implementing-semgrep-for-custom-sast-rules

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

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/implementing-semgrep-for-custom-sast-rules
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summary

Write custom Semgrep SAST rules in YAML to detect application-specific vulnerabilities, enforce coding standards, and integrate into CI/CD pipelines.

skill.md
name
implementing-semgrep-for-custom-sast-rules
description
Write custom Semgrep SAST rules in YAML to detect application-specific vulnerabilities, enforce coding standards, and integrate into CI/CD pipelines.
domain
cybersecurity
subdomain
devsecops
tags
- semgrep - sast - static-analysis - custom-rules - devsecops - code-security
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- PR.PS-01 - GV.SC-07 - ID.IM-04 - PR.PS-04

Implementing Semgrep for Custom SAST Rules

Overview

Semgrep is an open-source static analysis tool that uses pattern-matching to find bugs, enforce code standards, and detect security vulnerabilities. Custom rules are written in YAML using Semgrep's pattern syntax, making it accessible without requiring compiler knowledge. It supports 30+ languages including Python, JavaScript, Go, Java, and C.

When to Use

  • When deploying or configuring implementing semgrep for custom sast rules 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

  • Python 3.8+ or Docker
  • Semgrep CLI installed
  • Target codebase in a supported language

Installation

# Install via pip
pip install semgrep

# Install via Homebrew
brew install semgrep

# Run via Docker
docker run -v "${PWD}:/src" returntocorp/semgrep semgrep --config auto /src

# Verify
semgrep --version

Running Semgrep

# Auto-detect rules for your code
semgrep --config auto .

# Use Semgrep registry rules
semgrep --config r/python.lang.security

# Use custom rule file
semgrep --config my-rules.yaml .

# Use multiple configs
semgrep --config auto --config ./custom-rules/ .

# JSON output
semgrep --config auto --json . > results.json

# SARIF output for GitHub
semgrep --config auto --sarif . > results.sarif

# Filter by severity
semgrep --config auto --severity ERROR .

Writing Custom Rules

Basic Pattern Matching

# rules/sql-injection.yaml
rules:
  - id: sql-injection-string-format
    languages: [python]
    severity: ERROR
    message: |
      Potential SQL injection via string formatting.
      Use parameterized queries instead.
    pattern: |
      cursor.execute(f"..." % ...)
    metadata:
      cwe: ["CWE-89"]
      owasp: ["A03:2021"]
      category: security
    fix: |
      cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))

Pattern Operators

rules:
  - id: hardcoded-secret-in-code
    languages: [python, javascript, typescript]
    severity: ERROR
    message: Hardcoded secret detected in source code
    patterns:
      - pattern-either:
          - pattern: $VAR = "..."
          - pattern: $VAR = '...'
      - metavariable-regex:
          metavariable: $VAR
          regex: (?i)(password|secret|api_key|token|aws_secret)
      - pattern-not: $VAR = ""
      - pattern-not: $VAR = "changeme"
      - pattern-not: $VAR = "PLACEHOLDER"
    metadata:
      cwe: ["CWE-798"]
      category: security

Taint Analysis

rules:
  - id: xss-taint-tracking
    languages: [python]
    severity: ERROR
    message: User input flows to HTML response without sanitization
    mode: taint
    pattern-sources:
      - pattern: request.args.get(...)
      - pattern: request.form.get(...)
      - pattern: request.form[...]
    pattern-sinks:
      - pattern: return render_template_string(...)
      - pattern: Markup(...)
    pattern-sanitizers:
      - pattern: bleach.clean(...)
      - pattern: escape(...)
    metadata:
      cwe: ["CWE-79"]
      owasp: ["A03:2021"]

Multiple Language Rule

rules:
  - id: insecure-random
    languages: [python, javascript, go, java]
    severity: WARNING
    message: |
      Using insecure random number generator. Use cryptographically
      secure alternatives for security-sensitive operations.
    pattern-either:
      # Python
      - pattern: random.random()
      - pattern: random.randint(...)
      # JavaScript
      - pattern: Math.random()
      # Go
      - pattern: math/rand.Intn(...)
      # Java
      - pattern: new java.util.Random()
    metadata:
      cwe: ["CWE-330"]

Enforce Coding Standards

rules:
  - id: require-error-handling
    languages: [go]
    severity: WARNING
    message: Error return value not checked
    pattern: |
      $VAR, _ := $FUNC(...)
    fix: |
      $VAR, err := $FUNC(...)
      if err != nil {
        return fmt.Errorf("$FUNC failed: %w", err)
      }

  - id: no-console-log-in-production
    languages: [javascript, typescript]
    severity: WARNING
    message: Remove console.log before merging to production
    pattern: console.log(...)
    paths:
      exclude:
        - "tests/*"
        - "*.test.*"

JWT Security Rules

rules:
  - id: jwt-none-algorithm
    languages: [python]
    severity: ERROR
    message: JWT decoded without algorithm verification - allows token forgery
    patterns:
      - pattern: jwt.decode($TOKEN, ..., algorithms=["none"], ...)
    metadata:
      cwe: ["CWE-347"]

  - id: jwt-no-verification
    languages: [python]
    severity: ERROR
    message: JWT decoded with verification disabled
    patterns:
      - pattern: jwt.decode($TOKEN, ..., options={"verify_signature": False}, ...)
    metadata:
      cwe: ["CWE-345"]

Rule Testing

# rules/test-sql-injection.yaml
rules:
  - id: sql-injection-format-string
    languages: [python]
    severity: ERROR
    message: SQL injection via format string
    pattern: |
      cursor.execute(f"...{$VAR}...")

# Test annotation in test file:
# test-sql-injection.py
def bad_query(user_id):
    # ruleid: sql-injection-format-string
    cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")

def good_query(user_id):
    # ok: sql-injection-format-string
    cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
# Run rule tests
semgrep --test rules/

# Test specific rule
semgrep --config rules/sql-injection.yaml --test

CI/CD Integration

GitHub Actions

name: Semgrep SAST
on: [pull_request]

jobs:
  semgrep:
    runs-on: ubuntu-latest
    container:
      image: returntocorp/semgrep
    steps:
      - uses: actions/checkout@v4

      - name: Run Semgrep
        run: |
          semgrep --config auto \
            --config ./custom-rules/ \
            --sarif --output results.sarif \
            --severity ERROR \
            .

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

GitLab CI

semgrep:
  stage: test
  image: returntocorp/semgrep
  script:
    - semgrep --config auto --config ./custom-rules/ --json --output semgrep.json .
  artifacts:
    reports:
      sast: semgrep.json

Configuration File

# .semgrep.yaml
rules:
  - id: my-org-rules
    # ... rules here

# .semgrepignore
tests/
node_modules/
vendor/
*.min.js

Best Practices

  1. Start with auto config then add custom rules for org-specific patterns
  2. Test rules with # ruleid: and # ok: annotations
  3. Use taint mode for data flow vulnerabilities (XSS, SQLi, SSRF)
  4. Include metadata (CWE, OWASP) for vulnerability classification
  5. Provide fix suggestions with the fix key where possible
  6. Exclude test files to reduce false positives
  7. Version control rules in a shared repository
  8. Run in CI as a blocking check for ERROR severity findings
how to use implementing-semgrep-for-custom-sast-rules

How to use implementing-semgrep-for-custom-sast-rules 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 implementing-semgrep-for-custom-sast-rules
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/implementing-semgrep-for-custom-sast-rules

The skills CLI fetches implementing-semgrep-for-custom-sast-rules 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/implementing-semgrep-for-custom-sast-rules

Reload or restart Cursor to activate implementing-semgrep-for-custom-sast-rules. Access the skill through slash commands (e.g., /implementing-semgrep-for-custom-sast-rules) 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

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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

4.734 reviews
  • Soo Khanna· Dec 24, 2024

    implementing-semgrep-for-custom-sast-rules fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Pratham Ware· Dec 20, 2024

    Useful defaults in implementing-semgrep-for-custom-sast-rules — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aanya Patel· Dec 20, 2024

    I recommend implementing-semgrep-for-custom-sast-rules for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Anika Rahman· Nov 15, 2024

    implementing-semgrep-for-custom-sast-rules is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakshi Patil· Nov 11, 2024

    implementing-semgrep-for-custom-sast-rules has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Lucas Agarwal· Nov 11, 2024

    Solid pick for teams standardizing on skills: implementing-semgrep-for-custom-sast-rules is focused, and the summary matches what you get after install.

  • Hiroshi Garcia· Oct 6, 2024

    Keeps context tight: implementing-semgrep-for-custom-sast-rules is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Oct 2, 2024

    Solid pick for teams standardizing on skills: implementing-semgrep-for-custom-sast-rules is focused, and the summary matches what you get after install.

  • Ama Li· Oct 2, 2024

    implementing-semgrep-for-custom-sast-rules has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aarav Chen· Sep 25, 2024

    I recommend implementing-semgrep-for-custom-sast-rules for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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