where it stopped
./shannon resume WORKSPACE=sprint-42-audit

# Workspaces persist results so you can re-run reports
./shannon report WORKSPACE=sprint-42-audit

Output and Reports

Reports are written to the workspace directory (default: ./workspaces/<workflow-id>/):

workspaces/
└── my-audit-2024/
    ├── report.md          # Final pentest report with PoC exploits
    ├── findings.json      # Machine-readable findings
    └── logs/              # Per-agent execution logs

The report includes:

  • Vulnerability title and CVSS-style severity
  • Affected endpoint and parameter
  • Root cause with source code reference
  • Step-by-step reproduction instructions
  • Copy-paste curl/HTTP PoC

Vulnerability Coverage

Shannon currently tests for:

CategoryExamples
InjectionSQL injection, command injection, LDAP injection
XSSReflected, stored, DOM-based
SSRFInternal network access, cloud metadata endpoints
Broken AuthenticationWeak tokens, session fixation, auth bypass
Broken AuthorizationIDOR, privilege escalation, missing access controls

CI/CD Integration Pattern

# .github/workflows/pentest.yml
name: Shannon Pentest
on:
  push:
    branches: [staging]

jobs:
  pentest:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          path: app

      - name: Clone Shannon
        run: git clone https://github.com/KeygraphHQ/shannon.git

      - name: Start Application
        run: |
          cd app
          docker compose up -d
          # Wait for app to be healthy
          sleep 30

      - name: Run Shannon
        working-directory: shannon
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
          CLAUDE_CODE_MAX_OUTPUT_TOKENS: 64000
        run: |
          ./shannon start \
            URL=http://localhost:3000 \
            REPO=${{ github.workspace }}/app \
            WORKSPACE=ci-${{ github.sha }}
          # Wait for completion and get report
          ./shannon wait ci-${{ github.sha }}
          ./shannon report ci-${{ github.sha }} > pentest-report.md

      - name: Upload Report
        uses: actions/upload-artifact@v4
        with:
          name: pentest-report
          path: shannon/pentest-report.md

Troubleshooting

Docker not found or permission denied

# Ensure Docker daemon is running
docker info

# Add your user to the docker group (Linux)
sudo usermod -aG docker $USER
newgrp docker

Shannon containers fail to build

# Force a clean rebuild
docker compose -f shannon/docker-compose.yml build --no-cache

Pentest stalls / no progress

# Check live logs for the blocking agent
./shannon logs <workflow-id>

# Common causes:
# - Target app is not reachable from inside the Shannon container
# - ANTHROPIC_API_KEY is missing or rate-limited
# - CLAUDE_CODE_MAX_OUTPUT_TOKENS not set (model hits default limit)

Target app not reachable from Shannon containers

# Use host.docker.internal instead of localhost
./shannon start \
  URL=http://host.docker.internal:3000 \
  REPO=/path/to/repo

# Or put both on the same Docker network
docker network create pentest-net
docker run --network pentest-net ...   # your app
# Then set SHANNON_DOCKER_NETWORK=pentest-net in .env

Rate limit errors from Anthropic

# Use AWS Bedrock or Vertex AI to avoid shared rate limits
export SHANNON_AI_PROVIDER=bedrock
export AWS_DEFAULT_REGION=us-east-1

Resume after crash

# Always use WORKSPACE= when starting to enable resumability
./shannon start URL=... REPO=... WORKSPACE=named-session

# Resume
./shannon resume WORKSPACE=named-session

Important Disclaimers

  • Only test applications you own or have explicit written permission to test.
  • Shannon Lite is AGPL-3.0 licensed — any modifications must be open-sourced under the same license.
  • Shannon is a white-box tool: it expects access to your application's source code.
  • It is not a black-box scanner. Running it against third-party targets without authorization is illegal.

Key Links

平台分布

Codex

37.92%
按下载量换算3,270

Claude

29.72%
按下载量换算2,563

Cursor

19.98%
按下载量换算1,723

Gemini CLI

10%
按下载量换算862

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills