Source profileQuality 91/100

adriannoes/awesome-agentic-ai/cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-github-advanced-security-for-code-scanning/SKILL.md

implementing-github-advanced-security-for-code-scanning

Configure GitHub Advanced Security with CodeQL to perform automated static analysis and vulnerability detection across repositories at enterprise scale.

Source repository stars
52
Declared platforms
0
Static risk flags
0
Last source update
2026-08-22
Source checked
2026-08-28

Decision brief

What it does: where it fits

Configure GitHub Advanced Security with CodeQL to perform automated static analysis and vulnerability detection across repositories at enterprise scale.

Best for

  • When deploying or configuring implementing github advanced security for code scanning capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain

Not for

  • Compiled language build failures --- CodeQL requires successful compilation for C/C++, Java, C, Go, and Swift; ensure build dependencies are available in the Actions runner
  • Ignoring scheduled scans --- Push/PR scanning misses vulnerabilities in dependencies; weekly scheduled scans catch newly disclosed CVEs in existing code

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/adriannoes/awesome-agentic-ai --skill "cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-github-advanced-security-for-code-scanning"
Safe inspection promptEditorial

Inspect the Agent Skill "implementing-github-advanced-security-for-code-scanning" from https://github.com/adriannoes/awesome-agentic-ai/blob/7f71af8164e8f5a775253417aa405b5d9d063faf/cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-github-advanced-security-for-code-scanning/SKILL.md at commit 7f71af8164e8f5a775253417aa405b5d9d063faf. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Default Setup vs. Advanced Setup

    Default Setup enables code scanning with a single click from the repository's Code Security settings. GitHub automatically determines the languages present, selects appropriate query suites, and configures scanning triggers. This approach requires no workflow file and is ideal f…

    Default Setup enables code scanning with a single click from the repository's Code Security settings. GitHub automatically determines the languages present, selects appropriate query suites, and configures scanning trig…Advanced Setup generates a .github/workflows/codeql.yml workflow file that can be customized. Teams control scheduling, language matrices, build commands for compiled languages, additional query packs, and integration w…
  2. 02

    Workflow

    1. Navigate to Organization Settings Code security and analysis 2. Enable GitHub Advanced Security for all repositories or selected repositories 3. Confirm license seat allocation (GHAS is billed per active committer)

    Navigate to Organization Settings Code security and analysisEnable GitHub Advanced Security for all repositories or selected repositoriesConfirm license seat allocation (GHAS is billed per active committer)
  3. 03

    Step 1 --- Enable GHAS on the Organization

    1. Navigate to Organization Settings Code security and analysis 2. Enable GitHub Advanced Security for all repositories or selected repositories 3. Confirm license seat allocation (GHAS is billed per active committer)

    Navigate to Organization Settings Code security and analysisEnable GitHub Advanced Security for all repositories or selected repositoriesConfirm license seat allocation (GHAS is billed per active committer)
  4. 04

    Step 2 --- Configure Default Setup for Quick Wins

    1. Go to Repository Settings Code security Code scanning 2. Click "Set up" in the CodeQL analysis row and select "Default" 3. Review the auto-detected languages and query suite (default or extended) 4. Click "Enable CodeQL" to activate scanning on push and pull request events

    Go to Repository Settings Code security Code scanningClick "Set up" in the CodeQL analysis row and select "Default"Review the auto-detected languages and query suite (default or extended)
  5. 05

    Step 3 --- Advanced Setup with Custom Workflow

    Create .github/workflows/codeql-analysis.yml:

    Create .github/workflows/codeql-analysis.yml:

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score91/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars52SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
adriannoes/awesome-agentic-ai
Skill path
cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-github-advanced-security-for-code-scanning/SKILL.md
Commit
7f71af8164e8f5a775253417aa405b5d9d063faf
License
MIT
Collected
2026-08-28
Default branch
main
View the original SKILL.md

Implementing GitHub Advanced Security for Code Scanning

Overview

GitHub Advanced Security (GHAS) integrates CodeQL-powered static application security testing directly into the GitHub development workflow. CodeQL treats code as data, enabling semantic analysis that identifies security vulnerabilities such as SQL injection, cross-site scripting, buffer overflows, and authentication flaws with significantly fewer false positives than traditional pattern-matching scanners. GHAS encompasses code scanning, secret scanning, dependency review, and Dependabot alerts to provide a comprehensive security posture for repositories.

When to Use

  • When deploying or configuring implementing github advanced security for code scanning capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain
  • When conducting security assessments that require this implementation

Prerequisites

  • GitHub Enterprise Cloud or GitHub Enterprise Server 3.0+ with GHAS license
  • Repository admin or organization owner permissions
  • Familiarity with GitHub Actions workflow syntax (YAML)
  • Supported languages: C/C++, C#, Go, Java/Kotlin, JavaScript/TypeScript, Python, Ruby, Swift

Core Concepts

CodeQL Analysis Engine

CodeQL compiles source code into a queryable database, then executes security-focused queries against that database. The query suites ship with hundreds of checks mapped to CWE identifiers and cover OWASP Top 10, SANS Top 25, and language-specific vulnerability patterns. Custom queries can be authored using the CodeQL query language (QL) to detect organization-specific anti-patterns.

Default Setup vs. Advanced Setup

Default Setup enables code scanning with a single click from the repository's Code Security settings. GitHub automatically determines the languages present, selects appropriate query suites, and configures scanning triggers. This approach requires no workflow file and is ideal for rapid onboarding.

Advanced Setup generates a .github/workflows/codeql.yml workflow file that can be customized. Teams control scheduling, language matrices, build commands for compiled languages, additional query packs, and integration with third-party SARIF producers. Advanced setup is required when custom build steps, monorepo configurations, or private query packs are needed.

Organization-Wide Rollout

For enterprises managing hundreds of repositories, GHAS supports configuring code scanning at scale using the organization-level security overview. Administrators can enable default setup across all eligible repositories, define custom security configurations, and monitor adoption through the security coverage dashboard.

Workflow

Step 1 --- Enable GHAS on the Organization

  1. Navigate to Organization Settings > Code security and analysis
  2. Enable GitHub Advanced Security for all repositories or selected repositories
  3. Confirm license seat allocation (GHAS is billed per active committer)

Step 2 --- Configure Default Setup for Quick Wins

  1. Go to Repository Settings > Code security > Code scanning
  2. Click "Set up" in the CodeQL analysis row and select "Default"
  3. Review the auto-detected languages and query suite (default or extended)
  4. Click "Enable CodeQL" to activate scanning on push and pull request events

Step 3 --- Advanced Setup with Custom Workflow

Create .github/workflows/codeql-analysis.yml:

name: "CodeQL Analysis"

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]
  schedule:
    - cron: '30 2 * * 1'  # Weekly Monday 2:30 AM UTC

jobs:
  analyze:
    name: Analyze (${{ matrix.language }})
    runs-on: ubuntu-latest
    permissions:
      security-events: write
      contents: read
      actions: read

    strategy:
      fail-fast: false
      matrix:
        language: ['javascript-typescript', 'python', 'java-kotlin']

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Initialize CodeQL
        uses: github/codeql-action/init@v3
        with:
          languages: ${{ matrix.language }}
          queries: +security-extended,security-and-quality
          # For compiled languages, add build commands below

      - name: Autobuild
        uses: github/codeql-action/autobuild@v3

      - name: Perform CodeQL Analysis
        uses: github/codeql-action/analyze@v3
        with:
          category: "/language:${{ matrix.language }}"

Step 4 --- Custom Query Packs

Install organization-specific query packs by referencing them in the workflow:

- name: Initialize CodeQL
  uses: github/codeql-action/init@v3
  with:
    languages: java-kotlin
    packs: |
      my-org/[email protected]
      codeql/java-queries:cwe/cwe-089

Step 5 --- Configure Branch Protection Rules

  1. Navigate to Repository Settings > Branches > Branch protection rules
  2. Enable "Require status checks to pass" and add the CodeQL analysis check
  3. Enable "Require code scanning results" and set severity thresholds (e.g., block on High/Critical)

Step 6 --- Secret Scanning and Push Protection

  1. Enable secret scanning from Code security settings
  2. Activate push protection to block commits containing detected secrets
  3. Configure custom patterns for organization-specific secrets (API keys, internal tokens)

Step 7 --- Dependency Review and Dependabot

  1. Enable Dependabot alerts and security updates
  2. Configure .github/dependabot.yml for automated dependency version updates
  3. Enable dependency review enforcement on pull requests to block PRs that introduce known vulnerable dependencies

Query Suite Reference

SuiteDescriptionUse Case
defaultHigh-confidence security queriesProduction scanning with minimal false positives
security-extendedBroader security queries including lower-severity findingsComprehensive security coverage
security-and-qualitySecurity plus code quality queriesTeams wanting both security and maintainability checks
Custom packsOrganization-authored queriesDetecting internal anti-patterns and compliance violations

Integration with Security Workflows

SARIF Upload from Third-Party Tools

GHAS accepts SARIF (Static Analysis Results Interchange Format) uploads from external tools:

- name: Upload SARIF
  uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results.sarif
    category: "semgrep"

Security Overview Dashboard

The organization-level security overview provides:

  • Risk view showing repositories with open alerts by severity
  • Coverage view showing GHAS feature enablement across repositories
  • Alert trends over time for tracking remediation progress
  • Filter by team, language, and alert type for targeted review

Monitoring and Metrics

  • Track mean time to remediate (MTTR) for code scanning alerts
  • Monitor false positive rates and tune query configurations accordingly
  • Review alert dismissal reasons to identify areas for developer training
  • Use the API (/repos/{owner}/{repo}/code-scanning/alerts) for custom reporting dashboards

Common Pitfalls

  1. Compiled language build failures --- CodeQL requires successful compilation for C/C++, Java, C#, Go, and Swift; ensure build dependencies are available in the Actions runner
  2. Ignoring scheduled scans --- Push/PR scanning misses vulnerabilities in dependencies; weekly scheduled scans catch newly disclosed CVEs in existing code
  3. Over-alerting with security-and-quality --- Start with default suite and expand gradually to avoid developer alert fatigue
  4. Missing GHAS license seats --- Only active committers to GHAS-enabled repositories consume license seats; plan capacity accordingly

References

Frequently asked questions

What to verify before installation and use

What does the implementing-github-advanced-security-for-code-scanning source document cover?

Configure GitHub Advanced Security with CodeQL to perform automated static analysis and vulnerability detection across repositories at enterprise scale.

How do I install implementing-github-advanced-security-for-code-scanning?

The source record exposes this install command: npx skills add https://github.com/adriannoes/awesome-agentic-ai --skill "cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-github-advanced-security-for-code-scanning". Inspect the command and pinned source before running it.

Alternatives

Compare before choosing

Computed 10025,136

alirezarezvani/claude-skills

app-store-optimization

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklist

Computed 9967

brucesongs/kali-claw

insecure-design

Insecure Design (OWASP A06:2025) focuses on security flaws in system architecture and design phases, rather than code implementation-level bugs.

Computed 9916

NintendaDev/unikit-ai

unikit-docs

Generate and maintain the project's TECHNICAL documentation from its codebase — scans the project structure, tech stack, and module boundaries, then writes a lean README landing page plus detailed topic pages (architecture, modules, setup, build, APIs), only the docs that are relevant. Use whenever the user wants to create, update, or validate documentation of the CODE or the project itself, e.g. "generate documentation", "create docs", "write the README", "update the project docs", "document th

Computed 9836,049

K-Dense-AI/scientific-agent-skills

dask

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.