scanning-container-images-with-grype

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/scanning-container-images-with-grype
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summary

Scan container images for known vulnerabilities using Anchore Grype with SBOM-based matching and configurable severity thresholds.

skill.md
name
scanning-container-images-with-grype
description
Scan container images for known vulnerabilities using Anchore Grype with SBOM-based matching and configurable severity thresholds.
domain
cybersecurity
subdomain
container-security
tags
- grype - vulnerability-scanning - container-security - sbom - anchore - supply-chain
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- PR.PS-01 - PR.IR-01 - ID.AM-08 - DE.CM-01

Scanning Container Images with Grype

Overview

Grype is an open-source vulnerability scanner from Anchore that inspects container images, filesystems, and SBOMs for known CVEs. It leverages Syft-generated SBOMs to match packages against multiple vulnerability databases including NVD, GitHub Advisories, and OS-specific feeds.

When to Use

  • When conducting security assessments that involve scanning container images with grype
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Docker or Podman installed
  • Grype CLI installed (curl -sSfL https://raw.githubusercontent.com/anchore/grype/main/install.sh | sh -s -- -b /usr/local/bin)
  • Syft CLI (optional, for SBOM generation)
  • Network access to pull vulnerability databases

Core Commands

Install Grype

# Install via script
curl -sSfL https://raw.githubusercontent.com/anchore/grype/main/install.sh | sh -s -- -b /usr/local/bin

# Verify installation
grype version

# Install via Homebrew (macOS/Linux)
brew install grype

Scan Container Images

# Scan a Docker Hub image
grype nginx:latest

# Scan from Docker daemon
grype docker:myapp:1.0

# Scan a local archive
grype docker-archive:image.tar

# Scan an OCI directory
grype oci-dir:path/to/oci/

# Scan a Singularity image
grype sif:image.sif

# Scan a local directory / filesystem
grype dir:/path/to/project

Output Formats

# Default table output
grype alpine:3.18

# JSON output for pipeline processing
grype alpine:3.18 -o json > results.json

# CycloneDX SBOM output
grype alpine:3.18 -o cyclonedx

# SARIF output for GitHub Security tab
grype alpine:3.18 -o sarif > grype.sarif

# Template-based custom output
grype alpine:3.18 -o template -t /path/to/template.tmpl

Filtering and Thresholds

# Fail if vulnerabilities meet or exceed a severity
grype nginx:latest --fail-on critical

# Show only fixed vulnerabilities
grype nginx:latest --only-fixed

# Show only non-fixed vulnerabilities
grype nginx:latest --only-notfixed

# Filter by severity
grype nginx:latest --only-fixed -o json | jq '[.matches[] | select(.vulnerability.severity == "High")]'

# Explain a specific CVE
grype nginx:latest --explain --id CVE-2024-1234

Working with SBOMs

# Generate SBOM with Syft then scan
syft nginx:latest -o spdx-json > nginx-sbom.json
grype sbom:nginx-sbom.json

# Scan CycloneDX SBOM
grype sbom:bom.json

Configuration File (.grype.yaml)

# .grype.yaml
check-for-app-update: false
fail-on-severity: "high"
output: "json"
scope: "squashed"  # or "all-layers"
quiet: false

ignore:
  - vulnerability: CVE-2023-12345
    reason: "False positive - not exploitable in our context"
  - vulnerability: CVE-2023-67890
    fix-state: unknown

db:
  auto-update: true
  cache-dir: "/tmp/grype-db"
  max-allowed-built-age: 120h  # 5 days

match:
  java:
    using-cpes: true
  python:
    using-cpes: true
  javascript:
    using-cpes: false

CI/CD Integration

# GitHub Actions
- name: Scan image with Grype
  uses: anchore/scan-action@v4
  with:
    image: "myregistry/myapp:${{ github.sha }}"
    fail-build: true
    severity-cutoff: high
    output-format: sarif
  id: scan

- name: Upload SARIF
  uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: ${{ steps.scan.outputs.sarif }}
# GitLab CI
container_scan:
  stage: test
  image: anchore/grype:latest
  script:
    - grype ${CI_REGISTRY_IMAGE}:${CI_COMMIT_SHA} --fail-on high -o json > grype-report.json
  artifacts:
    reports:
      container_scanning: grype-report.json

Database Management

# Check database status
grype db status

# Manually update vulnerability database
grype db update

# Delete cached database
grype db delete

# List supported database providers
grype db list

Key Vulnerability Sources

SourceCoverage
NVDCVEs across all ecosystems
GitHub AdvisoriesOpen source package vulnerabilities
Alpine SecDBAlpine Linux packages
Amazon Linux ALASAmazon Linux AMI
Debian Security TrackerDebian packages
Red Hat OVALRHEL, CentOS
Ubuntu SecurityUbuntu packages
Wolfi SecDBWolfi/Chainguard images

Best Practices

  1. Pin image tags - Always scan specific digests, not latest
  2. Fail on severity - Set --fail-on high or critical in CI gates
  3. Use SBOMs - Generate SBOMs with Syft for reproducible scanning
  4. Suppress false positives - Use .grype.yaml ignore rules with documented reasons
  5. Scan all layers - Use --scope all-layers to catch vulnerabilities in intermediate layers
  6. Automate database updates - Keep the vulnerability database current in CI runners
  7. Compare scans - Track vulnerability count over time for regression detection
how to use scanning-container-images-with-grype

How to use scanning-container-images-with-grype on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add scanning-container-images-with-grype
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/scanning-container-images-with-grype

The skills CLI fetches scanning-container-images-with-grype from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/scanning-container-images-with-grype

Reload or restart Cursor to activate scanning-container-images-with-grype. Access the skill through slash commands (e.g., /scanning-container-images-with-grype) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.452 reviews
  • Ava Robinson· Dec 28, 2024

    scanning-container-images-with-grype has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Camila Farah· Dec 20, 2024

    scanning-container-images-with-grype fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Zaid Flores· Dec 16, 2024

    Registry listing for scanning-container-images-with-grype matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Charlotte Yang· Dec 12, 2024

    Keeps context tight: scanning-container-images-with-grype is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Zhang· Nov 27, 2024

    scanning-container-images-with-grype fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 23, 2024

    We added scanning-container-images-with-grype from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Carlos Mehta· Nov 19, 2024

    Keeps context tight: scanning-container-images-with-grype is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Benjamin Gonzalez· Nov 7, 2024

    Useful defaults in scanning-container-images-with-grype — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kiara Diallo· Nov 3, 2024

    scanning-container-images-with-grype has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Benjamin Khan· Oct 26, 2024

    I recommend scanning-container-images-with-grype for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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