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secret-scanner秘密扫描仪

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:secret-scanner(秘密扫描仪)
来源仓库:https://github.com/1mangesh1/dev-skills-collection
仓库路径:skills/secret-scanner
安装命令:
npx skills add https://github.com/1mangesh1/dev-skills-collection --skill secret-scanner
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/1mangesh1/dev-skills-collection --skill secret-scanner

简介

可扫描代码仓库、配置文件及Git提交历史中的潜在泄露风险,覆盖AWS、GCP、GitHub等常见平台。

  • 核心能力包括基于熵值的字符串分析、200+正则模式匹配以及多格式文件支持。
  • 建议结合CI/CD流程自动化执行,并配合人工复核以减少误报干扰。
  • 安装需通过npx命令添加指定GitHub仓库路径, 运行前应确认最小权限原则并避免在生产环境直接扫描敏感数据。

SKILL.md

Secret Scanner

A comprehensive secret detection skill for AI agents. Detects API keys, tokens, passwords, private keys, and credentials across 50+ services. Features entropy-based detection, git history scanning, and CI/CD integration.

Capabilities

  1. Secret Detection - Find hardcoded secrets using 200+ regex patterns
  2. Entropy Analysis - Detect high-entropy strings that may be secrets
  3. Provider Coverage - AWS, GCP, Azure, GitHub, Stripe, and 50+ more
  4. Git History Scan - Scan entire commit history for leaked secrets
  5. File Type Support - Code, configs, env files, JSON, YAML, and more
  6. Risk Scoring - Score findings by severity and exposure context
  7. False Positive Filtering - Smart exclusions for test data and examples
  8. Remediation Guidance - Step-by-step secret rotation instructions
  9. CI/CD Integration - Pre-commit hooks and GitHub Actions
  10. Allowlist Support - Configure known-safe patterns to skip

Usage

/secret-scanner [command] [path] [options]

Commands

  • scan <path> - Scan files or directories for secrets
  • scan-git <path> - Scan git history for leaked secrets
  • audit <path> - Full security audit with report generation
  • verify <secret> - Check if a specific string matches secret patterns
  • providers - List all supported secret providers
  • report - Generate report from existing findings

Options

  • --format <type> - Output format: json, markdown, sarif (default: markdown)
  • --output <file> - Write results to file
  • --severity <level> - Minimum severity: low, medium, high, critical
  • --include <patterns> - File patterns to include
  • --exclude <patterns> - File patterns to exclude
  • --entropy <threshold> - Entropy threshold (default: 4.5)
  • --no-entropy - Disable entropy-based detection
  • --allowlist <file> - Path to allowlist configuration
  • --git-depth <n> - Number of commits to scan (default: all)

Workflow

When invoked, follow this workflow:

Step 1: Determine Scan Scope

Ask the user to specify:

  • Target path (file, directory, or repository)
  • Scan type (current files, git history, or both)
  • Whether to include entropy-based detection

Step 2: File Discovery

Use Glob to find relevant files:

# Source code
**/*.{py,js,ts,tsx,jsx,java,go,rb,php,cs,swift,kt,rs,c,cpp,h}

# Configuration
**/*.{env,json,yaml,yml,xml,toml,ini,conf,cfg,properties}

# Infrastructure
**/*.{tf,tfvars,hcl,dockerfile,docker-compose*}

# Shell scripts
**/*.{sh,bash,zsh,ps1,bat,cmd}

# Certificates and keys
**/*.{pem,key,p12,pfx,jks,keystore}

Step 3: Pattern Matching

Apply detection patterns from references/secret-patterns.md:

Critical Severity

# AWS Access Keys
AKIA[0-9A-Z]{16}

# AWS Secret Keys
(?i)aws.{0,20}['"][0-9a-zA-Z/+]{40}['"]

# GitHub Tokens
gh[pousr]_[A-Za-z0-9_]{36,255}

# Private Keys
-----BEGIN (RSA |EC |DSA |OPENSSH )?PRIVATE KEY-----

High Severity

# Generic API Keys
(?i)(api[_-]?key|apikey)['"]?\s*[:=]\s*['"][a-zA-Z0-9_\-]{20,}['"]

# Generic Tokens
(?i)(token|bearer|auth)['"]?\s*[:=]\s*['"][a-zA-Z0-9_\-\.]{20,}['"]

# Passwords
(?i)(password|passwd|pwd)['"]?\s*[:=]\s*['"][^'"]{8,}['"]

Step 4: Entropy Analysis

For strings not matching known patterns, calculate Shannon entropy:

def calculate_entropy(string):
    """Calculate Shannon entropy of a string."""
    from collections import Counter
    import math

    if not string:
        return 0

    counts = Counter(string)
    length = len(string)

    entropy = -sum(
        (count / length) * math.log2(count / length)
        for count in counts.values()
    )

    return entropy

# Flag strings with entropy > 4.5 and length >= 20

Step 5: Context Analysis

For each potential secret:

  1. Check surrounding context (variable names, comments)
  2. Verify it's not in a test/example file
  3. Check against allowlist
  4. Determine exposure context (public repo,.env, etc.)

Step 6: Calculate Risk Score

Apply formula from references/risk-scoring.md:

Risk = (Sensitivity × 0.40) + (Exposure × 0.30) +
       (Verifiability × 0.15) + (Scope × 0.15)

Step 7: Generate Output

Format findings following examples/sample-finding.json:

{
  "id": "S-20260204-0001",
  "file": "config/settings.py",
  "line": 42,
  "secret_type": "aws_access_key",
  "provider": "AWS",
  "value_preview": "AKIA...XXXX",
  "confidence": 0.98,
  "risk_score": 95,
  "severity": "critical",
  "context": "AWS_ACCESS_KEY = 'AKIA[REDACTED]'",
  "remediation": [...],
  "verified": false
}

Supported Providers

Cloud Providers

ProviderSecret TypesPattern Count
AWSAccess Keys, Secret Keys, Session Tokens8
GCPAPI Keys, Service Account Keys, OAuth6
AzureStorage Keys, Connection Strings, SAS Tokens7
DigitalOceanAPI Tokens, Spaces Keys3
HerokuAPI Keys, OAuth Tokens2
Alibaba CloudAccess Keys, Secret Keys3

Code Platforms

ProviderSecret TypesPattern Count
GitHubPersonal Access Tokens, App Tokens, OAuth5
GitLabPersonal Tokens, Pipeline Tokens, Runner Tokens4
BitbucketApp Passwords, OAuth, Repository Tokens3
npmAuth Tokens, Publish Tokens2
PyPIAPI Tokens1

Payment Services

ProviderSecret TypesPattern Count
StripeSecret Keys, Publishable Keys, Restricted Keys4
PayPalClient Secrets, Access Tokens2
SquareAccess Tokens, Application IDs2
BraintreeAccess Tokens, Merchant IDs2

Communication Services

ProviderSecret TypesPattern Count
TwilioAccount SID, Auth Token, API Key4
SendGridAPI Keys2
MailchimpAPI Keys1
SlackBot Tokens, User Tokens, Webhooks4
DiscordBot Tokens, Webhooks2

Database Services

ProviderSecret TypesPattern Count
MongoDBConnection Strings2
PostgreSQLConnection Strings2
MySQLConnection Strings2
RedisConnection Strings, Auth Tokens2

Other Services

ProviderSecret TypesPattern Count
OpenAIAPI Keys2
AnthropicAPI Keys1
FirebaseAPI Keys, Admin SDK Keys3
CloudflareAPI Keys, API Tokens2
DatadogAPI Keys, App Keys2
New RelicLicense Keys, API Keys2
Auth0Client Secrets, API Tokens2
OktaAPI Tokens1
JWTTokens (signature analysis)1

Full pattern list: references/secret-patterns.md

Git History Scanning

Scan Modes

  1. Full History - Scan all commits
  2. Depth Limited - Scan last N commits
  3. Branch Specific - Scan specific branch
  4. Diff Mode - Only scan changed lines

Usage

# Scan entire history
/secret-scanner scan-git ./repo

# Scan last 100 commits
/secret-scanner scan-git ./repo --git-depth 100

# Scan specific branch
/secret-scanner scan-git ./repo --branch feature/auth

Git-Specific Findings

{
  "commit": "abc123",
  "author": "developer@example.com",
  "date": "2026-01-15T10:30:00Z",
  "message": "Add API configuration",
  "file": "config.py",
  "secret_type": "stripe_secret_key",
  "still_present": false,
  "removed_in": "def456"
}

Entropy-Based Detection

How It Works

  1. Extract string literals and values from files
  2. Calculate Shannon entropy for each string
  3. Flag high-entropy strings (> 4.5) that are:

- At least 20 characters long - Contain mixed character classes - In security-sensitive contexts

Entropy Thresholds

ThresholdDetection LevelFalse Positive Rate
3.5AggressiveHigh
4.0ModerateMedium
4.5Balanced (default)Low
5.0ConservativeVery Low

Context Boosting

Entropy findings are boosted if found in:

  • Variable names containing: key, secret, token, password, auth
  • Files: .env, secrets.*, credentials.*
  • Config sections: [credentials], [auth]

False Positive Handling

Built-in Exclusions

  1. Test Files - *_test.*, *_spec.*, test_*.*, __tests__/*
  2. Example Files - example.*, sample.*, demo.*
  3. Documentation - *.md, *.rst, docs/*
  4. Mock Data - Files containing "mock", "fake", "dummy"
  5. Known Safe Patterns:

- AKIAIOSFODNN7EXAMPLE (AWS example key) - sk_test_* (Stripe test keys) - pk_test_* (Stripe test publishable keys) - xoxb-PLACEHOLDER-EXAMPLE-TOKEN (Slack example)

Allowlist Configuration

Create .secret-scanner-allowlist.yaml:

# Allowlist configuration
patterns:
  # Regex patterns to ignore
  - "EXAMPLE_[A-Z_]+"
  - "test_api_key_\\d+"

paths:
  # Files/directories to skip
  - "test/"
  - "fixtures/"
  - "*.example"

hashes:
  # SHA256 hashes of known false positives
  - "abc123..."

comments:
  # Inline comments that suppress warnings
  - "# secret-scanner:ignore"
  - "// nosecret"

Risk Scoring

Severity Levels

ScoreSeverityResponseExamples
90-100CriticalImmediateAWS keys, private keys, prod DB passwords
70-89HighWithin 4 hoursAPI keys, OAuth tokens, service accounts
50-69MediumWithin 24 hoursTest API keys, internal tokens
25-49LowWithin 1 weekEntropy matches, partial credentials
0-24InfoReviewPossible false positives

Factor Weights

  • Sensitivity (40%): Type of secret and potential damage
  • Exposure (30%): Where the secret was found
  • Verifiability (15%): Can the secret be validated as real
  • Scope (15%): Blast radius if exploited

Full methodology: references/risk-scoring.md

Remediation Workflow

Step 1: Immediate Actions

  1. Revoke the secret - Invalidate immediately in provider console
  2. Rotate credentials - Generate new secret
  3. Update applications - Deploy new credentials
  4. Audit access logs - Check for unauthorized usage

Step 2: Clean Up

  1. Remove from code - Delete the hardcoded secret
  2. Clean git history - Use BFG or git filter-branch
  3. Force push - Update all branches
  4. Invalidate caches - Clear CI/CD caches

Step 3: Prevention

  1. Add to.gitignore - Prevent future commits
  2. Install pre-commit hook - Block commits with secrets
  3. Use secrets manager - AWS Secrets Manager, HashiCorp Vault
  4. Environment variables - Store secrets in environment

Provider-specific instructions: references/remediation.md

CI/CD Integration

Pre-Commit Hook

# Install the pre-commit hook
cp scripts/pre-commit-hook.sh .git/hooks/pre-commit
chmod +x .git/hooks/pre-commit

Pre-commit Framework

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: secret-scanner
        name: Secret Scanner
        entry: python scripts/detect-secrets.py
        language: python
        types: [file]
        pass_filenames: true

GitHub Actions

# .github/workflows/secret-scan.yml
name: Secret Scan
on: [push, pull_request]
jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0  # Full history for git scanning
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - name: Run Secret Scan
        run: |
          python scripts/detect-secrets.py . --format sarif --output results.sarif
      - name: Upload SARIF
        uses: github/codeql-action/upload-sarif@v3
        with:
          sarif_file: results.sarif

Environment Variables

# Configure behavior
export SECRET_SCANNER_SEVERITY=high      # Minimum severity to report
export SECRET_SCANNER_ENTROPY=4.5        # Entropy threshold
export SECRET_SCANNER_BLOCK=true         # Block on findings
export SECRET_SCANNER_ALLOWLIST=.secret-scanner-allowlist.yaml

Output Formats

JSON (findings.json)

Structured array with all findings and metadata.

Markdown (report.md)

Human-readable report with:

  • Executive summary
  • Findings by severity
  • Provider breakdown
  • Remediation checklist

SARIF

Static Analysis Results Interchange Format for GitHub Security tab integration.

Security Guardrails

  1. Never output full secrets - Show only prefix/suffix with masking
  2. Secure temporary files - Use scratchpad, clean up after
  3. No secret logging - Redact from all log output
  4. Verification is optional - Don't auto-verify against live APIs
  5. Respect allowlists - Honor configured exclusions

References

  • references/secret-patterns.md - All detection patterns
  • references/provider-patterns.md - Provider-specific patterns
  • references/entropy-detection.md - Entropy analysis methodology
  • references/risk-scoring.md - Risk scoring methodology
  • references/remediation.md - Secret rotation guides
  • references/allowlist-config.md - Allowlist configuration

Examples

  • examples/sample-finding.json - Example finding output
  • examples/sample-report.md - Example audit report
  • examples/allowlist.yaml - Example allowlist configuration

Scripts

  • scripts/detect-secrets.py - Main secret detection script
  • scripts/scan-git-history.py - Git history scanner
  • scripts/entropy-analyzer.py - Entropy-based detection
  • scripts/generate-report.py - Report generation
  • scripts/pre-commit-hook.sh - Git pre-commit hook

适合场景

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展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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